upload

LICENSE

@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2021 ZJUNLP
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

QA/__init__.py

@@ -1,6 +0,0 @@
-
-
-from transformers import BartForConditionalGeneration, T5ForConditionalGeneration, GPT2LMHeadModel
-
-from .model import *
-

QA/fbqa/relphormer-filtered/dev-stat.json

@@ -1,6 +0,0 @@
-{
-	"#examples": 3994,
-	"#kept_examples": 3994,
-	"#mappable_examples": 743,
-	"#multiple_answer_examples": 2
-}

QA/fbqa/relphormer-filtered/test-stat.json

@@ -1,6 +0,0 @@
-{
-	"#examples": 3996,
-	"#kept_examples": 3996,
-	"#mappable_examples": 755,
-	"#multiple_answer_examples": 0
-}

QA/fbqa/relphormer-filtered/train-stat.json

@@ -1,6 +0,0 @@
-{
-	"#examples": 20358,
-	"#kept_examples": 20358,
-	"#mappable_examples": 3713,
-	"#multiple_answer_examples": 4
-}

QA/fbqa/relphormer/dev-stat.json

@@ -1,6 +0,0 @@
-{
-	"#examples": 3994,
-	"#kept_examples": 3994,
-	"#mappable_examples": 743,
-	"#multiple_answer_examples": 2
-}

QA/fbqa/relphormer/test-stat.json

@@ -1,6 +0,0 @@
-{
-	"#examples": 3996,
-	"#kept_examples": 3996,
-	"#mappable_examples": 755,
-	"#multiple_answer_examples": 0
-}

QA/fbqa/relphormer/train-stat.json

@@ -1,6 +0,0 @@
-{
-	"#examples": 20358,
-	"#kept_examples": 20358,
-	"#mappable_examples": 3713,
-	"#multiple_answer_examples": 4
-}

QA/hitter-bert.py

@@ -1,403 +0,0 @@
-import json
-import math
-import argparse
-from pathlib import Path
-
-from transformers import BertTokenizer, BertForMaskedLM, AdamW, get_linear_schedule_with_warmup, AutoConfig
-
-import torch
-from torch import device, nn
-from torch.utils.data import DataLoader, Dataset
-
-import pytorch_lightning as pl
-from pytorch_lightning.loggers import TensorBoardLogger
-from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
-from pytorch_lightning.utilities.seed import seed_everything
-from transformers.tokenization_bert import BertTokenizerFast
-
-from kge.model import KgeModel
-from kge.util.io import load_checkpoint
-from kge.util import sc
-# from relphormer.lit_models import TransformerLitModel
-from relphormer.models import BertKGC
-# from relphormer.data import KGC
-import os
-
-os.environ['CUDA_VISIBLE_DEVICES']='4'
-
-MODEL = 'bert-base-uncased'
-tokenizer = BertTokenizer.from_pretrained(MODEL)
-
-
-class FBQADataset(Dataset):
-
-    def __init__(self, file_dir):
-        self.examples = json.load(Path(file_dir).open("rb"))
-
-    def __len__(self):
-        return len(self.examples)
-
-    def __getitem__(self, idx):
-        if torch.is_tensor(idx):
-            idx = idx.tolist()
-        return self.examples[idx]
-
-
-def fbqa_collate(samples):
-    questions = []
-    answers = []
-    answer_ids = []
-    entities = []
-    entity_names = []
-    relations = []
-    for item in samples:
-        q = item["RawQuestion"] + "[MASK]" * len(item["AnswerEntity"]) + "."
-        questions.append(q)
-        answers.append(item["AnswerEntity"])
-        answer_ids.append(item["AnswerEntityID"])
-        entities.append(item["TopicEntityID"])
-        entity_names.append(item["TopicEntityName"])
-        relations.append(item["RelationID"])
-        
-    questions = tokenizer(questions, return_tensors='pt', padding=True)
-    entity_names = tokenizer(entity_names, add_special_tokens=False)
-    answers, answers_lengths = sc.pad_seq_of_seq(answers)
-    answers = torch.LongTensor(answers)
-    answers_lengths = torch.LongTensor(answers_lengths)
-    answer_ids = torch.LongTensor(answer_ids)
-
-    input_ids = questions['input_ids']
-    masked_labels = torch.ones_like(input_ids) * -100
-    masked_labels[input_ids == tokenizer.mask_token_id] = answers[answers != 0]
-    entity_mask = torch.zeros_like(input_ids).bool()
-    entity_span_index = input_ids.new_zeros((len(input_ids), 2))
-    for i, e_tokens in enumerate(entity_names['input_ids']):
-        q_tokens = input_ids[i].tolist()
-        for s_index in range(len(q_tokens) - len(e_tokens)):
-            if all([e_token == q_tokens[s_index + j] for j, e_token in enumerate(e_tokens)]):
-                entity_mask[i][s_index:s_index + len(e_tokens)] = True
-                entity_span_index[i][0] = s_index
-                entity_span_index[i][1] = s_index + len(e_tokens) - 1
-                break
-
-    entities = torch.LongTensor(entities)
-    relations = torch.LongTensor(relations)
-
-    return questions.data, masked_labels, answers, answers_lengths, answer_ids, entities, relations, entity_mask, entity_span_index
-
-
-class SelfOutput(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
-        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
-        self.dropout = nn.Dropout(config.hidden_dropout_prob)
-
-    def forward(self, hidden_states, input_tensor):
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        hidden_states = self.LayerNorm(hidden_states + input_tensor)
-        return hidden_states
-
-
-class CrossAttention(nn.Module):
-    def __init__(self, config, ctx_hidden_size):
-        super().__init__()
-        self.self = CrossAttentionInternal(config, ctx_hidden_size)
-        self.output = SelfOutput(config)
-        self.config = config
-        self.apply(self._init_weights)
-
-    def _init_weights(self, module):
-        """ Initialize the weights """
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            # Slightly different from the TF version which uses truncated_normal for initialization
-            # cf https://github.com/pytorch/pytorch/pull/5617
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-        if isinstance(module, nn.Linear) and module.bias is not None:
-            module.bias.data.zero_()
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        output_attentions=False,
-    ):
-        self_outputs = self.self(
-            hidden_states,
-            attention_mask,
-            head_mask,
-            encoder_hidden_states,
-            encoder_attention_mask,
-            output_attentions,
-        )
-        attention_output = self.output(self_outputs[0], hidden_states)
-        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
-        return outputs
-
-
-class CrossAttentionInternal(nn.Module):
-    def __init__(self, config, ctx_hidden_size):
-        super().__init__()
-        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
-            raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
-            )
-
-        self.num_attention_heads = config.num_attention_heads
-        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
-        self.all_head_size = self.num_attention_heads * self.attention_head_size
-
-        self.query = nn.Linear(config.hidden_size, self.all_head_size)
-        self.key = nn.Linear(ctx_hidden_size, self.all_head_size)
-        self.value = nn.Linear(ctx_hidden_size, self.all_head_size)
-
-        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-
-    def transpose_for_scores(self, x):
-        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
-        x = x.view(*new_x_shape)
-        return x.permute(0, 2, 1, 3)
-
-    def forward(
-        self,
-        hidden_states,
-        attention_mask=None,
-        head_mask=None,
-        encoder_hidden_states=None,
-        encoder_attention_mask=None,
-        output_attentions=False,
-    ):
-        mixed_query_layer = self.query(hidden_states)
-
-        # If this is instantiated as a cross-attention module, the keys
-        # and values come from an encoder; the attention mask needs to be
-        # such that the encoder's padding tokens are not attended to.
-        mixed_key_layer = self.key(encoder_hidden_states)
-        mixed_value_layer = self.value(encoder_hidden_states)
-        attention_mask = encoder_attention_mask
-
-        query_layer = self.transpose_for_scores(mixed_query_layer)
-        key_layer = self.transpose_for_scores(mixed_key_layer)
-        value_layer = self.transpose_for_scores(mixed_value_layer)
-
-        # Take the dot product between "query" and "key" to get the raw attention scores.
-        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-        if attention_mask is not None:
-            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
-            attention_scores = attention_scores + attention_mask
-
-        # Normalize the attention scores to probabilities.
-        attention_probs = nn.Softmax(dim=-1)(attention_scores)
-
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        attention_probs = self.dropout(attention_probs)
-
-        # Mask heads if we want to
-        if head_mask is not None:
-            attention_probs = attention_probs * head_mask
-
-        context_layer = torch.matmul(attention_probs, value_layer)
-
-        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
-        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
-        context_layer = context_layer.view(*new_context_layer_shape)
-
-        outputs = (context_layer, nn.Softmax(dim=-1)(attention_scores)) if output_attentions else (context_layer,)
-        return outputs
-
-
-class CrossTrmFinetuner(pl.LightningModule):
-    def __init__(self, hparams, bertmodel):
-        super().__init__()
-        self._hparams = hparams
-
-        self.lr = hparams['lr']
-        self.weight_decay = hparams['weight_decay']
-
-        self.kg_dim = 320
-        # self.bert = BertForMaskedLM.from_pretrained(MODEL)
-        self.bert = bertmodel
-
-        if self._hparams['use_hitter']:
-            self.kg_layer_num = 10
-            self.cross_attentions = nn.ModuleList([CrossAttention(self.bert.config, self.kg_dim)
-                                                   for _ in range(self.kg_layer_num)])
-            checkpoint = load_checkpoint('local/best/20200812-174221-trmeh-fb15k237-best/checkpoint_best.pt')
-            self.hitter = KgeModel.create_from(checkpoint)
-
-    def forward(self, batch):
-        sent_input, masked_labels, batch_labels, label_lens, answer_ids, s, p, entity_mask, entity_span_index = batch
-
-        if self._hparams['use_hitter']:
-            # kg_masks: [bs, 1, 1, length]
-            # kg_embeds: nlayer*[bs, length, dim]
-            kg_embeds, kg_masks = self.hitter('get_hitter_repr', s, p)
-            kg_attentions = [None] * 2 + [(self.cross_attentions[i], kg_embeds[(i + 2) // 2], kg_masks)
-                                          for i in range(self.kg_layer_num)]
-        else:
-            kg_attentions = []
-
-        out = self.bert(kg_attentions=kg_attentions,
-                        output_attentions=True,
-                        output_hidden_states=True,
-                        return_dict=True,
-                        labels=masked_labels,
-                        **sent_input,
-                        )
-
-        return out
-
-    def training_step(self, batch, batch_idx):
-        output = self(batch)
-        loss = output.loss
-        self.log('train_loss', loss, on_epoch=True, prog_bar=True)
-        return {'loss': loss}
-
-    def validation_step(self, batch, batch_idx):
-        batch_inputs, masked_labels, batch_labels, label_lens, answer_ids, s, p, entity_mask, _ = batch
-        output = self(batch)
-        input_tokens = batch_inputs["input_ids"].clone()
-
-        logits = output.logits[masked_labels != -100]
-        probs = logits.softmax(dim=-1)
-        values, predictions = probs.topk(1)
-        hits = []
-        now_pos = 0
-        for sample_i, label_length in enumerate(label_lens.tolist()):
-            failed = False
-            for i in range(label_length):
-                if (predictions[now_pos + i] == batch_labels[sample_i][i]).sum() != 1:
-                    failed = True
-                    break
-            hits += [1] if not failed else [0]
-            now_pos += label_length
-        hits = torch.tensor(hits)
-        input_tokens[input_tokens == tokenizer.mask_token_id] = predictions.flatten()
-        pred_strings = [str(hits[i].item()) + ' ' + tokenizer.decode(input_tokens[i], skip_special_tokens=True)
-                        for i in range(input_tokens.size(0))]
-
-        return {'val_loss': output.loss,
-                'val_acc': hits.float(),
-                'pred_strings': pred_strings}
-
-    def validation_epoch_end(self, outputs):
-        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
-        avg_val_acc = torch.cat([x['val_acc'] for x in outputs]).mean().to(avg_loss.device)
-
-        if self.global_rank == 0:
-            tensorboard = self.logger.experiment
-            tensorboard.add_text('pred', '\n\n'.join(sum([x['pred_strings'] for x in outputs], [])), self.global_step)
-
-        self.log('avg_loss', avg_loss, on_epoch=True, prog_bar=True, sync_dist=True)
-        self.log('avg_val_acc', avg_val_acc, on_epoch=True, prog_bar=True, sync_dist=True)
-        return {'val_loss': avg_loss}
-
-    def train_dataloader(self):
-        return DataLoader(FBQADataset(self._hparams['train_dataset']),
-                          self._hparams['batch_size'],
-                          shuffle=True,
-                          collate_fn=fbqa_collate,
-                          num_workers=0)
-
-    def val_dataloader(self):
-        return DataLoader(FBQADataset(self._hparams['val_dataset']),
-                          1,
-                          shuffle=False,
-                          collate_fn=fbqa_collate,
-                          num_workers=0)
-
-    def test_dataloader(self):
-        return DataLoader(FBQADataset(self._hparams['test_dataset']),
-                          1,
-                          shuffle=False,
-                          collate_fn=fbqa_collate,
-                          num_workers=0)
-
-    def configure_optimizers(self):
-        no_decay = ['bias', 'LayerNorm.weight']
-        no_fine_tune = ['cross_attentions']
-        pgs = [{'params': [p for n, p in self.named_parameters() if not any(nd in n for nd in no_decay) and not any([i in n for i in no_fine_tune])],
-                'weight_decay': 0.01},
-               {'params': [p for n, p in self.named_parameters() if any(nd in n for nd in no_decay) and not any([i in n for i in no_fine_tune])],
-                'weight_decay': 0.0}]
-        if self._hparams['use_hitter']:
-            pgs.append({'params': self.cross_attentions.parameters(), 'lr': 5e-5, 'weight_decay': 0.01})
-        # bert_optimizer = AdamW(pgs, lr=3e-5, weight_decay=1e-2)
-        bert_optimizer = AdamW(pgs, lr=self.lr, weight_decay=self.weight_decay)
-        bert_scheduler = {
-            'scheduler': get_linear_schedule_with_warmup(bert_optimizer, self._hparams['max_steps'] // 10, self._hparams['max_steps']),
-            'interval': 'step',
-            'monitor': None
-        }
-        return [bert_optimizer], [bert_scheduler]
-    
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--exp_name", default='default', nargs='?', help="Name of the experiment")
-    parser.add_argument('--dataset', choices=['fbqa', 'webqsp'], default='fbqa', help="fbqa or webqsp")
-    parser.add_argument('--filtered', default=False, action='store_true', help="Filtered or not")
-    parser.add_argument('--hitter', default=False, action='store_true', help="Use pretrained HittER or not")
-    parser.add_argument('--relphormer', default=False, action='store_true', help="Use pretrained relphormer or not")
-    parser.add_argument('--seed', default=333, type=int, help='Seed number')
-    parser.add_argument('--lr', default=3e-5, type=float, help='learning rate')
-    parser.add_argument('--weight_decay', default=1e-2, type=float, help='weight decay')
-    args = parser.parse_args()
-    seed_everything(args.seed)
-
-    QA_DATASET = args.dataset
-    if args.filtered and args.relphormer:
-        SUBSET = 'relphormer-filtered'
-    elif not args.filtered and args.relphormer:
-        SUBSET = 'relphormer'
-    elif args.filtered and not args.relphormer:
-        SUBSET = 'fb15k237-filtered'
-    else:
-        SUBSET = 'fb15k237'
-
-    hparams = {
-        'use_hitter': args.hitter,
-        'relphormer': args.relphormer,
-        'lr': args.lr,
-        'weight_decay': args.weight_decay,
-        'batch_size': 16,
-        'max_epochs': 20,
-        'train_dataset': f'data/{QA_DATASET}/{SUBSET}/train.json',
-        'val_dataset': f'data/{QA_DATASET}/{SUBSET}/test.json',
-        'test_dataset': f'data/{QA_DATASET}/{SUBSET}/test.json',
-    }
-
-    if hparams['relphormer']:
-        MODEL = "./local/relphormer/"
-        config = AutoConfig.from_pretrained(MODEL)
-        bertmodel = BertForMaskedLM.from_pretrained(MODEL, config=config)
-        model = CrossTrmFinetuner(hparams, bertmodel=bertmodel)
-    else:
-        bertmodel = BertForMaskedLM.from_pretrained(MODEL)
-        model = CrossTrmFinetuner(hparams, bertmodel=bertmodel)
-    model.hparams['max_steps'] = (len(model.train_dataloader().dataset) // hparams['batch_size'] + 1) * hparams['max_epochs']
-    base_path = '/tmp/hitbert-paper'
-    logger = TensorBoardLogger(base_path, args.exp_name)
-    checkpoint_callback = ModelCheckpoint(
-            monitor='avg_val_acc',
-            dirpath=base_path + '/' + args.exp_name,
-            filename='{epoch:02d}-{avg_val_acc:.3f}',
-            save_top_k=1,
-            mode='max')
-    trainer = pl.Trainer(gpus=1, accelerator="ddp",
-                         max_epochs=hparams['max_epochs'], max_steps=model.hparams['max_steps'],
-                         checkpoint_callback=True,
-                         gradient_clip_val=1.0, logger=logger,
-                         callbacks=[LearningRateMonitor(), checkpoint_callback])
-    trainer.fit(model)
-    print("QA Task End!")

QA/model.py

@@ -1,8 +0,0 @@
-# from transformers.models.bert.modeling_bert import BertForMaskedLM
-from models.huggingface_relformer import BertForMaskedLM
-class BertKGC(BertForMaskedLM):
-
-    @staticmethod
-    def add_to_argparse(parser):
-        parser.add_argument("--pretrain", type=int, default=0, help="")
-        return parser

QA/scripts/relphormer_fbqa.sh

@@ -1,10 +0,0 @@
-for SEED in  111 222 333 444 555 666 777 888 999
-do
-# echo ${LR} ${WD}
-python hitter-bert.py --dataset fbqa \
-        --relphormer \
-        --seed ${SEED} \
-        --exp_name relphormer-fbqa \
-        --lr 3e-5 \
-        --weight_decay 1e-2
-done

QA/scripts/relphormer_fbqa_filtered.sh

@@ -1,13 +0,0 @@
-
-for SEED in  111 222 333 444 555 666 777 888 999
-do
-
-# echo ${LR} ${WD}
-python hitter-bert.py --dataset fbqa \
-        --relphormer \
-        --filtered \
-        --seed ${SEED} \
-        --exp_name relphormer-filtered-fbqa \
-        --lr 3e-5 \
-        --weight_decay 1e-2
-done

QA/scripts/relphormer_webqsp.sh

@@ -1,10 +0,0 @@
-
-for SEED in  222 333 444 555 666 777 888 999
-do
-python hitter-bert.py --dataset webqsp \
-        --relphormer \
-        --seed ${SEED} \
-        --exp_name relphormer-webqsp \
-        --lr 3e-5 \
-        --weight_decay 1e-2
-done

QA/scripts/relphormer_webqsp_filtered.sh

@@ -1,12 +0,0 @@
-
-for SEED in  111 222 333 444 555 666 777 888 999
-do
-# echo ${LR} ${WD}
-python hitter-bert.py --dataset webqsp \
-        --relphormer \
-        --filtered \
-        --seed ${SEED} \
-        --exp_name relphormer-filtered-webqsp \
-        --lr 3e-5 \
-        --weight_decay 1e-2
-done

QA/webqsp/relphormer-filtered/test-stat.json

@@ -1,6 +0,0 @@
-{
-	"#examples": 1639,
-	"#kept_examples": 484,
-	"#mappable_examples": 484,
-	"#multiple_answer_examples": 800
-}

QA/webqsp/relphormer-filtered/train-stat.json

@@ -1,6 +0,0 @@
-{
-	"#examples": 3098,
-	"#kept_examples": 850,
-	"#mappable_examples": 850,
-	"#multiple_answer_examples": 1437
-}

QA/webqsp/relphormer/test-stat.json

@@ -1,6 +0,0 @@
-{
-	"#examples": 1639,
-	"#kept_examples": 1582,
-	"#mappable_examples": 484,
-	"#multiple_answer_examples": 800
-}

QA/webqsp/relphormer/train-stat.json

@@ -1,6 +0,0 @@
-{
-	"#examples": 3098,
-	"#kept_examples": 2997,
-	"#mappable_examples": 850,
-	"#multiple_answer_examples": 1437
-}

README.md

@@ -1,115 +0,0 @@
-# Relphormer
-
-Code for the paper: "Relphormer: Relational Graph Transformer for Knowledge Graph Representations".
-
-> Transformers have achieved remarkable performance in widespread fields, including natural language processing, computer vision and graph mining. However, vanilla Transformer architectures have not yielded promising improvements in the Knowledge Graph (KG) representations, where the translational distance paradigm dominates this area. Note that vanilla Transformer architectures struggle to capture the intrinsically heterogeneous semantic and structural information of knowledge graphs. To this end, we propose a new variant of Transformer for knowledge graph representations dubbed Relphormer. Specifically, we introduce Triple2Seq which can dynamically sample contextualized sub-graph sequences as the input to alleviate the heterogeneity issue. We propose a novel structure-enhanced self-attention mechanism to encode the relational information and keep the globally semantic information among sub-graphs. Moreover, we propose masked knowledge modeling as a new paradigm for knowledge graph representation learning. We apply Relphormer to three tasks, namely, knowledge graph completion, KG-based question answering and KG-based recommendation for evaluation. Experimental results show that Relphormer can obtain better performance on benchmark datasets compared with baselines.
-
-
-# Model Architecture
-
-<div align=center>
-<img src="./resource/model.png" width="85%" height="75%" />
-</div>
- 
-
-The model architecture of Relphormer. 
-The contextualized sub-graph is sampled with Triple2Seq, and then it will be converted into sequences while maintaining its sub-graph structure.
-Next, we conduct masked knowledge modeling, which randomly masks the nodes in the center triple in the contextualized sub-graph sequences.
-For the transformer architecture, we design a novel structure-enhanced mechanism to preserve the structure feature.
-Finally, we utilize our pre-trained KG transformer for KG-based downstream tasks. 
-
-# Environments
-
-- python (3.8.13)
-- cuda(11.2)
-- Ubuntu-18.04.6 (4.15.0-156-generic)
-
-# Requirements
-
-To run the codes, you need to install the requirements:
-```
-pip install -r requirements.txt
-```
-
-The expected structure of files is:
-
-```
- ── Relphormer
-    ├── data
-    ├── dataset
-    │   ├── FB15k-237
-    │   ├── WN18RR
-    │   ├── umls
-    │   ├── create_neighbor.py
-    ├── lit_models
-    │   ├── _init_.py
-    │   ├── base.py
-    │   ├── transformer.py
-    │   └── utils.py
-    ├── models
-    │   ├── _init_.py
-    │   ├── huggingface_relformer.py
-    │   ├── model.py
-    │   └── utils.py    
-    ├── resource
-    │   └── model.png    
-    ├── scripts
-    │   ├── fb15k-237
-    │   ├── wn18rr
-    │   └── umls
-    ├── QA
-    ├── logs
-    ├── main.py
-    └── requirements.txt
-```
-
-# How to run
-
-## KGC Task
-
-### Generate Masked Neighbors
-
-- Use the command below to generate the masked neighbors.
-```shell
->> cd dataset
->> python create_neighbor.py --dataset xxx  # like python create_neighbor.py --dataset umls
-```
-
-### Entity Embedding Initialization
-
-- Then use the command below to add entities to BERT and initialize the entity embedding layer to be used in the later training. For other datasets `FB15k-237`  and `WN18RR` ,  just replace the dataset name with  `fb15k-237` and  `wn18rr` will be fine.
-
-```shell
->> cd pretrain
->> mkdir logs
->> bash scripts/pretrain_umls.sh
->> tail -f -n 2000 logs/pretrain_umls.log
-```
-
-The pretrained models are saved in the `Relphormer/pretrain/output` directory.
-
-### Entity Prediction
-
-- Next use the command below to train the model to predict the correct entity in the masked position. Same as above for other datasets.
-
-```shell
->> cd Relphormer
->> mkdir logs
->> bash scripts/umls/umls.sh
->> tail -f -n 2000 logs/train_umls.log
-```
-
-The trained models are saved in the `Relphormer/output` directory.
-
-## QA Task
-The experimental settings in QA follow the [Hitter](https://arxiv.org/pdf/2008.12813.pdf) experimental settings, and the environment installation can be done by referring to [GitHub](https://github.com/microsoft/HittER). We only modified **hitter-best.py** to fit our model.
-
-- The relphormer model used by QA can be downloaded [here](https://drive.google.com/file/d/1FK_A_kFq1ECoNm75RfkcvYv8rZiJL1Bw/view?usp=sharing).
-
-```shell
->> cd QA
->> sh scripts/relphormer_fbqa.sh
->> sh scripts/relphormer_fbqa_filtered.sh 
->> sh scripts/relphormer_webqsp.sh
->> sh scripts/relphormer_webqsp_filtered.sh
-```

config/log_config.json

@@ -0,0 +1,24 @@
+{
+    "version": 1,
+    "disable_existing_loggers": false,
+    "formatters": {
+        "simple": {
+            "format": "%(asctime)s - %(name)s - [%(levelname)s] - %(message)s"
+        }
+    },
+    "handlers": {
+        "file_handler": {
+            "class": "logging.FileHandler",
+            "level": "DEBUG",
+            "formatter": "simple",
+            "filename": "python_logging.log",
+            "encoding": "utf8"
+        }
+    },
+    "root": {
+        "level": "DEBUG",
+        "handlers": [
+            "file_handler"
+        ]
+    }
+}

data/__init__.py

@@ -1,2 +0,0 @@
-from .data_module import KGC
-from .processor import convert_examples_to_features, KGProcessor

data/algos.pyx

@@ -1,63 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-import cython
-from cython.parallel cimport prange, parallel
-cimport numpy
-import numpy
-
-def floyd_warshall(adjacency_matrix):
-
-    (nrows, ncols) = adjacency_matrix.shape
-    assert nrows == ncols
-    cdef unsigned int n = nrows
-
-    adj_mat_copy = adjacency_matrix.astype(long, order='C', casting='safe', copy=True)
-    assert adj_mat_copy.flags['C_CONTIGUOUS']
-    cdef numpy.ndarray[long, ndim=2, mode='c'] M = adj_mat_copy
-    cdef numpy.ndarray[long, ndim=2, mode='c'] path = numpy.zeros([n, n], dtype=numpy.int64)
-
-    cdef unsigned int i, j, k
-    cdef long M_ij, M_ik, cost_ikkj
-    cdef long* M_ptr = &M[0,0]
-    cdef long* M_i_ptr
-    cdef long* M_k_ptr
-
-    # set unreachable nodes distance to 510
-    for i in range(n):
-        for j in range(n):
-            if i == j:
-                M[i][j] = 0
-            elif M[i][j] == 0:
-                M[i][j] = 510
-
-    # floyed algo
-    for k in range(n):
-        M_k_ptr = M_ptr + n*k
-        for i in range(n):
-            M_i_ptr = M_ptr + n*i
-            M_ik = M_i_ptr[k]
-            for j in range(n):
-                cost_ikkj = M_ik + M_k_ptr[j]
-                M_ij = M_i_ptr[j]
-                if M_ij > cost_ikkj:
-                    M_i_ptr[j] = cost_ikkj
-                    path[i][j] = k
-
-    # set unreachable path to 510
-    for i in range(n):
-        for j in range(n):
-            if M[i][j] >= 510:
-                path[i][j] = 510
-                M[i][j] = 510
-
-    return M, path
-
-
-def get_all_edges(path, i, j):
-    cdef unsigned int k = path[i][j]
-    if k == 0:
-        return []
-    else:
-        return get_all_edges(path, i, k) + [k] + get_all_edges(path, k, j)
-

data/base_data_module.py

@@ -1,71 +0,0 @@
-"""Base DataModule class."""
-from pathlib import Path
-from typing import Dict
-import argparse
-import os
-
-import pytorch_lightning as pl
-from torch.utils.data import DataLoader
-
-
-class Config(dict):
-    def __getattr__(self, name):
-        return self.get(name)
-
-    def __setattr__(self, name, val):
-        self[name] = val
-
-
-BATCH_SIZE = 8
-NUM_WORKERS = 8
-
-
-class BaseDataModule(pl.LightningDataModule):
-    """
-    Base DataModule.
-    Learn more at https://pytorch-lightning.readthedocs.io/en/stable/datamodules.html
-    """
-
-    def __init__(self, args: argparse.Namespace = None) -> None:
-        super().__init__()
-        self.args = Config(vars(args)) if args is not None else {}
-        self.batch_size = self.args.get("batch_size", BATCH_SIZE)
-        self.num_workers = self.args.get("num_workers", NUM_WORKERS)
-
-
-    @staticmethod
-    def add_to_argparse(parser):
-        parser.add_argument(
-            "--batch_size", type=int, default=BATCH_SIZE, help="Number of examples to operate on per forward step."
-        )
-        parser.add_argument(
-            "--num_workers", type=int, default=0, help="Number of additional processes to load data."
-        )
-        parser.add_argument(
-            "--dataset", type=str, default="./dataset/NELL", help="Number of additional processes to load data."
-        )
-        return parser
-
-    def prepare_data(self):
-        """
-        Use this method to do things that might write to disk or that need to be done only from a single GPU in distributed settings (so don't set state `self.x = y`).
-        """
-        pass
-
-    def setup(self, stage=None):
-        """
-        Split into train, val, test, and set dims.
-        Should assign `torch Dataset` objects to self.data_train, self.data_val, and optionally self.data_test.
-        """
-        self.data_train = None
-        self.data_val = None
-        self.data_test = None
-
-    def train_dataloader(self):
-        return DataLoader(self.data_train, shuffle=True, batch_size=self.batch_size, num_workers=self.num_workers, pin_memory=True)
-
-    def val_dataloader(self):
-        return DataLoader(self.data_val, shuffle=False, batch_size=self.batch_size, num_workers=self.num_workers, pin_memory=True)
-
-    def test_dataloader(self):
-        return DataLoader(self.data_test, shuffle=False, batch_size=self.batch_size, num_workers=self.num_workers, pin_memory=True)

data/data_module.py

@@ -1,195 +0,0 @@
-from dataclasses import dataclass
-from typing import Any, Callable, Dict, List, NewType, Optional, Tuple, Union
-from enum import Enum
-import torch
-
-from torch.utils.data import DataLoader
-from transformers import AutoTokenizer, BertTokenizer
-# from transformers.configuration_bert import BertTokenizer, BertTokenizerFast
-from transformers.tokenization_utils_base import (BatchEncoding,
-                                                  PreTrainedTokenizerBase)
-
-from .base_data_module import BaseDataModule
-from .processor import KGProcessor, get_dataset
-import transformers
-transformers.logging.set_verbosity_error()
-
-class ExplicitEnum(Enum):
-    """
-    Enum with more explicit error message for missing values.
-    """
-
-    @classmethod
-    def _missing_(cls, value):
-        raise ValueError(
-            f"{value} is not a valid {cls.__name__}, please select one of {list(cls._value2member_map_.keys())}"
-        )
-
-class PaddingStrategy(ExplicitEnum):
-    """
-    Possible values for the ``padding`` argument in :meth:`PreTrainedTokenizerBase.__call__`. Useful for tab-completion
-    in an IDE.
-    """
-
-    LONGEST = "longest"
-    MAX_LENGTH = "max_length"
-    DO_NOT_PAD = "do_not_pad"
-
-import numpy as np
-
-@dataclass
-class DataCollatorForSeq2Seq:
-    """
-    Data collator that will dynamically pad the inputs received, as well as the labels.
-
-    Args:
-        tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`):
-            The tokenizer used for encoding the data.
-        model (:class:`~transformers.PreTrainedModel`):
-            The model that is being trained. If set and has the `prepare_decoder_input_ids_from_labels`, use it to
-            prepare the `decoder_input_ids`
-
-            This is useful when using `label_smoothing` to avoid calculating loss twice.
-        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`True`):
-            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)
-            among:
-
-            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
-              sequence is provided).
-            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
-              maximum acceptable input length for the model if that argument is not provided.
-            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
-              different lengths).
-        max_length (:obj:`int`, `optional`):
-            Maximum length of the returned list and optionally padding length (see above).
-        pad_to_multiple_of (:obj:`int`, `optional`):
-            If set will pad the sequence to a multiple of the provided value.
-
-            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=
-            7.5 (Volta).
-        label_pad_token_id (:obj:`int`, `optional`, defaults to -100):
-            The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
-    """
-
-    tokenizer: PreTrainedTokenizerBase
-    model: Optional[Any] = None
-    padding: Union[bool, str, PaddingStrategy] = True
-    max_length: Optional[int] = None
-    pad_to_multiple_of: Optional[int] = None
-    label_pad_token_id: int = -100
-    return_tensors: str = "pt"
-    num_labels: int = 0
-
-    def __call__(self, features, return_tensors=None):
-
-        if return_tensors is None:
-            return_tensors = self.return_tensors
-        labels = [feature.pop("labels") for feature in features] if "labels" in features[0].keys() else None
-        label = [feature.pop("label") for feature in features]
-        features_keys = {}
-        name_keys = list(features[0].keys())
-        for k in name_keys:
-            # ignore the padding arguments
-            if k in ["input_ids", "attention_mask", "token_type_ids"]: continue
-            try:
-                features_keys[k] = [feature.pop(k) for feature in features]
-            except KeyError:
-                continue
-
-        # We have to pad the labels before calling `tokenizer.pad` as this method won't pad them and needs them of the
-        # same length to return tensors.
-        bsz = len(labels)
-        with torch.no_grad():
-            new_labels = torch.zeros(bsz, self.num_labels)
-            for i,l in enumerate(labels):
-                if isinstance(l, int): 
-                    new_labels[i][l] = 1
-                else:
-                    for j in l:
-                        new_labels[i][j] = 1
-            labels = new_labels
-
-        features = self.tokenizer.pad(
-            features,
-            padding=self.padding,
-            max_length=self.max_length,
-            pad_to_multiple_of=self.pad_to_multiple_of,
-            return_tensors=return_tensors,
-        )
-        features['labels'] = labels
-        features['label'] = torch.tensor(label)
-        features.update(features_keys)
-
-        return features
-
-
-
-class KGC(BaseDataModule):
-    def __init__(self, args, model) -> None:
-        super().__init__(args)
-        self.tokenizer = AutoTokenizer.from_pretrained(self.args.model_name_or_path, use_fast=False)
-        self.processor = KGProcessor(self.tokenizer, args)
-        self.label_list = self.processor.get_labels(args.data_dir)
-
-        entity_list = self.processor.get_entities(args.data_dir)
-        
-        num_added_tokens = self.tokenizer.add_special_tokens({'additional_special_tokens': entity_list})
-        self.sampler = DataCollatorForSeq2Seq(self.tokenizer,
-            model=model,
-            label_pad_token_id=self.tokenizer.pad_token_id,
-            pad_to_multiple_of=8 if self.args.precision == 16 else None,
-            padding="longest",
-            max_length=self.args.max_seq_length,
-            num_labels = len(entity_list),
-        )
-        relations_tokens = self.processor.get_relations(args.data_dir)
-        self.num_relations = len(relations_tokens)
-        num_added_tokens = self.tokenizer.add_special_tokens({'additional_special_tokens': relations_tokens})
-
-        vocab = self.tokenizer.get_added_vocab()
-        self.relation_id_st = vocab[relations_tokens[0]]
-        self.relation_id_ed = vocab[relations_tokens[-1]] + 1
-        self.entity_id_st = vocab[entity_list[0]]
-        self.entity_id_ed = vocab[entity_list[-1]] + 1
-
-
-    def setup(self, stage=None):
-        self.data_train = get_dataset(self.args, self.processor, self.label_list, self.tokenizer, "train")
-        self.data_val = get_dataset(self.args, self.processor, self.label_list, self.tokenizer, "dev")
-        self.data_test = get_dataset(self.args, self.processor, self.label_list, self.tokenizer, "test")
-
-    def prepare_data(self):
-        pass
-
-    def get_config(self):
-        d = {}
-        for k, v in self.__dict__.items():
-            if "st" in k or "ed" in k:
-                d.update({k:v})
-        
-        return d
-
-
-    @staticmethod
-    def add_to_argparse(parser):
-        BaseDataModule.add_to_argparse(parser)
-        parser.add_argument("--model_name_or_path", type=str, default="roberta-base", help="the name or the path to the pretrained model")
-        parser.add_argument("--data_dir", type=str, default="roberta-base", help="the name or the path to the pretrained model")
-        parser.add_argument("--max_seq_length", type=int, default=256, help="Number of examples to operate on per forward step.")
-        parser.add_argument("--warm_up_radio", type=float, default=0.1, help="Number of examples to operate on per forward step.")
-        parser.add_argument("--eval_batch_size", type=int, default=8)
-        parser.add_argument("--overwrite_cache", action="store_true", default=False)
-        return parser
-
-    def get_tokenizer(self):
-        return self.tokenizer
-
-    def train_dataloader(self):
-        return DataLoader(self.data_train, num_workers=self.num_workers, pin_memory=True, collate_fn=self.sampler, batch_size=self.args.batch_size, shuffle=not self.args.faiss_init)
-
-    def val_dataloader(self):
-        return DataLoader(self.data_val, num_workers=self.num_workers, pin_memory=True, collate_fn=self.sampler, batch_size=self.args.eval_batch_size)
-
-    def test_dataloader(self):
-        return DataLoader(self.data_test, num_workers=self.num_workers, pin_memory=True, collate_fn=self.sampler, batch_size=self.args.eval_batch_size)
-

data/processor.py

@@ -1,954 +0,0 @@
-from hashlib import new
-from re import DEBUG
-
-import contextlib
-import sys
-
-from collections import Counter
-from multiprocessing import Pool
-from torch._C import HOIST_CONV_PACKED_PARAMS
-from torch.utils.data import Dataset, Sampler, IterableDataset
-from collections import defaultdict
-from functools import partial
-from multiprocessing import Pool
-import os
-import random
-import json
-import torch
-import copy
-import numpy as np
-import pickle
-from tqdm import tqdm
-from dataclasses import dataclass, asdict, replace
-import inspect
-
-from transformers.models.auto.tokenization_auto import AutoTokenizer
-
-from models.utils import get_entity_spans_pre_processing
-import pyximport
-
-pyximport.install(setup_args={'include_dirs': np.get_include()})
-import data.algos as algos
-
-def lmap(a, b):
-    return list(map(a,b))  # a是个函数，b是个值列表，返回函数值列表
-
-def cache_results(_cache_fp, _refresh=False, _verbose=1):
-    r"""
-    cache_results是fastNLP中用于cache数据的装饰器。通过下面的例子看一下如何使用::
-
-        import time
-        import numpy as np
-        from fastNLP import cache_results
-        
-        @cache_results('cache.pkl')
-        def process_data():
-            # 一些比较耗时的工作，比如读取数据，预处理数据等，这里用time.sleep()代替耗时
-            time.sleep(1)
-            return np.random.randint(10, size=(5,))
-        
-        start_time = time.time()
-        print("res =",process_data())
-        print(time.time() - start_time)
-        
-        start_time = time.time()
-        print("res =",process_data())
-        print(time.time() - start_time)
-        
-        # 输出内容如下，可以看到两次结果相同，且第二次几乎没有花费时间
-        # Save cache to cache.pkl.
-        # res = [5 4 9 1 8]
-        # 1.0042750835418701
-        # Read cache from cache.pkl.
-        # res = [5 4 9 1 8]
-        # 0.0040721893310546875
-
-    可以看到第二次运行的时候，只用了0.0001s左右，是由于第二次运行将直接从cache.pkl这个文件读取数据，而不会经过再次预处理::
-
-        # 还是以上面的例子为例，如果需要重新生成另一个cache，比如另一个数据集的内容，通过如下的方式调用即可
-        process_data(_cache_fp='cache2.pkl')  # 完全不影响之前的‘cache.pkl'
-
-    上面的_cache_fp是cache_results会识别的参数，它将从'cache2.pkl'这里缓存/读取数据，即这里的'cache2.pkl'覆盖默认的
-    'cache.pkl'。如果在你的函数前面加上了@cache_results()则你的函数会增加三个参数[_cache_fp, _refresh, _verbose]。
-    上面的例子即为使用_cache_fp的情况，这三个参数不会传入到你的函数中，当然你写的函数参数名也不可能包含这三个名称::
-
-        process_data(_cache_fp='cache2.pkl', _refresh=True)  # 这里强制重新生成一份对预处理的cache。
-        #  _verbose是用于控制输出信息的，如果为0,则不输出任何内容;如果为1,则会提醒当前步骤是读取的cache还是生成了新的cache
-
-    :param str _cache_fp: 将返回结果缓存到什么位置;或从什么位置读取缓存。如果为None，cache_results没有任何效用，除非在
-        函数调用的时候传入_cache_fp这个参数。
-    :param bool _refresh: 是否重新生成cache。
-    :param int _verbose: 是否打印cache的信息。
-    :return:
-    """
-
-    def wrapper_(func):
-        signature = inspect.signature(func)
-        for key, _ in signature.parameters.items():
-            if key in ('_cache_fp', '_refresh', '_verbose'):
-                raise RuntimeError("The function decorated by cache_results cannot have keyword `{}`.".format(key))
-
-        def wrapper(*args, **kwargs):
-            my_args = args[0]
-            mode = args[-1]
-            if '_cache_fp' in kwargs:
-                cache_filepath = kwargs.pop('_cache_fp')
-                assert isinstance(cache_filepath, str), "_cache_fp can only be str."
-            else:
-                cache_filepath = _cache_fp
-            if '_refresh' in kwargs:
-                refresh = kwargs.pop('_refresh')
-                assert isinstance(refresh, bool), "_refresh can only be bool."
-            else:
-                refresh = _refresh
-            if '_verbose' in kwargs:
-                verbose = kwargs.pop('_verbose')
-                assert isinstance(verbose, int), "_verbose can only be integer."
-            else:
-                verbose = _verbose
-            refresh_flag = True
-            
-            model_name = my_args.model_name_or_path.split("/")[-1]
-            is_pretrain = my_args.pretrain
-            cache_filepath = os.path.join(my_args.data_dir, f"cached_{mode}_features{model_name}_pretrain{is_pretrain}_faiss{my_args.faiss_init}_seqlength{my_args.max_seq_length}_{my_args.litmodel_class}.pkl")
-            refresh = my_args.overwrite_cache
-
-            if cache_filepath is not None and refresh is False:
-                # load data
-                if os.path.exists(cache_filepath):
-                    with open(cache_filepath, 'rb') as f:
-                        results = pickle.load(f)
-                    if verbose == 1:
-                        logger.info("Read cache from {}.".format(cache_filepath))
-                    refresh_flag = False
-
-            if refresh_flag:
-                results = func(*args, **kwargs)
-                if cache_filepath is not None:
-                    if results is None:
-                        raise RuntimeError("The return value is None. Delete the decorator.")
-                    with open(cache_filepath, 'wb') as f:
-                        pickle.dump(results, f)
-                    logger.info("Save cache to {}.".format(cache_filepath))
-
-            return results
-
-        return wrapper
-
-    return wrapper_
-
-
-import argparse
-import csv
-import logging
-import os
-import random
-import sys
-
-import numpy as np
-import torch
-from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
-                              TensorDataset)
-from torch.utils.data.distributed import DistributedSampler
-from tqdm import tqdm, trange
-
-# from torch.nn import CrossEntropyLoss, MSELoss
-# from scipy.stats import pearsonr, spearmanr
-# from sklearn.metrics import matthews_corrcoef, f1_scoreclass 
-
-
-
-logger = logging.getLogger(__name__)
-
-class InputExample(object):
-    """A single training/test example for simple sequence classification."""
-
-    def __init__(self, guid, text_a, text_b=None, text_c=None, text_d=None, label=None, real_label=None, en=None, en_id=None, rel=None, text_d_id=None, graph_inf=None):
-        """Constructs a InputExample.
-
-        Args:
-            guid: Unique id for the example.
-            text_a: string. The untokenized text of the first sequence. For single
-            sequence tasks, only this sequence must be specified.
-            text_b: (Optional) string. The untokenized text of the second sequence.
-            Only must be specified for sequence pair tasks.
-            text_c: (Optional) string. The untokenized text of the third sequence.
-            Only must be specified for sequence triple tasks.
-            label: (Optional) string. list of entities
-        """
-        self.guid = guid
-        self.text_a = text_a
-        self.text_b = text_b
-        self.text_c = text_c
-        self.text_d = text_d
-        self.label = label
-        self.real_label = real_label
-        self.en = en
-        self.rel = rel # rel id
-        self.text_d_id = text_d_id
-        self.graph_inf = graph_inf
-        self.en_id = en_id
-
-
-@dataclass
-class InputFeatures:
-    """A single set of features of data."""
-
-    input_ids: torch.Tensor
-    attention_mask: torch.Tensor
-    labels: torch.Tensor = None
-    label: torch.Tensor = None
-    en: torch.Tensor = 0
-    rel: torch.Tensor = 0
-    pos: torch.Tensor = 0
-    graph: torch.Tensor = 0
-    distance_attention: torch.Tensor = 0
-    # attention_bias: torch.Tensor = 0
-
-
-class DataProcessor(object):
-    """Base class for data converters for sequence classification data sets."""
-
-    def get_train_examples(self, data_dir):
-        """Gets a collection of `InputExample`s for the train set."""
-        raise NotImplementedError()
-
-    def get_dev_examples(self, data_dir):
-        """Gets a collection of `InputExample`s for the dev set."""
-        raise NotImplementedError()
-
-    def get_labels(self, data_dir):
-        """Gets the list of labels for this data set."""
-        raise NotImplementedError()
-
-    @classmethod
-    def _read_tsv(cls, input_file, quotechar=None):
-        """Reads a tab separated value file."""
-        with open(input_file, "r", encoding="utf-8") as f:
-            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
-            lines = []
-            for line in reader:
-                if sys.version_info[0] == 2:
-                    line = list(unicode(cell, 'utf-8') for cell in line)
-                lines.append(line)
-            return lines
-
-import copy
-
-
-def solve_get_knowledge_store(line, set_type="train", pretrain=1):
-    """
-    use the LM to get the entity embedding.
-    Transductive: triples + text description
-    Inductive: text description
-    
-    """
-    examples = []
-        
-    head_ent_text = ent2text[line[0]]
-    tail_ent_text = ent2text[line[2]]
-    relation_text = rel2text[line[1]]
-    
-    i=0
-    
-    a = tail_filter_entities["\t".join([line[0],line[1]])]
-    b = head_filter_entities["\t".join([line[2],line[1]])]
-    
-    guid = "%s-%s" % (set_type, i)
-    text_a = head_ent_text
-    text_b = relation_text
-    text_c = tail_ent_text 
-
-    # use the description of c to predict A
-    examples.append(
-        InputExample(guid=guid, text_a="[PAD]", text_b=text_b + "[PAD]", text_c = "[PAD]" + " " + text_c, label=lmap(lambda x: ent2id[x], b), real_label=ent2id[line[0]], en=[ent2id[line[0]], rel2id[line[1]], ent2id[line[2]]], rel=0)
-    )
-    examples.append(
-        InputExample(guid=guid, text_a="[PAD]", text_b=text_b + "[PAD]", text_c = "[PAD]" + " " + text_a, label=lmap(lambda x: ent2id[x], b), real_label=ent2id[line[2]], en=[ent2id[line[0]], rel2id[line[1]], ent2id[line[2]]], rel=0)
-    )
-    return examples
-
-
-def solve(line,  set_type="train", pretrain=1, max_triplet=32):
-    examples = []
-        
-    head_ent_text = ent2text[line[0]]
-    tail_ent_text = ent2text[line[2]]
-    relation_text = rel2text[line[1]]
-    
-    i=0
-    
-    a = tail_filter_entities["\t".join([line[0],line[1]])]
-    b = head_filter_entities["\t".join([line[2],line[1]])]
-    
-    guid = "%s-%s" % (set_type, i)
-    text_a = head_ent_text
-    text_b = relation_text
-    text_c = tail_ent_text
-
-    
-    if pretrain:
-        text_a_tokens = text_a.split()
-        for i in range(10):
-            st = random.randint(0, len(text_a_tokens))
-            examples.append(
-                InputExample(guid=guid, text_a="[MASK]", text_b=" ".join(text_a_tokens[st:min(st+64, len(text_a_tokens))]), text_c = "", label=ent2id[line[0]], real_label=ent2id[line[0]], en=0, rel=0)
-            )
-        examples.append(
-            InputExample(guid=guid, text_a="[MASK]", text_b=text_a, text_c = "", label=ent2id[line[0]], real_label=ent2id[line[0]], en=0, rel=0)
-        )
-        # examples.append(
-        #     InputExample(guid=guid, text_a="[MASK]", text_b=text_c, text_c = "", label=ent2id[line[2]], real_label=ent2id[line[2]], en=0, rel=0)
-        # )
-    else:
-        # 主要是对text_c进行包装，不再是原来的文本，而是对应子图的graph（变量graph_seq）。如果mask的是尾实体，那么就让text_c在后面加入graph_seq
-        # masked_head_seq = []
-        # masked_tail_seq = []
-        # masked_tail_graph_list = masked_tail_neighbor["\t".join([line[0],line[1]])]
-        # masked_head_graph_list = masked_head_neighbor["\t".join([line[2],line[1]])]
-        # for item in masked_head_graph_list:
-        #     masked_head_seq.append(ent2id[item[0]])
-        #     masked_head_seq.append(rel2id[item[1]])
-        #     masked_head_seq.append(ent2id[item[2]])
-
-        # for item in masked_tail_graph_list:
-        #     masked_tail_seq.append(ent2id[item[0]])
-        #     masked_tail_seq.append(rel2id[item[1]])
-        #     masked_tail_seq.append(ent2id[item[2]])
-
-        masked_head_seq = set()
-        masked_head_seq_id = set()
-        masked_tail_seq = set()
-        masked_tail_seq_id = set()
-
-        masked_tail_graph_list = masked_tail_neighbor["\t".join([line[0],line[1]])] if len(masked_tail_neighbor["\t".join([line[0],line[1]])]) < max_triplet else \
-            random.sample(masked_tail_neighbor["\t".join([line[0],line[1]])], max_triplet)
-        masked_head_graph_list = masked_head_neighbor["\t".join([line[2],line[1]])] if len(masked_head_neighbor["\t".join([line[2],line[1]])]) < max_triplet else \
-            random.sample(masked_head_neighbor["\t".join([line[2],line[1]])], max_triplet)
-        # masked_tail_graph_list = masked_tail_neighbor["\t".join([line[0],line[1]])][:16]
-        # masked_head_graph_list = masked_head_neighbor["\t".join([line[2],line[1]])][:16]
-        for item in masked_head_graph_list:
-            masked_head_seq.add(item[0])
-            masked_head_seq.add(item[1])
-            masked_head_seq.add(item[2])
-            masked_head_seq_id.add(ent2id[item[0]])
-            masked_head_seq_id.add(rel2id[item[1]])
-            masked_head_seq_id.add(ent2id[item[2]])
-
-        for item in masked_tail_graph_list:
-            masked_tail_seq.add(item[0])
-            masked_tail_seq.add(item[1])
-            masked_tail_seq.add(item[2])
-            masked_tail_seq_id.add(ent2id[item[0]])
-            masked_tail_seq_id.add(rel2id[item[1]])
-            masked_tail_seq_id.add(ent2id[item[2]])
-        # print(masked_tail_seq)
-        masked_head_seq = masked_head_seq.difference({line[0]})
-        masked_head_seq = masked_head_seq.difference({line[2]})
-        masked_head_seq = masked_head_seq.difference({line[1]})
-        masked_head_seq_id = masked_head_seq_id.difference({ent2id[line[0]]})
-        masked_head_seq_id = masked_head_seq_id.difference({rel2id[line[1]]})
-        masked_head_seq_id = masked_head_seq_id.difference({ent2id[line[2]]})
-
-        masked_tail_seq = masked_tail_seq.difference({line[0]})
-        masked_tail_seq = masked_tail_seq.difference({line[2]})
-        masked_tail_seq = masked_tail_seq.difference({line[1]})
-        masked_tail_seq_id = masked_tail_seq_id.difference({ent2id[line[0]]})
-        masked_tail_seq_id = masked_tail_seq_id.difference({rel2id[line[1]]})
-        masked_tail_seq_id = masked_tail_seq_id.difference({ent2id[line[2]]})
-        # examples.append(
-        #     InputExample(guid=guid, text_a="[MASK]", text_b=' '.join(text_b.split(' ')[:16]) + " [PAD]", text_c = "[PAD]" + " " + ' '.join(text_c.split(' ')[:16]), text_d = masked_head_seq, label=lmap(lambda x: ent2id[x], b), real_label=ent2id[line[0]], en=[rel2id[line[1]], ent2id[line[2]]], rel=rel2id[line[1]]))
-        # examples.append(
-        #     InputExample(guid=guid, text_a="[PAD] ", text_b=' '.join(text_b.split(' ')[:16]) + " [PAD]", text_c = "[MASK]" +" " + ' '.join(text_a.split(' ')[:16]), text_d = masked_tail_seq, label=lmap(lambda x: ent2id[x], a), real_label=ent2id[line[2]], en=[ent2id[line[0]], rel2id[line[1]]], rel=rel2id[line[1]]))
-        examples.append(
-            InputExample(guid=guid, text_a="[MASK]", text_b="[PAD]", text_c = "[PAD]", text_d = list(masked_head_seq), label=lmap(lambda x: ent2id[x], b), real_label=ent2id[line[0]], en=[line[1], line[2]], en_id = [rel2id[line[1]], ent2id[line[2]]], rel=rel2id[line[1]], text_d_id = list(masked_head_seq_id), graph_inf = masked_head_graph_list))
-        examples.append(
-            InputExample(guid=guid, text_a="[PAD]", text_b="[PAD]", text_c = "[MASK]", text_d = list(masked_tail_seq), label=lmap(lambda x: ent2id[x], a), real_label=ent2id[line[2]], en=[line[0], line[1]], en_id = [ent2id[line[0]], rel2id[line[1]]], rel=rel2id[line[1]], text_d_id = list(masked_tail_seq_id), graph_inf = masked_tail_graph_list))
-    return examples
-
-def filter_init(head, tail, t1,t2, ent2id_, ent2token_, rel2id_, masked_head_neighbor_, masked_tail_neighbor_, rel2token_):
-    global head_filter_entities
-    global tail_filter_entities
-    global ent2text
-    global rel2text
-    global ent2id
-    global ent2token
-    global rel2id
-    global masked_head_neighbor
-    global masked_tail_neighbor
-    global rel2token
-
-    head_filter_entities = head
-    tail_filter_entities = tail
-    ent2text =t1
-    rel2text =t2
-    ent2id = ent2id_
-    ent2token = ent2token_
-    rel2id = rel2id_
-    masked_head_neighbor = masked_head_neighbor_
-    masked_tail_neighbor = masked_tail_neighbor_
-    rel2token = rel2token_
-
-def delete_init(ent2text_):
-    global ent2text
-    ent2text = ent2text_
-
-
-class KGProcessor(DataProcessor):
-    """Processor for knowledge graph data set."""
-    def __init__(self, tokenizer, args):
-        self.labels = set()
-        self.tokenizer = tokenizer
-        self.args = args
-        self.entity_path = os.path.join(args.data_dir, "entity2textlong.txt") if os.path.exists(os.path.join(args.data_dir, 'entity2textlong.txt')) \
-        else os.path.join(args.data_dir, "entity2text.txt")
-    
-    def get_train_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(
-            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train", data_dir, self.args)
-
-    def get_dev_examples(self, data_dir):
-        """See base class."""
-        return self._create_examples(
-            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev", data_dir, self.args)
-
-    def get_test_examples(self, data_dir, chunk=""):
-      """See base class."""
-      return self._create_examples(
-          self._read_tsv(os.path.join(data_dir, f"test{chunk}.tsv")), "test", data_dir, self.args)
-
-    def get_relations(self, data_dir):
-        """Gets all labels (relations) in the knowledge graph."""
-        # return list(self.labels)
-        with open(os.path.join(data_dir, "relations.txt"), 'r') as f:
-            lines = f.readlines()
-            relations = []
-            for line in lines:
-                relations.append(line.strip().split('\t')[0])
-        rel2token = {ent : f"[RELATION_{i}]" for i, ent in enumerate(relations)}
-        return list(rel2token.values())
-
-    def get_labels(self, data_dir):
-        """Gets all labels (0, 1) for triples in the knowledge graph."""
-        relation = []
-        with open(os.path.join(data_dir, "relation2text.txt"), 'r') as f:
-            lines = f.readlines()
-            entities = []
-            for line in lines:
-                relation.append(line.strip().split("\t")[-1])
-        return relation
-
-    def get_entities(self, data_dir):
-        """Gets all entities in the knowledge graph."""
-        with open(self.entity_path, 'r') as f:
-            lines = f.readlines()
-            entities = []
-            for line in lines:
-                entities.append(line.strip().split("\t")[0])
-        
-        ent2token = {ent : f"[ENTITY_{i}]" for i, ent in enumerate(entities)}
-        return list(ent2token.values())
-
-    def get_train_triples(self, data_dir):
-        """Gets training triples."""
-        return self._read_tsv(os.path.join(data_dir, "train.tsv"))
-
-    def get_dev_triples(self, data_dir):
-        """Gets validation triples."""
-        return self._read_tsv(os.path.join(data_dir, "dev.tsv"))
-
-    def get_test_triples(self, data_dir, chunk=""):
-        """Gets test triples."""
-        return self._read_tsv(os.path.join(data_dir, f"test{chunk}.tsv"))
-
-    def _create_examples(self, lines, set_type, data_dir, args):
-        """Creates examples for the training and dev sets."""
-        # entity to text
-        ent2text = {}
-        ent2text_with_type = {}
-        with open(self.entity_path, 'r') as f:
-            ent_lines = f.readlines()
-            for line in ent_lines:
-                temp = line.strip().split('\t')
-                try:
-                    end = temp[1]#.find(',')
-                    if "wiki" in data_dir:
-                        assert "Q" in temp[0]
-                    ent2text[temp[0]] = temp[1].replace("\\n", " ").replace("\\", "") #[:end]
-                except IndexError:
-                    # continue
-                    end = " "#.find(',')
-                    if "wiki" in data_dir:
-                        assert "Q" in temp[0]
-                    ent2text[temp[0]] = end #[:end]
-  
-        entities = list(ent2text.keys())
-        ent2token = {ent : f"[ENTITY_{i}]" for i, ent in enumerate(entities)}
-        ent2id = {ent : i for i, ent in enumerate(entities)}
-        
-        rel2text = {}
-        with open(os.path.join(data_dir, "relation2text.txt"), 'r') as f:
-            rel_lines = f.readlines()
-            for line in rel_lines:
-                temp = line.strip().split('\t')
-                rel2text[temp[0]] = temp[1]      
-
-        relation_names = {}
-        with open(os.path.join(data_dir, "relations.txt"), "r") as file:
-            for line in file.readlines():
-                t = line.strip()
-                relation_names[t] = rel2text[t]
-
-        tmp_lines = []
-        not_in_text = 0
-        for line in tqdm(lines, desc="delete entities without text name."):
-            if (line[0] not in ent2text) or (line[2] not in ent2text) or (line[1] not in rel2text):
-                not_in_text += 1
-                continue
-            tmp_lines.append(line)
-        lines = tmp_lines
-        print(f"total entity not in text : {not_in_text} ")
-
-        relations = list(rel2text.keys())
-        rel2token = {rel : f"[RELATION_{i}]" for i, rel in enumerate(relations)}
-        # rel id -> relation token id
-        num_entities = len(self.get_entities(args.data_dir))
-        rel2id = {w:i+num_entities for i,w in enumerate(relation_names.keys())}
-
-
-        with open(os.path.join(data_dir, "masked_head_neighbor.txt"), 'r') as file:
-            masked_head_neighbor = json.load(file)
-
-        with open(os.path.join(data_dir, "masked_tail_neighbor.txt"), 'r') as file:
-            masked_tail_neighbor = json.load(file)
-        
-        examples = []
-        # head filter head entity
-        head_filter_entities = defaultdict(list)
-        tail_filter_entities = defaultdict(list)
-
-        dataset_list = ["train.tsv", "dev.tsv", "test.tsv"]
-        # in training, only use the train triples
-        if set_type == "train" and not args.pretrain: dataset_list = dataset_list[0:1]
-        for m in dataset_list:
-            with open(os.path.join(data_dir, m), 'r') as file:
-                train_lines = file.readlines()
-                for idx in range(len(train_lines)):
-                    train_lines[idx] = train_lines[idx].strip().split("\t")
-
-            for line in train_lines:
-                tail_filter_entities["\t".join([line[0], line[1]])].append(line[2])
-                head_filter_entities["\t".join([line[2], line[1]])].append(line[0])
-        
-        max_head_entities = max(len(_) for _ in head_filter_entities.values())
-        max_tail_entities = max(len(_) for _ in tail_filter_entities.values())
-
-        # use bce loss, ignore the mlm
-        if set_type == "train" and args.bce:
-            lines = []
-            for k, v in tail_filter_entities.items():
-                h, r = k.split('\t')
-                t = v[0]
-                lines.append([h, r, t])
-            for k, v in head_filter_entities.items():
-                t, r = k.split('\t')
-                h = v[0]
-                lines.append([h, r, t])
-        
-
-        # for training , select each entity as for get mask embedding.
-        if args.pretrain:
-            rel = list(rel2text.keys())[0]
-            lines = []
-            for k in ent2text.keys():
-                lines.append([k, rel, k])
-        
-        print(f"max number of filter entities : {max_head_entities} {max_tail_entities}")
-        # 把子图信息加入到filter_init中（初始化为文件夹，及固定子图），设置为全局变量，solve中调用
-        from os import cpu_count
-        threads = min(1, cpu_count())
-        filter_init(head_filter_entities, tail_filter_entities,ent2text, rel2text, ent2id, ent2token, rel2id, masked_head_neighbor, masked_tail_neighbor, rel2token
-            )
-        
-        if hasattr(args, "faiss_init") and args.faiss_init:
-            annotate_ = partial(
-                solve_get_knowledge_store,
-                pretrain=self.args.pretrain
-            )
-        else:
-            annotate_ = partial(
-                solve,
-                pretrain=self.args.pretrain,
-                max_triplet=self.args.max_triplet
-            )
-        examples = list(
-            tqdm(
-                map(annotate_, lines),
-                total=len(lines),
-                desc="convert text to examples"
-            )
-        )
-
-        tmp_examples = []
-        for e in examples:
-            for ee in e:
-                tmp_examples.append(ee)
-        examples = tmp_examples
-        # delete vars
-        del head_filter_entities, tail_filter_entities, ent2text, rel2text, ent2id, ent2token, rel2id
-        return examples
-
-class Verbalizer(object):
-    def __init__(self, args):
-        if "WN18RR" in args.data_dir:
-            self.mode = "WN18RR"
-        elif "FB15k" in args.data_dir:
-            self.mode = "FB15k"
-        elif "umls" in args.data_dir:
-            self.mode = "umls"
-        elif "codexs" in args.data_dir:
-            self.mode = "codexs"
-        elif "FB13" in args.data_dir:
-            self.mode = "FB13"
-        elif "WN11" in args.data_dir:
-            self.mode = "WN11"
-        
-    
-    def _convert(self, head, relation, tail):
-        if self.mode == "umls":
-            return f"The {relation} {head} is "
-        
-        return f"{head} {relation}"
-
-
-class KGCDataset(Dataset):
-    def __init__(self, features):
-        self.features = features
-
-    def __getitem__(self, index):
-        return self.features[index]
-    
-    def __len__(self):
-        return len(self.features)
-
-def convert_examples_to_features_init(tokenizer_for_convert):
-    global tokenizer
-    tokenizer = tokenizer_for_convert
-
-def convert_examples_to_features(example, max_seq_length, mode, pretrain=1):
-    """Loads a data file into a list of `InputBatch`s."""
-    text_a = " ".join(example.text_a.split()[:128])
-    text_b = " ".join(example.text_b.split()[:128])
-    text_c = " ".join(example.text_c.split()[:128])
-    
-    if pretrain:
-        input_text_a = text_a
-        input_text_b = text_b
-    else:
-        input_text_a = " ".join([text_a, text_b])
-        input_text_b = text_c
-    
-
-    inputs = tokenizer(
-        input_text_a,
-        input_text_b,
-        truncation="longest_first",
-        max_length=max_seq_length,
-        padding="longest",
-        add_special_tokens=True,
-    )
-    # assert tokenizer.mask_token_id in inputs.input_ids, "mask token must in input"
-
-    features = asdict(InputFeatures(input_ids=inputs["input_ids"],
-                            attention_mask=inputs['attention_mask'],
-                            labels=torch.tensor(example.label),
-                            label=torch.tensor(example.real_label)
-        )
-    )
-    return features
-
-
-def _truncate_seq_pair(tokens_a, tokens_b, max_length):
-    """Truncates a sequence pair in place to the maximum length."""
-
-    # This is a simple heuristic which will always truncate the longer sequence
-    # one token at a time. This makes more sense than truncating an equal percent
-    # of tokens from each, since if one sequence is very short then each token
-    # that's truncated likely contains more information than a longer sequence.
-    while True:
-        total_length = len(tokens_a) + len(tokens_b)
-        if total_length <= max_length:
-            break
-        if len(tokens_a) > len(tokens_b):
-            tokens_a.pop()
-        else:
-            tokens_b.pop()
-
-def _truncate_seq_triple(tokens_a, tokens_b, tokens_c, max_length):
-    """Truncates a sequence triple in place to the maximum length."""
-
-    # This is a simple heuristic which will always truncate the longer sequence
-    # one token at a time. This makes more sense than truncating an equal percent
-    # of tokens from each, since if one sequence is very short then each token
-    # that's truncated likely contains more information than a longer sequence.
-    while True:
-        total_length = len(tokens_a) + len(tokens_b) + len(tokens_c)
-        if total_length <= max_length:
-            break
-        if len(tokens_a) > len(tokens_b) and len(tokens_a) > len(tokens_c):
-            tokens_a.pop()
-        elif len(tokens_b) > len(tokens_a) and len(tokens_b) > len(tokens_c):
-            tokens_b.pop()
-        elif len(tokens_c) > len(tokens_a) and len(tokens_c) > len(tokens_b):
-            tokens_c.pop()
-        else:
-            tokens_c.pop()
-
-
-@cache_results(_cache_fp="./dataset")
-def get_dataset(args, processor, label_list, tokenizer, mode):
-
-    assert mode in ["train", "dev", "test"], "mode must be in train dev test!"
-
-    # use training data to construct the entity embedding
-    combine_train_and_test = False
-    if args.faiss_init and mode == "test" and not args.pretrain:
-        mode = "train"
-        if "ind" in args.data_dir: combine_train_and_test = True
-    else:
-        pass
-
-    if mode == "train":
-        train_examples = processor.get_train_examples(args.data_dir)
-    elif mode == "dev":
-        train_examples = processor.get_dev_examples(args.data_dir)
-    else:
-        train_examples = processor.get_test_examples(args.data_dir)
-    
-    if combine_train_and_test:
-        logger.info("use all the dataset for getting the entity mask embedding in pretraining pretraining")
-        logger.info("use all the dataset for getting the entity mask embedding in pretraining pretraining")
-        train_examples = processor.get_test_examples(args.data_dir) + processor.get_train_examples(args.data_dir) + processor.get_dev_examples(args.data_dir)
-
-    from os import cpu_count
-    with open(os.path.join(args.data_dir, f"examples_{mode}.txt"), 'w') as file:
-        for line in train_examples:
-            d = {}
-            d.update(line.__dict__)
-            file.write(json.dumps(d) + '\n')
-
-    # 这里应该不需要重新from_pretrain，必须沿用加入token的
-    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=False)
-
-    features = []
-
-    file_inputs = [os.path.join(args.data_dir, f"examples_{mode}.txt")]
-    file_outputs = [os.path.join(args.data_dir, f"features_{mode}.txt")]
-
-    with contextlib.ExitStack() as stack:
-        inputs = [
-            stack.enter_context(open(input, "r", encoding="utf-8"))
-            if input != "-" else sys.stdin
-            for input in file_inputs
-        ]
-        outputs = [
-            stack.enter_context(open(output, "w", encoding="utf-8"))
-            if output != "-" else sys.stdout
-            for output in file_outputs
-        ]
-
-        encoder = MultiprocessingEncoder(tokenizer, args)
-        pool = Pool(16, initializer=encoder.initializer)
-        encoder.initializer()
-        encoded_lines = pool.imap(encoder.encode_lines, zip(*inputs), 1000)
-        # encoded_lines = map(encoder.encode_lines, zip(*inputs))
-
-        stats = Counter()
-        for i, (filt, enc_lines) in tqdm(enumerate(encoded_lines, start=1), total=len(train_examples)):
-            if filt == "PASS":
-                for enc_line, output_h in zip(enc_lines, outputs):
-                    features.append(eval(enc_line))
-                    # features.append(enc_line)
-                    # print(enc_line, file=output_h)
-            else:
-                stats["num_filtered_" + filt] += 1
-
-        for k, v in stats.most_common():
-            print("[{}] filtered {} lines".format(k, v), file=sys.stderr)
-
-    for f_id, f in enumerate(features):
-        en = features[f_id].pop("en")
-        rel = features[f_id].pop("rel")
-        graph = features[f_id].pop("graph")
-        real_label = f['label']
-        features[f_id]['distance_attention'] = torch.Tensor(features[f_id]['distance_attention'])
-        
-        cnt = 0
-        cnt_2 = 0
-        if not isinstance(en, list): break
-
-        pos = 0
-        for i,t in enumerate(f['input_ids']):
-            if t == tokenizer.pad_token_id:
-                features[f_id]['input_ids'][i] = en[cnt] + len(tokenizer)
-                cnt += 1
-            if t == tokenizer.unk_token_id:
-                features[f_id]['input_ids'][i] = graph[cnt_2] + len(tokenizer)
-                cnt_2 += 1
-            if features[f_id]['input_ids'][i] == real_label + len(tokenizer):
-                pos = i
-            if cnt_2 == len(graph) and cnt == len(en): break
-            # 如果等于UNK， pop出图节点list，然后替换
-        assert not (args.faiss_init and pos == 0)
-        features[f_id]['pos'] = pos
-
-        # for i,t in enumerate(f['input_ids']):
-        #     if t == tokenizer.pad_token_id:
-        #         features[f_id]['input_ids'][i] = rel + len(tokenizer) + num_entities
-        #         break
-
-        
-
-    features = KGCDataset(features)
-    return features
-
-
-class MultiprocessingEncoder(object):
-    def __init__(self, tokenizer, args):
-        self.tokenizer = tokenizer
-        self.pretrain = args.pretrain
-        self.max_seq_length = args.max_seq_length
-
-    def initializer(self):
-        global bpe
-        bpe = self.tokenizer
-
-    def encode(self, line):
-        global bpe
-        ids = bpe.encode(line)
-        return list(map(str, ids))
-
-    def decode(self, tokens):
-        global bpe
-        return bpe.decode(tokens)
-
-    def encode_lines(self, lines):
-        """
-        Encode a set of lines. All lines will be encoded together.
-        """
-        enc_lines = []
-        for line in lines:
-            line = line.strip()
-            if len(line) == 0:
-                return ["EMPTY", None]
-            # enc_lines.append(" ".join(tokens))
-            enc_lines.append(json.dumps(self.convert_examples_to_features(example=eval(line))))
-            # enc_lines.append(" ")
-            # enc_lines.append("123")
-        return ["PASS", enc_lines]
-
-    def decode_lines(self, lines):
-        dec_lines = []
-        for line in lines:
-            tokens = map(int, line.strip().split())
-            dec_lines.append(self.decode(tokens))
-        return ["PASS", dec_lines]
-
-    def convert_examples_to_features(self, example):
-        pretrain = self.pretrain
-        max_seq_length = self.max_seq_length
-        global bpe
-        """Loads a data file into a list of `InputBatch`s."""
-        # tokens_a = tokenizer.tokenize(example.text_a)
-        # tokens_b = tokenizer.tokenize(example.text_b)
-        # tokens_c = tokenizer.tokenize(example.text_c)
-
-        # _truncate_seq_triple(tokens_a, tokens_b, tokens_c, max_length= max_seq_length)
-        # text_a = " ".join(example['text_a'].split()[:128])
-        # text_b = " ".join(example['text_b'].split()[:128])
-        # text_c = " ".join(example['text_c'].split()[:128])
-        
-        text_a = example['text_a']
-        text_b = example['text_b']
-        text_c = example['text_c']
-        text_d = example['text_d']
-        graph_list = example['graph_inf']
-
-        if pretrain:
-            # the des of xxx is [MASK] .
-            input_text = f"The description of {text_a} is that {text_b} ."
-            inputs = bpe(
-                input_text,
-                truncation="longest_first",
-                max_length=max_seq_length,
-                padding="longest",
-                add_special_tokens=True,
-            )
-        else:
-            if text_a == "[MASK]":
-                input_text_a = " ".join([text_a, text_b])
-                input_text_b = text_c
-                origin_triplet = ["MASK"] + example['en']
-                graph_seq = ["MASK"] + example['en'] + text_d
-            else:
-                input_text_a = text_a
-                input_text_b = " ".join([text_b, text_c])
-                origin_triplet = example['en'] + ["MASK"]
-                graph_seq = example['en'] + ["MASK"] + text_d
-            # 加入graph信息, 拼接等量[UNK]
-            input_text_b = " ".join(["[CLS]", input_text_a, input_text_b, bpe.unk_token * len(text_d)])
-
-            inputs = bpe(
-                input_text_b,
-                truncation="longest_first",
-                max_length=max_seq_length,
-                padding="longest",
-                add_special_tokens=False,
-            )
-        # assert bpe.mask_token_id in inputs.input_ids, "mask token must in input"
-
-        # graph_seq = input_text_b[] 把图结构信息读取出来
-        # [CLS] [ENTITY_13258] [RELATION_68] [MASK] [ENTITY_4] [RELATION_127] [ENTITY_8] [RELATION_9] [ENTITY_9011] [ENTITY_12477] [PAD] [PAD]
-        # 获取图结构信息
-        # 首先在solve中加入一个存储所有子图三元组的临时存储变量
-        # 在这里graph_information = example['graph']
-        new_rel = set()
-        new_rel.add(tuple((origin_triplet[0], origin_triplet[1])))
-        new_rel.add(tuple((origin_triplet[1], origin_triplet[0])))
-        new_rel.add(tuple((origin_triplet[1], origin_triplet[2])))
-        new_rel.add(tuple((origin_triplet[2], origin_triplet[1])))
-        for triplet in graph_list:
-            rel1, rel2, rel3, rel4 = tuple((triplet[0], triplet[1])), tuple((triplet[1], triplet[2])), tuple((triplet[1], triplet[0])), tuple((triplet[2], triplet[1]))
-            new_rel.add(rel1)
-            new_rel.add(rel2)
-            new_rel.add(rel3)
-            new_rel.add(rel4)
-        # 这里的三元组转换为new_rel
-        KGid2Graphid_map = defaultdict(int)
-        for i in range(len(graph_seq)):
-            KGid2Graphid_map[graph_seq[i]] = i
-
-        N = len(graph_seq)
-        adj = torch.zeros([N, N], dtype=torch.bool)
-        for item in list(new_rel):
-            adj[KGid2Graphid_map[item[0]], KGid2Graphid_map[item[1]]] = True
-        shortest_path_result, _ = algos.floyd_warshall(adj.numpy())
-        max_dist = np.amax(shortest_path_result)
-        # [PAD]部分， [CLS]部分补全， [SEP]额外引入也当作[PAD]处理
-        # 加上一个attention_bias, PAD部分设置为-inf，在送入model前，对其进行处理, 将其相加（让模型无法关注PAD）
-        
-        # 加入attention到huggingface的BertForMaskedLM（这个可能需要再去查查）
-        # attention_bias = torch.zero(N, N, dtype=torch.float)
-        # attention_bias[torch.tensor(shortest_path_result == )]
-        features = asdict(InputFeatures(input_ids=inputs["input_ids"],
-                                attention_mask=inputs['attention_mask'],
-                                labels=example['label'],
-                                label=example['real_label'],
-                                en=example['en_id'],
-                                rel=example['rel'],
-                                graph=example['text_d_id'],
-                                distance_attention = shortest_path_result.tolist(),
-            )
-        )
-        return features

data_loader.py

@@ -0,0 +1,136 @@
+import torch
+import numpy as np
+
+from torch.utils.data import Dataset
+
+
+class TrainDataset(Dataset):
+    """
+    Training Dataset class.
+    Parameters
+    ----------
+    triples:	The triples used for training the model
+    params:		Parameters for the experiments
+
+    Returns
+    -------
+    A training Dataset class instance used by DataLoader
+    """
+
+    def __init__(self, triples, params):
+        self.triples = triples
+        self.p = params
+        self.strategy = self.p.train_strategy
+        self.entities = np.arange(self.p.num_ent, dtype=np.int32)
+
+    def __len__(self):
+        return len(self.triples)
+
+    def __getitem__(self, idx):
+        ele = self.triples[idx]
+        triple, label, sub_samp = torch.LongTensor(ele['triple']), np.int32(
+            ele['label']), np.float32(ele['sub_samp'])
+        trp_label = self.get_label(label)
+
+        if self.p.lbl_smooth != 0.0:
+            trp_label = (1.0 - self.p.lbl_smooth) * \
+                trp_label + (1.0/self.p.num_ent)
+
+        if self.strategy == 'one_to_n':
+            return triple, trp_label, None, None
+
+        elif self.strategy == 'one_to_x':
+            sub_samp = torch.FloatTensor([sub_samp])
+            neg_ent = torch.LongTensor(self.get_neg_ent(triple, label))
+            return triple, trp_label, neg_ent, sub_samp
+        else:
+            raise NotImplementedError
+
+    @staticmethod
+    def collate_fn(data):
+        triple = torch.stack([_[0] for _ in data], dim=0)
+        trp_label = torch.stack([_[1] for _ in data], dim=0)
+
+        if not data[0][2] is None:							# one_to_x
+            neg_ent = torch.stack([_[2] for _ in data], dim=0)
+            sub_samp = torch.cat([_[3] for _ in data], dim=0)
+            return triple, trp_label, neg_ent, sub_samp
+        else:
+            return triple, trp_label
+
+    def get_neg_ent(self, triple, label):
+        def get(triple, label):
+            if self.strategy == 'one_to_x':
+                pos_obj = triple[2]
+                mask = np.ones([self.p.num_ent], dtype=np.bool)
+                mask[label] = 0
+                neg_ent = np.int32(np.random.choice(
+                    self.entities[mask], self.p.neg_num, replace=False)).reshape([-1])
+                neg_ent = np.concatenate((pos_obj.reshape([-1]), neg_ent))
+            else:
+                pos_obj = label
+                mask = np.ones([self.p.num_ent], dtype=np.bool)
+                mask[label] = 0
+                neg_ent = np.int32(np.random.choice(
+                    self.entities[mask], self.p.neg_num - len(label), replace=False)).reshape([-1])
+                neg_ent = np.concatenate((pos_obj.reshape([-1]), neg_ent))
+
+                if len(neg_ent) > self.p.neg_num:
+                    import pdb
+                    pdb.set_trace()
+
+            return neg_ent
+
+        neg_ent = get(triple, label)
+        return neg_ent
+
+    def get_label(self, label):
+        if self.strategy == 'one_to_n':
+            y = np.zeros([self.p.num_ent], dtype=np.float32)
+            for e2 in label:
+                y[e2] = 1.0
+        elif self.strategy == 'one_to_x':
+            y = [1] + [0] * self.p.neg_num
+        else:
+            raise NotImplementedError
+        return torch.FloatTensor(y)
+
+
+class TestDataset(Dataset):
+    """
+    Evaluation Dataset class.
+    Parameters
+    ----------
+    triples:	The triples used for evaluating the model
+    params:		Parameters for the experiments
+
+    Returns
+    -------
+    An evaluation Dataset class instance used by DataLoader for model evaluation
+    """
+
+    def __init__(self, triples, params):
+        self.triples = triples
+        self.p = params
+
+    def __len__(self):
+        return len(self.triples)
+
+    def __getitem__(self, idx):
+        ele = self.triples[idx]
+        triple, label = torch.LongTensor(ele['triple']), np.int32(ele['label'])
+        label = self.get_label(label)
+
+        return triple, label
+
+    @staticmethod
+    def collate_fn(data):
+        triple = torch.stack([_[0] for _ in data], dim=0)
+        label = torch.stack([_[1] for _ in data], dim=0)
+        return triple, label
+
+    def get_label(self, label):
+        y = np.zeros([self.p.num_ent], dtype=np.float32)
+        for e2 in label:
+            y[e2] = 1.0
+        return torch.FloatTensor(y)

dataset/FB15k-237/.ipynb_checkpoints/get_neighbor-checkpoint.ipynb

@@ -1,6 +0,0 @@
-{
- "cells": [],
- "metadata": {},
- "nbformat": 4,
- "nbformat_minor": 2
-}

dataset/FB15k-237/get_neighbor.ipynb

@@ -1,155 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path1 = './entities.txt'\n",
-    "path2 = './relations.txt'\n",
-    "path3 = './train.tsv'\n",
-    "path4 = './dev.tsv'\n",
-    "path5 = './test.tsv'\n",
-    "path6 = './get_neighbor/entity2id.txt'\n",
-    "path7 = './get_neighbor/relation2id.txt'\n",
-    "path8 = './get_neighbor/train2id.txt'\n",
-    "path9 = './get_neighbor/valid2id.txt'\n",
-    "path10 = './get_neighbor/test2id.txt'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path1, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "cnt = 0\n",
-    "with open(path6, 'w') as f:\n",
-    "    for line in a:\n",
-    "        en = line.strip()\n",
-    "        f.write(en + '\\t' + str(cnt) + '\\n')\n",
-    "        cnt += 1\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path2, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "cnt = 0\n",
-    "with open(path7, 'w') as f:\n",
-    "    for line in a:\n",
-    "        re = line.strip()\n",
-    "        f.write(re + '\\t' + str(cnt) + '\\n')\n",
-    "        cnt += 1\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path6, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "en2id = {}\n",
-    "for line in a:\n",
-    "    b = line.strip().split('\\t')\n",
-    "    en, num = b[0], b[1]\n",
-    "    en2id[en] = num"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path7, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "re2id = {}\n",
-    "for line in a:\n",
-    "    b = line.strip().split('\\t')\n",
-    "    re, num = b[0], b[1]\n",
-    "    re2id[re] = num"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path3, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "with open(path8, 'w') as f:\n",
-    "    for line in a:\n",
-    "        b = line.strip().split('\\t')\n",
-    "        h, r, t = b[0], b[1], b[2]\n",
-    "        f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path4, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "with open(path9, 'w') as f:\n",
-    "    for line in a:\n",
-    "        b = line.strip().split('\\t')\n",
-    "        h, r, t = b[0], b[1], b[2]\n",
-    "        f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path5, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "with open(path10, 'w') as f:\n",
-    "    for line in a:\n",
-    "        b = line.strip().split('\\t')\n",
-    "        h, r, t = b[0], b[1], b[2]\n",
-    "        f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
-    "    "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [default]",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

dataset/FB15k-237/get_neighbor/relation2id.txt

@@ -1,237 +0,0 @@
-/soccer/football_team/current_roster./soccer/football_roster_position/position	0
-/music/artist/origin	1
-/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position	2
-/food/food/nutrients./food/nutrition_fact/nutrient	3
-/film/actor/film./film/performance/film	4
-/award/award_nominee/award_nominations./award/award_nomination/nominated_for	5
-/government/political_party/politicians_in_this_party./government/political_party_tenure/politician	6
-/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency	7
-/people/deceased_person/place_of_death	8
-/people/person/profession	9
-/location/administrative_division/first_level_division_of	10
-/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team	11
-/education/university/international_tuition./measurement_unit/dated_money_value/currency	12
-/location/us_county/county_seat	13
-/location/location/partially_contains	14
-/tv/tv_program/program_creator	15
-/film/film/music	16
-/tv/tv_program/languages	17
-/common/topic/webpage./common/webpage/category	18
-/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy	19
-/education/field_of_study/students_majoring./education/education/major_field_of_study	20
-/business/business_operation/assets./measurement_unit/dated_money_value/currency	21
-/film/film_set_designer/film_sets_designed	22
-/dataworld/gardening_hint/split_to	23
-/people/person/languages	24
-/business/job_title/people_with_this_title./business/employment_tenure/company	25
-/location/country/form_of_government	26
-/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language	27
-/people/person/place_of_birth	28
-/sports/sports_team/colors	29
-/education/educational_institution/school_type	30
-/award/award_category/winners./award/award_honor/award_winner	31
-/organization/organization/headquarters./location/mailing_address/citytown	32
-/education/educational_degree/people_with_this_degree./education/education/student	33
-/government/legislative_session/members./government/government_position_held/legislative_sessions	34
-/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium	35
-/education/educational_degree/people_with_this_degree./education/education/major_field_of_study	36
-/location/hud_county_place/county	37
-/location/administrative_division/country	38
-/film/film/film_production_design_by	39
-/award/award_winning_work/awards_won./award/award_honor/award	40
-/organization/organization/headquarters./location/mailing_address/state_province_region	41
-/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category	42
-/tv/tv_program/country_of_origin	43
-/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal	44
-/location/country/second_level_divisions	45
-/award/award_ceremony/awards_presented./award/award_honor/honored_for	46
-/organization/organization_member/member_of./organization/organization_membership/organization	47
-/education/educational_institution/campuses	48
-/music/artist/contribution./music/recording_contribution/performance_role	49
-/award/ranked_item/appears_in_ranked_lists./award/ranking/list	50
-/people/person/religion	51
-/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month	52
-/film/special_film_performance_type/film_performance_type./film/performance/film	53
-/award/award_nominee/award_nominations./award/award_nomination/award	54
-/location/statistical_region/religions./location/religion_percentage/religion	55
-/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school	56
-/film/film/distributors./film/film_film_distributor_relationship/region	57
-/government/politician/government_positions_held./government/government_position_held/legislative_sessions	58
-/organization/role/leaders./organization/leadership/organization	59
-/tv/tv_network/programs./tv/tv_network_duration/program	60
-/soccer/football_team/current_roster./sports/sports_team_roster/position	61
-/music/instrument/instrumentalists	62
-/business/business_operation/operating_income./measurement_unit/dated_money_value/currency	63
-/people/cause_of_death/people	64
-/film/film/film_art_direction_by	65
-/people/person/sibling_s./people/sibling_relationship/sibling	66
-/film/film/cinematography	67
-/film/actor/dubbing_performances./film/dubbing_performance/language	68
-/base/biblioness/bibs_location/state	69
-/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed	70
-/people/person/gender	71
-/education/field_of_study/students_majoring./education/education/student	72
-/base/popstra/celebrity/dated./base/popstra/dated/participant	73
-/sports/sports_team/roster./american_football/football_roster_position/position	74
-/award/award_winner/awards_won./award/award_honor/award_winner	75
-/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics	76
-/film/director/film	77
-/tv/tv_producer/programs_produced./tv/tv_producer_term/program	78
-/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film	79
-/olympics/olympic_games/sports	80
-/music/record_label/artist	81
-/education/university/local_tuition./measurement_unit/dated_money_value/currency	82
-/film/film/story_by	83
-/people/person/spouse_s./people/marriage/spouse	84
-/sports/sports_league/teams./sports/sports_league_participation/team	85
-/people/profession/specialization_of	86
-/base/americancomedy/celebrity_impressionist/celebrities_impersonated	87
-/tv/tv_program/genre	88
-/award/award_category/nominees./award/award_nomination/nominated_for	89
-/language/human_language/countries_spoken_in	90
-/organization/organization/headquarters./location/mailing_address/country	91
-/location/statistical_region/gdp_real./measurement_unit/adjusted_money_value/adjustment_currency	92
-/education/university/fraternities_and_sororities	93
-/award/award_nominee/award_nominations./award/award_nomination/award_nominee	94
-/military/military_combatant/military_conflicts./military/military_combatant_group/combatants	95
-/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for	96
-/location/location/time_zones	97
-/film/film/dubbing_performances./film/dubbing_performance/actor	98
-/film/film_subject/films	99
-/education/educational_degree/people_with_this_degree./education/education/institution	100
-/education/educational_institution/colors	101
-/award/award_category/category_of	102
-/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program	103
-/film/film/language	104
-/music/group_member/membership./music/group_membership/group	105
-/business/business_operation/revenue./measurement_unit/dated_money_value/currency	106
-/film/film/film_festivals	107
-/film/actor/film./film/performance/special_performance_type	108
-/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency	109
-/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office	110
-/base/aareas/schema/administrative_area/administrative_parent	111
-/award/award_winning_work/awards_won./award/award_honor/award_winner	112
-/organization/organization/place_founded	113
-/soccer/football_player/current_team./sports/sports_team_roster/team	114
-/government/politician/government_positions_held./government/government_position_held/basic_title	115
-/music/artist/track_contributions./music/track_contribution/role	116
-/base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months	117
-/celebrities/celebrity/celebrity_friends./celebrities/friendship/friend	118
-/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school	119
-/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee	120
-/influence/influence_node/peers./influence/peer_relationship/peers	121
-/medicine/disease/risk_factors	122
-/broadcast/content/artist	123
-/film/film/estimated_budget./measurement_unit/dated_money_value/currency	124
-/military/military_conflict/combatants./military/military_combatant_group/combatants	125
-/location/capital_of_administrative_division/capital_of./location/administrative_division_capital_relationship/administrative_division	126
-/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor	127
-/people/deceased_person/place_of_burial	128
-/location/location/adjoin_s./location/adjoining_relationship/adjoins	129
-/music/group_member/membership./music/group_membership/role	130
-/award/award_ceremony/awards_presented./award/award_honor/award_winner	131
-/film/film/prequel	132
-/film/film/produced_by	133
-/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type	134
-/sports/sports_position/players./sports/sports_team_roster/team	135
-/olympics/olympic_games/participating_countries	136
-/music/genre/parent_genre	137
-/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program	138
-/music/genre/artists	139
-/film/film/genre	140
-/people/person/employment_history./business/employment_tenure/company	141
-/education/university/domestic_tuition./measurement_unit/dated_money_value/currency	142
-/people/person/nationality	143
-/location/country/capital	144
-/location/statistical_region/gni_per_capita_in_ppp_dollars./measurement_unit/dated_money_value/currency	145
-/base/aareas/schema/administrative_area/capital	146
-/business/business_operation/industry	147
-/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source	148
-/film/film/other_crew./film/film_crew_gig/crewmember	149
-/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer	150
-/film/film/film_format	151
-/medicine/disease/notable_people_with_this_condition	152
-/film/film/costume_design_by	153
-/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office	154
-/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency	155
-/sports/sports_team/roster./baseball/baseball_roster_position/position	156
-/award/award_winning_work/awards_won./award/award_honor/honored_for	157
-/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics	158
-/celebrities/celebrity/sexual_relationships./celebrities/romantic_relationship/celebrity	159
-/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony	160
-/organization/organization/child./organization/organization_relationship/child	161
-/organization/organization_founder/organizations_founded	162
-/sports/sports_team/sport	163
-/people/ethnicity/geographic_distribution	164
-/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to	165
-/location/country/official_language	166
-/film/film/production_companies	167
-/user/jg/default_domain/olympic_games/sports	168
-/time/event/locations	169
-/people/person/spouse_s./people/marriage/type_of_union	170
-/government/governmental_body/members./government/government_position_held/legislative_sessions	171
-/media_common/netflix_genre/titles	172
-/user/alexander/philosophy/philosopher/interests	173
-/film/film/runtime./film/film_cut/film_release_region	174
-/education/educational_institution/students_graduates./education/education/student	175
-/base/eating/practicer_of_diet/diet	176
-/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person	177
-/sports/sports_position/players./sports/sports_team_roster/position	178
-/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft	179
-/medicine/symptom/symptom_of	180
-/film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/type_of_appearance	181
-/sports/sports_team_location/teams	182
-/american_football/football_team/current_roster./sports/sports_team_roster/position	183
-/people/person/places_lived./people/place_lived/location	184
-/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency	185
-/film/film/personal_appearances./film/personal_film_appearance/person	186
-/music/instrument/family	187
-/sports/sports_team/roster./basketball/basketball_roster_position/position	188
-/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location	189
-/film/film/release_date_s./film/film_regional_release_date/film_release_region	190
-/award/award_category/disciplines_or_subjects	191
-/base/popstra/celebrity/friendship./base/popstra/friendship/participant	192
-/music/performance_role/regular_performances./music/group_membership/group	193
-/film/film/edited_by	194
-/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club	195
-/base/popstra/celebrity/canoodled./base/popstra/canoodled/participant	196
-/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium	197
-/film/film/other_crew./film/film_crew_gig/film_crew_role	198
-/base/popstra/celebrity/breakup./base/popstra/breakup/participant	199
-/film/film/country	200
-/music/performance_role/regular_performances./music/group_membership/role	201
-/sports/sports_team/roster./american_football/football_historical_roster_position/position_s	202
-/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue	203
-/time/event/instance_of_recurring_event	204
-/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics	205
-/organization/endowed_organization/endowment./measurement_unit/dated_money_value/currency	206
-/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation	207
-/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season	208
-/award/award_category/winners./award/award_honor/ceremony	209
-/government/legislative_session/members./government/government_position_held/district_represented	210
-/influence/influence_node/influenced_by	211
-/base/culturalevent/event/entity_involved	212
-/people/ethnicity/people	213
-/sports/sport/pro_athletes./sports/pro_sports_played/athlete	214
-/location/statistical_region/gdp_nominal_per_capita./measurement_unit/dated_money_value/currency	215
-/location/hud_county_place/place	216
-/base/aareas/schema/administrative_area/administrative_area_type	217
-/base/locations/continents/countries_within	218
-/sports/sports_position/players./american_football/football_historical_roster_position/position_s	219
-/people/person/spouse_s./people/marriage/location_of_ceremony	220
-/education/educational_institution/students_graduates./education/education/major_field_of_study	221
-/film/film/written_by	222
-/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country	223
-/music/performance_role/guest_performances./music/recording_contribution/performance_role	224
-/film/film/featured_film_locations	225
-/education/educational_institution_campus/educational_institution	226
-/sports/pro_athlete/teams./sports/sports_team_roster/team	227
-/people/ethnicity/languages_spoken	228
-/film/film/executive_produced_by	229
-/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type	230
-/location/location/contains	231
-/base/biblioness/bibs_location/country	232
-/user/ktrueman/default_domain/international_organization/member_states	233
-/music/performance_role/track_performances./music/track_contribution/role	234
-/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal	235
-/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members	236

dataset/FB15k-237/relation2text.txt

@@ -1,237 +0,0 @@
-/soccer/football_team/current_roster./soccer/football_roster_position/position	soccer football team current roster. soccer football roster position position
-/music/artist/origin	music artist origin
-/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position	ice hockey hockey team current roster. sports sports team roster position
-/food/food/nutrients./food/nutrition_fact/nutrient	food food nutrients. food nutrition fact nutrient
-/film/actor/film./film/performance/film	film actor film. film performance film
-/award/award_nominee/award_nominations./award/award_nomination/nominated_for	award award nominee award nominations. award award nomination nominated for
-/government/political_party/politicians_in_this_party./government/political_party_tenure/politician	government political party politicians in this party. government political party tenure politician
-/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency	base schemastaging person extra net worth. measurement unit dated money value currency
-/people/deceased_person/place_of_death	people deceased person place of death
-/people/person/profession	people person profession
-/location/administrative_division/first_level_division_of	location administrative division first level division of
-/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team	base marchmadness ncaa basketball tournament seeds. base marchmadness ncaa tournament seed team
-/education/university/international_tuition./measurement_unit/dated_money_value/currency	education university international tuition. measurement unit dated money value currency
-/location/us_county/county_seat	location us county county seat
-/location/location/partially_contains	location location partially contains
-/tv/tv_program/program_creator	tv tv program program creator
-/film/film/music	film film music
-/tv/tv_program/languages	tv tv program languages
-/common/topic/webpage./common/webpage/category	common topic webpage. common webpage category
-/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy	user tsegaran random taxonomy subject entry. user tsegaran random taxonomy entry taxonomy
-/education/field_of_study/students_majoring./education/education/major_field_of_study	education field of study students majoring. education education major field of study
-/business/business_operation/assets./measurement_unit/dated_money_value/currency	business business operation assets. measurement unit dated money value currency
-/film/film_set_designer/film_sets_designed	film film set designer film sets designed
-/dataworld/gardening_hint/split_to	dataworld gardening hint split to
-/people/person/languages	people person languages
-/business/job_title/people_with_this_title./business/employment_tenure/company	business job title people with this title. business employment tenure company
-/location/country/form_of_government	location country form of government
-/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language	base schemastaging organization extra phone number. base schemastaging phone sandbox service language
-/people/person/place_of_birth	people person place of birth
-/sports/sports_team/colors	sports sports team colors
-/education/educational_institution/school_type	education educational institution school type
-/award/award_category/winners./award/award_honor/award_winner	award award category winners. award award honor award winner
-/organization/organization/headquarters./location/mailing_address/citytown	organization organization headquarters. location mailing address citytown
-/education/educational_degree/people_with_this_degree./education/education/student	education educational degree people with this degree. education education student
-/government/legislative_session/members./government/government_position_held/legislative_sessions	government legislative session members. government government position held legislative sessions
-/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium	film film distributors. film film film distributor relationship film distribution medium
-/education/educational_degree/people_with_this_degree./education/education/major_field_of_study	education educational degree people with this degree. education education major field of study
-/location/hud_county_place/county	location hud county place county
-/location/administrative_division/country	location administrative division country
-/film/film/film_production_design_by	film film film production design by
-/award/award_winning_work/awards_won./award/award_honor/award	award award winning work awards won. award award honor award
-/organization/organization/headquarters./location/mailing_address/state_province_region	organization organization headquarters. location mailing address state province region
-/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category	base schemastaging organization extra phone number. base schemastaging phone sandbox contact category
-/tv/tv_program/country_of_origin	tv tv program country of origin
-/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal	olympics olympic participating country medals won. olympics olympic medal honor medal
-/location/country/second_level_divisions	location country second level divisions
-/award/award_ceremony/awards_presented./award/award_honor/honored_for	award award ceremony awards presented. award award honor honored for
-/organization/organization_member/member_of./organization/organization_membership/organization	organization organization member member of. organization organization membership organization
-/education/educational_institution/campuses	education educational institution campuses
-/music/artist/contribution./music/recording_contribution/performance_role	music artist contribution. music recording contribution performance role
-/award/ranked_item/appears_in_ranked_lists./award/ranking/list	award ranked item appears in ranked lists. award ranking list
-/people/person/religion	people person religion
-/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month	travel travel destination climate. travel travel destination monthly climate month
-/film/special_film_performance_type/film_performance_type./film/performance/film	film special film performance type film performance type. film performance film
-/award/award_nominee/award_nominations./award/award_nomination/award	award award nominee award nominations. award award nomination award
-/location/statistical_region/religions./location/religion_percentage/religion	location statistical region religions. location religion percentage religion
-/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school	sports sports league draft picks. sports sports league draft pick school
-/film/film/distributors./film/film_film_distributor_relationship/region	film film distributors. film film film distributor relationship region
-/government/politician/government_positions_held./government/government_position_held/legislative_sessions	government politician government positions held. government government position held legislative sessions
-/organization/role/leaders./organization/leadership/organization	organization role leaders. organization leadership organization
-/tv/tv_network/programs./tv/tv_network_duration/program	tv tv network programs. tv tv network duration program
-/soccer/football_team/current_roster./sports/sports_team_roster/position	soccer football team current roster. sports sports team roster position
-/music/instrument/instrumentalists	music instrument instrumentalists
-/business/business_operation/operating_income./measurement_unit/dated_money_value/currency	business business operation operating income. measurement unit dated money value currency
-/people/cause_of_death/people	people cause of death people
-/film/film/film_art_direction_by	film film film art direction by
-/people/person/sibling_s./people/sibling_relationship/sibling	people person sibling s. people sibling relationship sibling
-/film/film/cinematography	film film cinematography
-/film/actor/dubbing_performances./film/dubbing_performance/language	film actor dubbing performances. film dubbing performance language
-/base/biblioness/bibs_location/state	base biblioness bibs location state
-/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed	base petbreeds city with dogs top breeds. base petbreeds dog city relationship dog breed
-/people/person/gender	people person gender
-/education/field_of_study/students_majoring./education/education/student	education field of study students majoring. education education student
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-/award/award_winner/awards_won./award/award_honor/award_winner	award award winner awards won. award award honor award winner
-/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics	olympics olympic participating country medals won. olympics olympic medal honor olympics
-/film/director/film	film director film
-/tv/tv_producer/programs_produced./tv/tv_producer_term/program	tv tv producer programs produced. tv tv producer term program
-/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film	film film distributor films distributed. film film film distributor relationship film
-/olympics/olympic_games/sports	olympics olympic games sports
-/music/record_label/artist	music record label artist
-/education/university/local_tuition./measurement_unit/dated_money_value/currency	education university local tuition. measurement unit dated money value currency
-/film/film/story_by	film film story by
-/people/person/spouse_s./people/marriage/spouse	people person spouse s. people marriage spouse
-/sports/sports_league/teams./sports/sports_league_participation/team	sports sports league teams. sports sports league participation team
-/people/profession/specialization_of	people profession specialization of
-/base/americancomedy/celebrity_impressionist/celebrities_impersonated	base americancomedy celebrity impressionist celebrities impersonated
-/tv/tv_program/genre	tv tv program genre
-/award/award_category/nominees./award/award_nomination/nominated_for	award award category nominees. award award nomination nominated for
-/language/human_language/countries_spoken_in	language human language countries spoken in
-/organization/organization/headquarters./location/mailing_address/country	organization organization headquarters. location mailing address country
-/location/statistical_region/gdp_real./measurement_unit/adjusted_money_value/adjustment_currency	location statistical region gdp real. measurement unit adjusted money value adjustment currency
-/education/university/fraternities_and_sororities	education university fraternities and sororities
-/award/award_nominee/award_nominations./award/award_nomination/award_nominee	award award nominee award nominations. award award nomination award nominee
-/military/military_combatant/military_conflicts./military/military_combatant_group/combatants	military military combatant military conflicts. military military combatant group combatants
-/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for	award award nominated work award nominations. award award nomination nominated for
-/location/location/time_zones	location location time zones
-/film/film/dubbing_performances./film/dubbing_performance/actor	film film dubbing performances. film dubbing performance actor
-/film/film_subject/films	film film subject films
-/education/educational_degree/people_with_this_degree./education/education/institution	education educational degree people with this degree. education education institution
-/education/educational_institution/colors	education educational institution colors
-/award/award_category/category_of	award award category category of
-/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program	tv tv personality tv regular appearances. tv tv regular personal appearance program
-/film/film/language	film film language
-/music/group_member/membership./music/group_membership/group	music group member membership. music group membership group
-/business/business_operation/revenue./measurement_unit/dated_money_value/currency	business business operation revenue. measurement unit dated money value currency
-/film/film/film_festivals	film film film festivals
-/film/actor/film./film/performance/special_performance_type	film actor film. film performance special performance type
-/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency	organization non profit organization registered with. organization non profit registration registering agency
-/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office	government politician government positions held. government government position held jurisdiction of office
-/base/aareas/schema/administrative_area/administrative_parent	base aareas schema administrative area administrative parent
-/award/award_winning_work/awards_won./award/award_honor/award_winner	award award winning work awards won. award award honor award winner
-/organization/organization/place_founded	organization organization place founded
-/soccer/football_player/current_team./sports/sports_team_roster/team	soccer football player current team. sports sports team roster team
-/government/politician/government_positions_held./government/government_position_held/basic_title	government politician government positions held. government government position held basic title
-/music/artist/track_contributions./music/track_contribution/role	music artist track contributions. music track contribution role
-/base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months	base localfood seasonal month produce available. base localfood produce availability seasonal months
-/celebrities/celebrity/celebrity_friends./celebrities/friendship/friend	celebrities celebrity celebrity friends. celebrities friendship friend
-/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school	sports professional sports team draft picks. sports sports league draft pick school
-/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee	award hall of fame inductees. award hall of fame induction inductee
-/influence/influence_node/peers./influence/peer_relationship/peers	influence influence node peers. influence peer relationship peers
-/medicine/disease/risk_factors	medicine disease risk factors
-/broadcast/content/artist	broadcast content artist
-/film/film/estimated_budget./measurement_unit/dated_money_value/currency	film film estimated budget. measurement unit dated money value currency
-/military/military_conflict/combatants./military/military_combatant_group/combatants	military military conflict combatants. military military combatant group combatants
-/location/capital_of_administrative_division/capital_of./location/administrative_division_capital_relationship/administrative_division	location capital of administrative division capital of. location administrative division capital relationship administrative division
-/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor	tv tv program regular cast. tv regular tv appearance actor
-/people/deceased_person/place_of_burial	people deceased person place of burial
-/location/location/adjoin_s./location/adjoining_relationship/adjoins	location location adjoin s. location adjoining relationship adjoins
-/music/group_member/membership./music/group_membership/role	music group member membership. music group membership role
-/award/award_ceremony/awards_presented./award/award_honor/award_winner	award award ceremony awards presented. award award honor award winner
-/film/film/prequel	film film prequel
-/film/film/produced_by	film film produced by
-/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type	tv tv program tv producer. tv tv producer term producer type
-/sports/sports_position/players./sports/sports_team_roster/team	sports sports position players. sports sports team roster team
-/olympics/olympic_games/participating_countries	olympics olympic games participating countries
-/music/genre/parent_genre	music genre parent genre
-/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program	tv tv writer tv programs. tv tv program writer relationship tv program
-/music/genre/artists	music genre artists
-/film/film/genre	film film genre
-/people/person/employment_history./business/employment_tenure/company	people person employment history. business employment tenure company
-/education/university/domestic_tuition./measurement_unit/dated_money_value/currency	education university domestic tuition. measurement unit dated money value currency
-/people/person/nationality	people person nationality
-/location/country/capital	location country capital
-/location/statistical_region/gni_per_capita_in_ppp_dollars./measurement_unit/dated_money_value/currency	location statistical region gni per capita in ppp dollars. measurement unit dated money value currency
-/base/aareas/schema/administrative_area/capital	base aareas schema administrative area capital
-/business/business_operation/industry	business business operation industry
-/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source	location hud foreclosure area estimated number of mortgages. measurement unit dated integer source
-/film/film/other_crew./film/film_crew_gig/crewmember	film film other crew. film film crew gig crewmember
-/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer	base popstra location vacationers. base popstra vacation choice vacationer
-/film/film/film_format	film film film format
-/medicine/disease/notable_people_with_this_condition	medicine disease notable people with this condition
-/film/film/costume_design_by	film film costume design by
-/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office	government government office category officeholders. government government position held jurisdiction of office
-/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency	location statistical region gdp nominal. measurement unit dated money value currency
-/sports/sports_team/roster./baseball/baseball_roster_position/position	sports sports team roster. baseball baseball roster position position
-/award/award_winning_work/awards_won./award/award_honor/honored_for	award award winning work awards won. award award honor honored for
-/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics	olympics olympic sport athletes. olympics olympic athlete affiliation olympics
-/celebrities/celebrity/sexual_relationships./celebrities/romantic_relationship/celebrity	celebrities celebrity sexual relationships. celebrities romantic relationship celebrity
-/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony	people marriage union type unions of this type. people marriage location of ceremony
-/organization/organization/child./organization/organization_relationship/child	organization organization child. organization organization relationship child
-/organization/organization_founder/organizations_founded	organization organization founder organizations founded
-/sports/sports_team/sport	sports sports team sport
-/people/ethnicity/geographic_distribution	people ethnicity geographic distribution
-/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to	location statistical region places exported to. location imports and exports exported to
-/location/country/official_language	location country official language
-/film/film/production_companies	film film production companies
-/user/jg/default_domain/olympic_games/sports	user jg default domain olympic games sports
-/time/event/locations	time event locations
-/people/person/spouse_s./people/marriage/type_of_union	people person spouse s. people marriage type of union
-/government/governmental_body/members./government/government_position_held/legislative_sessions	government governmental body members. government government position held legislative sessions
-/media_common/netflix_genre/titles	media common netflix genre titles
-/user/alexander/philosophy/philosopher/interests	user alexander philosophy philosopher interests
-/film/film/runtime./film/film_cut/film_release_region	film film runtime. film film cut film release region
-/education/educational_institution/students_graduates./education/education/student	education educational institution students graduates. education education student
-/base/eating/practicer_of_diet/diet	base eating practicer of diet diet
-/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person	tv non character role tv regular personal appearances. tv tv regular personal appearance person
-/sports/sports_position/players./sports/sports_team_roster/position	sports sports position players. sports sports team roster position
-/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft	sports professional sports team draft picks. sports sports league draft pick draft
-/medicine/symptom/symptom_of	medicine symptom symptom of
-/film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/type_of_appearance	film person or entity appearing in film films. film personal film appearance type of appearance
-/sports/sports_team_location/teams	sports sports team location teams
-/american_football/football_team/current_roster./sports/sports_team_roster/position	american football football team current roster. sports sports team roster position
-/people/person/places_lived./people/place_lived/location	people person places lived. people place lived location
-/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency	location statistical region rent50 2. measurement unit dated money value currency
-/film/film/personal_appearances./film/personal_film_appearance/person	film film personal appearances. film personal film appearance person
-/music/instrument/family	music instrument family
-/sports/sports_team/roster./basketball/basketball_roster_position/position	sports sports team roster. basketball basketball roster position position
-/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location	base schemastaging organization extra phone number. base schemastaging phone sandbox service location
-/film/film/release_date_s./film/film_regional_release_date/film_release_region	film film release date s. film film regional release date film release region
-/award/award_category/disciplines_or_subjects	award award category disciplines or subjects
-/base/popstra/celebrity/friendship./base/popstra/friendship/participant	base popstra celebrity friendship. base popstra friendship participant
-/music/performance_role/regular_performances./music/group_membership/group	music performance role regular performances. music group membership group
-/film/film/edited_by	film film edited by
-/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club	base x2010fifaworldcupsouthafrica world cup squad current world cup squad. base x2010fifaworldcupsouthafrica current world cup squad current club
-/base/popstra/celebrity/canoodled./base/popstra/canoodled/participant	base popstra celebrity canoodled. base popstra canoodled participant
-/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium	film film release date s. film film regional release date film release distribution medium
-/film/film/other_crew./film/film_crew_gig/film_crew_role	film film other crew. film film crew gig film crew role
-/base/popstra/celebrity/breakup./base/popstra/breakup/participant	base popstra celebrity breakup. base popstra breakup participant
-/film/film/country	film film country
-/music/performance_role/regular_performances./music/group_membership/role	music performance role regular performances. music group membership role
-/sports/sports_team/roster./american_football/football_historical_roster_position/position_s	sports sports team roster. american football football historical roster position position s
-/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue	film film release date s. film film regional release date film regional debut venue
-/time/event/instance_of_recurring_event	time event instance of recurring event
-/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics	olympics olympic participating country athletes. olympics olympic athlete affiliation olympics
-/organization/endowed_organization/endowment./measurement_unit/dated_money_value/currency	organization endowed organization endowment. measurement unit dated money value currency
-/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation	travel travel destination how to get here. travel transportation mode of transportation
-/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season	baseball baseball team team stats. baseball baseball team stats season
-/award/award_category/winners./award/award_honor/ceremony	award award category winners. award award honor ceremony
-/government/legislative_session/members./government/government_position_held/district_represented	government legislative session members. government government position held district represented
-/influence/influence_node/influenced_by	influence influence node influenced by
-/base/culturalevent/event/entity_involved	base culturalevent event entity involved
-/people/ethnicity/people	people ethnicity people
-/sports/sport/pro_athletes./sports/pro_sports_played/athlete	sports sport pro athletes. sports pro sports played athlete
-/location/statistical_region/gdp_nominal_per_capita./measurement_unit/dated_money_value/currency	location statistical region gdp nominal per capita. measurement unit dated money value currency
-/location/hud_county_place/place	location hud county place place
-/base/aareas/schema/administrative_area/administrative_area_type	base aareas schema administrative area administrative area type
-/base/locations/continents/countries_within	base locations continents countries within
-/sports/sports_position/players./american_football/football_historical_roster_position/position_s	sports sports position players. american football football historical roster position position s
-/people/person/spouse_s./people/marriage/location_of_ceremony	people person spouse s. people marriage location of ceremony
-/education/educational_institution/students_graduates./education/education/major_field_of_study	education educational institution students graduates. education education major field of study
-/film/film/written_by	film film written by
-/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country	olympics olympic sport athletes. olympics olympic athlete affiliation country
-/music/performance_role/guest_performances./music/recording_contribution/performance_role	music performance role guest performances. music recording contribution performance role
-/film/film/featured_film_locations	film film featured film locations
-/education/educational_institution_campus/educational_institution	education educational institution campus educational institution
-/sports/pro_athlete/teams./sports/sports_team_roster/team	sports pro athlete teams. sports sports team roster team
-/people/ethnicity/languages_spoken	people ethnicity languages spoken
-/film/film/executive_produced_by	film film executive produced by
-/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type	tv tv producer programs produced. tv tv producer term producer type
-/location/location/contains	location location contains
-/base/biblioness/bibs_location/country	base biblioness bibs location country
-/user/ktrueman/default_domain/international_organization/member_states	user ktrueman default domain international organization member states
-/music/performance_role/track_performances./music/track_contribution/role	music performance role track performances. music track contribution role
-/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal	olympics olympic games medals awarded. olympics olympic medal honor medal
-/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members	base saturdaynightlive snl cast member seasons. base saturdaynightlive snl season tenure cast members

dataset/FB15k-237/relations.txt

@@ -1,237 +0,0 @@
-/soccer/football_team/current_roster./soccer/football_roster_position/position
-/music/artist/origin
-/ice_hockey/hockey_team/current_roster./sports/sports_team_roster/position
-/food/food/nutrients./food/nutrition_fact/nutrient
-/film/actor/film./film/performance/film
-/award/award_nominee/award_nominations./award/award_nomination/nominated_for
-/government/political_party/politicians_in_this_party./government/political_party_tenure/politician
-/base/schemastaging/person_extra/net_worth./measurement_unit/dated_money_value/currency
-/people/deceased_person/place_of_death
-/people/person/profession
-/location/administrative_division/first_level_division_of
-/base/marchmadness/ncaa_basketball_tournament/seeds./base/marchmadness/ncaa_tournament_seed/team
-/education/university/international_tuition./measurement_unit/dated_money_value/currency
-/location/us_county/county_seat
-/location/location/partially_contains
-/tv/tv_program/program_creator
-/film/film/music
-/tv/tv_program/languages
-/common/topic/webpage./common/webpage/category
-/user/tsegaran/random/taxonomy_subject/entry./user/tsegaran/random/taxonomy_entry/taxonomy
-/education/field_of_study/students_majoring./education/education/major_field_of_study
-/business/business_operation/assets./measurement_unit/dated_money_value/currency
-/film/film_set_designer/film_sets_designed
-/dataworld/gardening_hint/split_to
-/people/person/languages
-/business/job_title/people_with_this_title./business/employment_tenure/company
-/location/country/form_of_government
-/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_language
-/people/person/place_of_birth
-/sports/sports_team/colors
-/education/educational_institution/school_type
-/award/award_category/winners./award/award_honor/award_winner
-/organization/organization/headquarters./location/mailing_address/citytown
-/education/educational_degree/people_with_this_degree./education/education/student
-/government/legislative_session/members./government/government_position_held/legislative_sessions
-/film/film/distributors./film/film_film_distributor_relationship/film_distribution_medium
-/education/educational_degree/people_with_this_degree./education/education/major_field_of_study
-/location/hud_county_place/county
-/location/administrative_division/country
-/film/film/film_production_design_by
-/award/award_winning_work/awards_won./award/award_honor/award
-/organization/organization/headquarters./location/mailing_address/state_province_region
-/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/contact_category
-/tv/tv_program/country_of_origin
-/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/medal
-/location/country/second_level_divisions
-/award/award_ceremony/awards_presented./award/award_honor/honored_for
-/organization/organization_member/member_of./organization/organization_membership/organization
-/education/educational_institution/campuses
-/music/artist/contribution./music/recording_contribution/performance_role
-/award/ranked_item/appears_in_ranked_lists./award/ranking/list
-/people/person/religion
-/travel/travel_destination/climate./travel/travel_destination_monthly_climate/month
-/film/special_film_performance_type/film_performance_type./film/performance/film
-/award/award_nominee/award_nominations./award/award_nomination/award
-/location/statistical_region/religions./location/religion_percentage/religion
-/sports/sports_league_draft/picks./sports/sports_league_draft_pick/school
-/film/film/distributors./film/film_film_distributor_relationship/region
-/government/politician/government_positions_held./government/government_position_held/legislative_sessions
-/organization/role/leaders./organization/leadership/organization
-/tv/tv_network/programs./tv/tv_network_duration/program
-/soccer/football_team/current_roster./sports/sports_team_roster/position
-/music/instrument/instrumentalists
-/business/business_operation/operating_income./measurement_unit/dated_money_value/currency
-/people/cause_of_death/people
-/film/film/film_art_direction_by
-/people/person/sibling_s./people/sibling_relationship/sibling
-/film/film/cinematography
-/film/actor/dubbing_performances./film/dubbing_performance/language
-/base/biblioness/bibs_location/state
-/base/petbreeds/city_with_dogs/top_breeds./base/petbreeds/dog_city_relationship/dog_breed
-/people/person/gender
-/education/field_of_study/students_majoring./education/education/student
-/base/popstra/celebrity/dated./base/popstra/dated/participant
-/sports/sports_team/roster./american_football/football_roster_position/position
-/award/award_winner/awards_won./award/award_honor/award_winner
-/olympics/olympic_participating_country/medals_won./olympics/olympic_medal_honor/olympics
-/film/director/film
-/tv/tv_producer/programs_produced./tv/tv_producer_term/program
-/film/film_distributor/films_distributed./film/film_film_distributor_relationship/film
-/olympics/olympic_games/sports
-/music/record_label/artist
-/education/university/local_tuition./measurement_unit/dated_money_value/currency
-/film/film/story_by
-/people/person/spouse_s./people/marriage/spouse
-/sports/sports_league/teams./sports/sports_league_participation/team
-/people/profession/specialization_of
-/base/americancomedy/celebrity_impressionist/celebrities_impersonated
-/tv/tv_program/genre
-/award/award_category/nominees./award/award_nomination/nominated_for
-/language/human_language/countries_spoken_in
-/organization/organization/headquarters./location/mailing_address/country
-/location/statistical_region/gdp_real./measurement_unit/adjusted_money_value/adjustment_currency
-/education/university/fraternities_and_sororities
-/award/award_nominee/award_nominations./award/award_nomination/award_nominee
-/military/military_combatant/military_conflicts./military/military_combatant_group/combatants
-/award/award_nominated_work/award_nominations./award/award_nomination/nominated_for
-/location/location/time_zones
-/film/film/dubbing_performances./film/dubbing_performance/actor
-/film/film_subject/films
-/education/educational_degree/people_with_this_degree./education/education/institution
-/education/educational_institution/colors
-/award/award_category/category_of
-/tv/tv_personality/tv_regular_appearances./tv/tv_regular_personal_appearance/program
-/film/film/language
-/music/group_member/membership./music/group_membership/group
-/business/business_operation/revenue./measurement_unit/dated_money_value/currency
-/film/film/film_festivals
-/film/actor/film./film/performance/special_performance_type
-/organization/non_profit_organization/registered_with./organization/non_profit_registration/registering_agency
-/government/politician/government_positions_held./government/government_position_held/jurisdiction_of_office
-/base/aareas/schema/administrative_area/administrative_parent
-/award/award_winning_work/awards_won./award/award_honor/award_winner
-/organization/organization/place_founded
-/soccer/football_player/current_team./sports/sports_team_roster/team
-/government/politician/government_positions_held./government/government_position_held/basic_title
-/music/artist/track_contributions./music/track_contribution/role
-/base/localfood/seasonal_month/produce_available./base/localfood/produce_availability/seasonal_months
-/celebrities/celebrity/celebrity_friends./celebrities/friendship/friend
-/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/school
-/award/hall_of_fame/inductees./award/hall_of_fame_induction/inductee
-/influence/influence_node/peers./influence/peer_relationship/peers
-/medicine/disease/risk_factors
-/broadcast/content/artist
-/film/film/estimated_budget./measurement_unit/dated_money_value/currency
-/military/military_conflict/combatants./military/military_combatant_group/combatants
-/location/capital_of_administrative_division/capital_of./location/administrative_division_capital_relationship/administrative_division
-/tv/tv_program/regular_cast./tv/regular_tv_appearance/actor
-/people/deceased_person/place_of_burial
-/location/location/adjoin_s./location/adjoining_relationship/adjoins
-/music/group_member/membership./music/group_membership/role
-/award/award_ceremony/awards_presented./award/award_honor/award_winner
-/film/film/prequel
-/film/film/produced_by
-/tv/tv_program/tv_producer./tv/tv_producer_term/producer_type
-/sports/sports_position/players./sports/sports_team_roster/team
-/olympics/olympic_games/participating_countries
-/music/genre/parent_genre
-/tv/tv_writer/tv_programs./tv/tv_program_writer_relationship/tv_program
-/music/genre/artists
-/film/film/genre
-/people/person/employment_history./business/employment_tenure/company
-/education/university/domestic_tuition./measurement_unit/dated_money_value/currency
-/people/person/nationality
-/location/country/capital
-/location/statistical_region/gni_per_capita_in_ppp_dollars./measurement_unit/dated_money_value/currency
-/base/aareas/schema/administrative_area/capital
-/business/business_operation/industry
-/location/hud_foreclosure_area/estimated_number_of_mortgages./measurement_unit/dated_integer/source
-/film/film/other_crew./film/film_crew_gig/crewmember
-/base/popstra/location/vacationers./base/popstra/vacation_choice/vacationer
-/film/film/film_format
-/medicine/disease/notable_people_with_this_condition
-/film/film/costume_design_by
-/government/government_office_category/officeholders./government/government_position_held/jurisdiction_of_office
-/location/statistical_region/gdp_nominal./measurement_unit/dated_money_value/currency
-/sports/sports_team/roster./baseball/baseball_roster_position/position
-/award/award_winning_work/awards_won./award/award_honor/honored_for
-/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/olympics
-/celebrities/celebrity/sexual_relationships./celebrities/romantic_relationship/celebrity
-/people/marriage_union_type/unions_of_this_type./people/marriage/location_of_ceremony
-/organization/organization/child./organization/organization_relationship/child
-/organization/organization_founder/organizations_founded
-/sports/sports_team/sport
-/people/ethnicity/geographic_distribution
-/location/statistical_region/places_exported_to./location/imports_and_exports/exported_to
-/location/country/official_language
-/film/film/production_companies
-/user/jg/default_domain/olympic_games/sports
-/time/event/locations
-/people/person/spouse_s./people/marriage/type_of_union
-/government/governmental_body/members./government/government_position_held/legislative_sessions
-/media_common/netflix_genre/titles
-/user/alexander/philosophy/philosopher/interests
-/film/film/runtime./film/film_cut/film_release_region
-/education/educational_institution/students_graduates./education/education/student
-/base/eating/practicer_of_diet/diet
-/tv/non_character_role/tv_regular_personal_appearances./tv/tv_regular_personal_appearance/person
-/sports/sports_position/players./sports/sports_team_roster/position
-/sports/professional_sports_team/draft_picks./sports/sports_league_draft_pick/draft
-/medicine/symptom/symptom_of
-/film/person_or_entity_appearing_in_film/films./film/personal_film_appearance/type_of_appearance
-/sports/sports_team_location/teams
-/american_football/football_team/current_roster./sports/sports_team_roster/position
-/people/person/places_lived./people/place_lived/location
-/location/statistical_region/rent50_2./measurement_unit/dated_money_value/currency
-/film/film/personal_appearances./film/personal_film_appearance/person
-/music/instrument/family
-/sports/sports_team/roster./basketball/basketball_roster_position/position
-/base/schemastaging/organization_extra/phone_number./base/schemastaging/phone_sandbox/service_location
-/film/film/release_date_s./film/film_regional_release_date/film_release_region
-/award/award_category/disciplines_or_subjects
-/base/popstra/celebrity/friendship./base/popstra/friendship/participant
-/music/performance_role/regular_performances./music/group_membership/group
-/film/film/edited_by
-/base/x2010fifaworldcupsouthafrica/world_cup_squad/current_world_cup_squad./base/x2010fifaworldcupsouthafrica/current_world_cup_squad/current_club
-/base/popstra/celebrity/canoodled./base/popstra/canoodled/participant
-/film/film/release_date_s./film/film_regional_release_date/film_release_distribution_medium
-/film/film/other_crew./film/film_crew_gig/film_crew_role
-/base/popstra/celebrity/breakup./base/popstra/breakup/participant
-/film/film/country
-/music/performance_role/regular_performances./music/group_membership/role
-/sports/sports_team/roster./american_football/football_historical_roster_position/position_s
-/film/film/release_date_s./film/film_regional_release_date/film_regional_debut_venue
-/time/event/instance_of_recurring_event
-/olympics/olympic_participating_country/athletes./olympics/olympic_athlete_affiliation/olympics
-/organization/endowed_organization/endowment./measurement_unit/dated_money_value/currency
-/travel/travel_destination/how_to_get_here./travel/transportation/mode_of_transportation
-/baseball/baseball_team/team_stats./baseball/baseball_team_stats/season
-/award/award_category/winners./award/award_honor/ceremony
-/government/legislative_session/members./government/government_position_held/district_represented
-/influence/influence_node/influenced_by
-/base/culturalevent/event/entity_involved
-/people/ethnicity/people
-/sports/sport/pro_athletes./sports/pro_sports_played/athlete
-/location/statistical_region/gdp_nominal_per_capita./measurement_unit/dated_money_value/currency
-/location/hud_county_place/place
-/base/aareas/schema/administrative_area/administrative_area_type
-/base/locations/continents/countries_within
-/sports/sports_position/players./american_football/football_historical_roster_position/position_s
-/people/person/spouse_s./people/marriage/location_of_ceremony
-/education/educational_institution/students_graduates./education/education/major_field_of_study
-/film/film/written_by
-/olympics/olympic_sport/athletes./olympics/olympic_athlete_affiliation/country
-/music/performance_role/guest_performances./music/recording_contribution/performance_role
-/film/film/featured_film_locations
-/education/educational_institution_campus/educational_institution
-/sports/pro_athlete/teams./sports/sports_team_roster/team
-/people/ethnicity/languages_spoken
-/film/film/executive_produced_by
-/tv/tv_producer/programs_produced./tv/tv_producer_term/producer_type
-/location/location/contains
-/base/biblioness/bibs_location/country
-/user/ktrueman/default_domain/international_organization/member_states
-/music/performance_role/track_performances./music/track_contribution/role
-/olympics/olympic_games/medals_awarded./olympics/olympic_medal_honor/medal
-/base/saturdaynightlive/snl_cast_member/seasons./base/saturdaynightlive/snl_season_tenure/cast_members

dataset/WN18RR/get_neighbor.ipynb

@@ -1,155 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path1 = './entities.txt'\n",
-    "path2 = './relations.txt'\n",
-    "path3 = './train.tsv'\n",
-    "path4 = './dev.tsv'\n",
-    "path5 = './test.tsv'\n",
-    "path6 = './get_neighbor/entity2id.txt'\n",
-    "path7 = './get_neighbor/relation2id.txt'\n",
-    "path8 = './get_neighbor/train2id.txt'\n",
-    "path9 = './get_neighbor/valid2id.txt'\n",
-    "path10 = './get_neighbor/test2id.txt'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path1, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "cnt = 0\n",
-    "with open(path6, 'w') as f:\n",
-    "    for line in a:\n",
-    "        en = line.strip()\n",
-    "        f.write(en + '\\t' + str(cnt) + '\\n')\n",
-    "        cnt += 1\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path2, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "cnt = 0\n",
-    "with open(path7, 'w') as f:\n",
-    "    for line in a:\n",
-    "        re = line.strip()\n",
-    "        f.write(re + '\\t' + str(cnt) + '\\n')\n",
-    "        cnt += 1\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path6, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "en2id = {}\n",
-    "for line in a:\n",
-    "    b = line.strip().split('\\t')\n",
-    "    en, num = b[0], b[1]\n",
-    "    en2id[en] = num"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path7, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "re2id = {}\n",
-    "for line in a:\n",
-    "    b = line.strip().split('\\t')\n",
-    "    re, num = b[0], b[1]\n",
-    "    re2id[re] = num"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path3, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "with open(path8, 'w') as f:\n",
-    "    for line in a:\n",
-    "        b = line.strip().split('\\t')\n",
-    "        h, r, t = b[0], b[1], b[2]\n",
-    "        f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path4, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "with open(path9, 'w') as f:\n",
-    "    for line in a:\n",
-    "        b = line.strip().split('\\t')\n",
-    "        h, r, t = b[0], b[1], b[2]\n",
-    "        f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path5, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "with open(path10, 'w') as f:\n",
-    "    for line in a:\n",
-    "        b = line.strip().split('\\t')\n",
-    "        h, r, t = b[0], b[1], b[2]\n",
-    "        f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
-    "    "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [default]",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

dataset/WN18RR/get_neighbor/relation2id.txt

@@ -1,11 +0,0 @@
-_member_of_domain_usage	0
-_has_part	1
-_also_see	2
-_hypernym	3
-_synset_domain_topic_of	4
-_derivationally_related_form	5
-_similar_to	6
-_instance_hypernym	7
-_verb_group	8
-_member_meronym	9
-_member_of_domain_region	10

dataset/WN18RR/relation2text.txt

@@ -1,11 +0,0 @@
-_member_of_domain_usage	member of domain usage
-_has_part	has part
-_also_see	also see
-_hypernym	hypernym
-_synset_domain_topic_of	synset domain topic of
-_derivationally_related_form	derivationally related form
-_similar_to	similar to
-_instance_hypernym	instance hypernym
-_verb_group	verb group
-_member_meronym	member meronym
-_member_of_domain_region	member of domain region

dataset/WN18RR/relations.txt

@@ -1,11 +0,0 @@
-_member_of_domain_usage
-_has_part
-_also_see
-_hypernym
-_synset_domain_topic_of
-_derivationally_related_form
-_similar_to
-_instance_hypernym
-_verb_group
-_member_meronym
-_member_of_domain_region

dataset/create_neighbor.py

@@ -1,151 +0,0 @@
-from collections import defaultdict
-import time
-import argparse
-id2entity_name = defaultdict(str)
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--dataset", type=str, default=None)
-args = parser.parse_args()
-
-# dataset_name = 'FB15k-237'
-
-with open('./' + args.dataset + '/get_neighbor/entity2id.txt', 'r') as file:
-    entity_lines = file.readlines()
-    for line in entity_lines:
-        _name, _id = line.strip().split("\t")
-        id2entity_name[int(_id)] = _name
-
-id2relation_name = defaultdict(str)
-
-with open('./' + args.dataset + '/get_neighbor/relation2id.txt', 'r') as file:
-    relation_lines = file.readlines()
-    for line in relation_lines:
-        _name, _id = line.strip().split("\t")
-        id2relation_name[int(_id)] = _name
-
-train_triplet = []
-
-
-for line in open('./' + args.dataset + '/get_neighbor/train2id.txt', 'r'):
-    head, relation, tail = line.strip('\n').split()
-    train_triplet.append(list((int(head), int(relation), int(tail))))
-
-for line in open('./' + args.dataset + '/get_neighbor/test2id.txt', 'r'):
-    head, relation, tail = line.strip('\n').split()
-    train_triplet.append(list((int(head), int(relation), int(tail))))
-
-for line in open('./'+args.dataset+'/get_neighbor/valid2id.txt', 'r'):
-    head, relation, tail = line.strip('\n').split()
-    train_triplet.append(list((int(head), int(relation), int(tail))))
-
-
-graph = {}
-reverse_graph = {}
-
-def init_graph(graph_triplet):
-
-        for triple in graph_triplet:
-            head = triple[0]
-            rela = triple[1]
-            tail = triple[2]
-
-            if(head not in graph.keys()):
-                graph[head] = {}
-                graph[head][tail] = rela
-            else:
-                graph[head][tail] = rela
-
-            if(tail not in reverse_graph.keys()):
-                reverse_graph[tail] = {}
-                reverse_graph[tail][head] = rela
-            else:
-                reverse_graph[tail][head] = rela
-        
-        # return graph, reverse_graph, node_indegree, node_outdegree
-
-init_graph(train_triplet)
-
-
-
-import random
-
-def random_delete(triplet, reserved_num): 
-    reserved = random.sample(triplet, reserved_num)
-    return reserved
-
-def get_onestep_neighbors(graph, source, sample_num):
-    triplet = []
-    try:
-        nei = list(graph[source].keys())
-        # nei = random.sample(graph[source].keys(), sample_num)
-        triplet = [tuple((source, graph[source][nei[i]], nei[i])) for i in range(len(nei))]
-    except KeyError:
-        pass
-    except ValueError:
-        nei = list(graph[source].keys())
-        triplet = [tuple((source, graph[source][nei[i]], nei[i])) for i in range(len(nei))]
-    return triplet
-
-def get_entity_neighbors(traget_entity, max_triplet):
-
-    as_head_neighbors = get_onestep_neighbors(graph, traget_entity, max_triplet // 2)
-    as_tail_neighbors = get_onestep_neighbors(reverse_graph, traget_entity, max_triplet // 2)
-
-    all_triplet = as_head_neighbors + as_tail_neighbors
-
-    return all_triplet
-
-def get_triplet(triplet):
-    head_entity = triplet[0]
-    tail_entity = triplet[2]
-    triplet = tuple((triplet[0], triplet[1], triplet[2]))
-
-    head_triplet = get_entity_neighbors(head_entity, 4)
-    tail_triplet = get_entity_neighbors(tail_entity, 4)
-
-    temp_triplet = list(set(head_triplet + tail_triplet))
-    temp_triplet = list(set(temp_triplet) - set([triplet]))
-    # if len(temp_triplet) > 8:
-    #     del_triplet = list(set(temp_triplet) - set([triplet]))
-        # temp_triplet = random_delete(del_triplet, 7)
-
-    return temp_triplet
-
-
-
-import copy
-
-def change_(triplet_list):
-    tri_text = []
-    for item in triplet_list:
-        # text = id2entity_name[item[0]] + '\t' + id2relation_name[item[1]] + '\t' + id2entity_name[item[2]]
-        h = id2entity_name[item[0]]
-        r = id2relation_name[item[1]]
-        t = id2entity_name[item[2]]
-        tri_text.append([h, r, t])
-    return tri_text
-
-mask_idx = 99999999
-masked_tail_neighbor = defaultdict(list)
-masked_head_neighbor = defaultdict(list)
-for triplet in train_triplet:
-    tail_masked = copy.deepcopy(triplet)
-    head_masked = copy.deepcopy(triplet)
-    tail_masked[2] = mask_idx
-    head_masked[0] = mask_idx
-    masked_tail_neighbor['\t'.join([id2entity_name[triplet[0]], id2relation_name[triplet[1]]])] = change_(get_triplet(tail_masked))
-    masked_head_neighbor['\t'.join([id2entity_name[triplet[2]], id2relation_name[triplet[1]]])] = change_(get_triplet(head_masked))
-
-
-import json
-
-with open("./" + args.dataset + "/masked_tail_neighbor.txt", "w") as file:
-    file.write(json.dumps(masked_tail_neighbor, indent=1))
-
-with open("./" + args.dataset + "/masked_head_neighbor.txt", "w") as file:
-    file.write(json.dumps(masked_head_neighbor, indent=1))
-
-
-
-
-

dataset/umls/entities.txt

@@ -1,135 +0,0 @@
-idea_or_concept
-virus
-spatial_concept
-human_caused_phenomenon_or_process
-human
-organ_or_tissue_function
-daily_or_recreational_activity
-steroid
-biomedical_or_dental_material
-vertebrate
-immunologic_factor
-inorganic_chemical
-invertebrate
-embryonic_structure
-functional_concept
-amino_acid_peptide_or_protein
-fish
-reptile
-physical_object
-disease_or_syndrome
-biologically_active_substance
-physiologic_function
-population_group
-group
-body_space_or_junction
-bird
-qualitative_concept
-bacterium
-cell_function
-enzyme
-organophosphorus_compound
-nucleic_acid_nucleoside_or_nucleotide
-cell
-language
-antibiotic
-indicator_reagent_or_diagnostic_aid
-fungus
-chemical_viewed_functionally
-rickettsia_or_chlamydia
-patient_or_disabled_group
-professional_society
-health_care_related_organization
-clinical_attribute
-biomedical_occupation_or_discipline
-temporal_concept
-phenomenon_or_process
-family_group
-chemical_viewed_structurally
-regulation_or_law
-acquired_abnormality
-experimental_model_of_disease
-professional_or_occupational_group
-injury_or_poisoning
-receptor
-drug_delivery_device
-hazardous_or_poisonous_substance
-organism
-neoplastic_process
-mammal
-molecular_function
-lipid
-group_attribute
-nucleotide_sequence
-biologic_function
-chemical
-cell_component
-intellectual_product
-manufactured_object
-classification
-geographic_area
-vitamin
-gene_or_genome
-self_help_or_relief_organization
-pathologic_function
-amphibian
-laboratory_or_test_result
-organism_attribute
-cell_or_molecular_dysfunction
-therapeutic_or_preventive_procedure
-sign_or_symptom
-occupational_activity
-anatomical_abnormality
-hormone
-fully_formed_anatomical_structure
-educational_activity
-quantitative_concept
-tissue
-organism_function
-social_behavior
-mental_or_behavioral_dysfunction
-governmental_or_regulatory_activity
-molecular_biology_research_technique
-occupation_or_discipline
-conceptual_entity
-body_location_or_region
-pharmacologic_substance
-clinical_drug
-food
-substance
-genetic_function
-congenital_abnormality
-medical_device
-carbohydrate
-health_care_activity
-eicosanoid
-element_ion_or_isotope
-diagnostic_procedure
-entity
-event
-laboratory_procedure
-environmental_effect_of_humans
-body_part_organ_or_organ_component
-molecular_sequence
-mental_process
-research_device
-alga
-natural_phenomenon_or_process
-anatomical_structure
-animal
-body_system
-behavior
-carbohydrate_sequence
-archaeon
-research_activity
-organization
-individual_behavior
-organic_chemical
-finding
-age_group
-activity
-machine_activity
-plant
-body_substance
-amino_acid_sequence
-neuroreactive_substance_or_biogenic_amine

dataset/umls/entity2text.txt

@@ -1,135 +0,0 @@
-idea_or_concept	idea or concept
-virus	virus
-spatial_concept	spatial concept
-human_caused_phenomenon_or_process	human caused phenomenon or process
-human	human
-organ_or_tissue_function	organ or tissue function
-daily_or_recreational_activity	daily or recreational activity
-steroid	steroid
-biomedical_or_dental_material	biomedical or dental material
-vertebrate	vertebrate
-immunologic_factor	immunologic factor
-inorganic_chemical	inorganic chemical
-invertebrate	invertebrate
-embryonic_structure	embryonic structure
-functional_concept	functional concept
-amino_acid_peptide_or_protein	amino acid peptide or protein
-fish	fish
-reptile	reptile
-physical_object	physical object
-disease_or_syndrome	disease or syndrome
-biologically_active_substance	biologically active substance
-physiologic_function	physiologic function
-population_group	population group
-group	group
-body_space_or_junction	body space or junction
-bird	bird
-qualitative_concept	qualitative concept
-bacterium	bacterium
-cell_function	cell function
-enzyme	enzyme
-organophosphorus_compound	organophosphorus compound
-nucleic_acid_nucleoside_or_nucleotide	nucleic acid nucleoside or nucleotide
-cell	cell
-language	language
-antibiotic	antibiotic
-indicator_reagent_or_diagnostic_aid	indicator reagent or diagnostic aid
-fungus	fungus
-chemical_viewed_functionally	chemical viewed functionally
-rickettsia_or_chlamydia	rickettsia or chlamydia
-patient_or_disabled_group	patient or disabled group
-professional_society	professional society
-health_care_related_organization	health care related organization
-clinical_attribute	clinical attribute
-biomedical_occupation_or_discipline	biomedical occupation or discipline
-temporal_concept	temporal concept
-phenomenon_or_process	phenomenon or process
-family_group	family group
-chemical_viewed_structurally	chemical viewed structurally
-regulation_or_law	regulation or law
-acquired_abnormality	acquired abnormality
-experimental_model_of_disease	experimental model of disease
-professional_or_occupational_group	professional or occupational group
-injury_or_poisoning	injury or poisoning
-receptor	receptor
-drug_delivery_device	drug delivery device
-hazardous_or_poisonous_substance	hazardous or poisonous substance
-organism	organism
-neoplastic_process	neoplastic process
-mammal	mammal
-molecular_function	molecular function
-lipid	lipid
-group_attribute	group attribute
-nucleotide_sequence	nucleotide sequence
-biologic_function	biologic function
-chemical	chemical
-cell_component	cell component
-intellectual_product	intellectual product
-manufactured_object	manufactured object
-classification	classification
-geographic_area	geographic area
-vitamin	vitamin
-gene_or_genome	gene or genome
-self_help_or_relief_organization	self help or relief organization
-pathologic_function	pathologic function
-amphibian	amphibian
-laboratory_or_test_result	laboratory or test result
-organism_attribute	organism attribute
-cell_or_molecular_dysfunction	cell or molecular dysfunction
-therapeutic_or_preventive_procedure	therapeutic or preventive procedure
-sign_or_symptom	sign or symptom
-occupational_activity	occupational activity
-anatomical_abnormality	anatomical abnormality
-hormone	hormone
-fully_formed_anatomical_structure	fully formed anatomical structure
-educational_activity	educational activity
-quantitative_concept	quantitative concept
-tissue	tissue
-organism_function	organism function
-social_behavior	social behavior
-mental_or_behavioral_dysfunction	mental or behavioral dysfunction
-governmental_or_regulatory_activity	governmental or regulatory activity
-molecular_biology_research_technique	molecular biology research technique
-occupation_or_discipline	occupation or discipline
-conceptual_entity	conceptual entity
-body_location_or_region	body location or region
-pharmacologic_substance	pharmacologic substance
-clinical_drug	clinical drug
-food	food
-substance	substance
-genetic_function	genetic function
-congenital_abnormality	congenital abnormality
-medical_device	medical device
-carbohydrate	carbohydrate
-health_care_activity	health care activity
-eicosanoid	eicosanoid
-element_ion_or_isotope	element ion or isotope
-diagnostic_procedure	diagnostic procedure
-entity	entity
-event	event
-laboratory_procedure	laboratory procedure
-environmental_effect_of_humans	environmental effect of humans
-body_part_organ_or_organ_component	body part organ or organ component
-molecular_sequence	molecular sequence
-mental_process	mental process
-research_device	research device
-alga	alga
-natural_phenomenon_or_process	natural phenomenon or process
-anatomical_structure	anatomical structure
-animal	animal
-body_system	body system
-behavior	behavior
-carbohydrate_sequence	carbohydrate sequence
-archaeon	archaeon
-research_activity	research activity
-organization	organization
-individual_behavior	individual behavior
-organic_chemical	organic chemical
-finding	finding
-age_group	age group
-activity	activity
-machine_activity	machine activity
-plant	plant
-body_substance	body substance
-amino_acid_sequence	amino acid sequence
-neuroreactive_substance_or_biogenic_amine	neuroreactive substance or biogenic amine

dataset/umls/get_neighbor.ipynb

@@ -1,155 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "path1 = './entities.txt'\n",
-    "path2 = './relations.txt'\n",
-    "path3 = './train.tsv'\n",
-    "path4 = './dev.tsv'\n",
-    "path5 = './test.tsv'\n",
-    "path6 = './get_neighbor/entity2id.txt'\n",
-    "path7 = './get_neighbor/relation2id.txt'\n",
-    "path8 = './get_neighbor/train2id.txt'\n",
-    "path9 = './get_neighbor/valid2id.txt'\n",
-    "path10 = './get_neighbor/test2id.txt'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path1, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "cnt = 0\n",
-    "with open(path6, 'w') as f:\n",
-    "    for line in a:\n",
-    "        en = line.strip()\n",
-    "        f.write(en + '\\t' + str(cnt) + '\\n')\n",
-    "        cnt += 1\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path2, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "cnt = 0\n",
-    "with open(path7, 'w') as f:\n",
-    "    for line in a:\n",
-    "        re = line.strip()\n",
-    "        f.write(re + '\\t' + str(cnt) + '\\n')\n",
-    "        cnt += 1\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path6, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "en2id = {}\n",
-    "for line in a:\n",
-    "    b = line.strip().split('\\t')\n",
-    "    en, num = b[0], b[1]\n",
-    "    en2id[en] = num"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path7, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "re2id = {}\n",
-    "for line in a:\n",
-    "    b = line.strip().split('\\t')\n",
-    "    re, num = b[0], b[1]\n",
-    "    re2id[re] = num"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path3, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "with open(path8, 'w') as f:\n",
-    "    for line in a:\n",
-    "        b = line.strip().split('\\t')\n",
-    "        h, r, t = b[0], b[1], b[2]\n",
-    "        f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path4, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "with open(path9, 'w') as f:\n",
-    "    for line in a:\n",
-    "        b = line.strip().split('\\t')\n",
-    "        h, r, t = b[0], b[1], b[2]\n",
-    "        f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with open(path5, 'r') as f:\n",
-    "    a = f.readlines()\n",
-    "with open(path10, 'w') as f:\n",
-    "    for line in a:\n",
-    "        b = line.strip().split('\\t')\n",
-    "        h, r, t = b[0], b[1], b[2]\n",
-    "        f.write(en2id[h] + ' ' + re2id[r] + ' ' + en2id[t] + '\\n')\n",
-    "    "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [default]",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

dataset/umls/get_neighbor/entity2id.txt

@@ -1,135 +0,0 @@
-idea_or_concept	0
-virus	1
-spatial_concept	2
-human_caused_phenomenon_or_process	3
-human	4
-organ_or_tissue_function	5
-daily_or_recreational_activity	6
-steroid	7
-biomedical_or_dental_material	8
-vertebrate	9
-immunologic_factor	10
-inorganic_chemical	11
-invertebrate	12
-embryonic_structure	13
-functional_concept	14
-amino_acid_peptide_or_protein	15
-fish	16
-reptile	17
-physical_object	18
-disease_or_syndrome	19
-biologically_active_substance	20
-physiologic_function	21
-population_group	22
-group	23
-body_space_or_junction	24
-bird	25
-qualitative_concept	26
-bacterium	27
-cell_function	28
-enzyme	29
-organophosphorus_compound	30
-nucleic_acid_nucleoside_or_nucleotide	31
-cell	32
-language	33
-antibiotic	34
-indicator_reagent_or_diagnostic_aid	35
-fungus	36
-chemical_viewed_functionally	37
-rickettsia_or_chlamydia	38
-patient_or_disabled_group	39
-professional_society	40
-health_care_related_organization	41
-clinical_attribute	42
-biomedical_occupation_or_discipline	43
-temporal_concept	44
-phenomenon_or_process	45
-family_group	46
-chemical_viewed_structurally	47
-regulation_or_law	48
-acquired_abnormality	49
-experimental_model_of_disease	50
-professional_or_occupational_group	51
-injury_or_poisoning	52
-receptor	53
-drug_delivery_device	54
-hazardous_or_poisonous_substance	55
-organism	56
-neoplastic_process	57
-mammal	58
-molecular_function	59
-lipid	60
-group_attribute	61
-nucleotide_sequence	62
-biologic_function	63
-chemical	64
-cell_component	65
-intellectual_product	66
-manufactured_object	67
-classification	68
-geographic_area	69
-vitamin	70
-gene_or_genome	71
-self_help_or_relief_organization	72
-pathologic_function	73
-amphibian	74
-laboratory_or_test_result	75
-organism_attribute	76
-cell_or_molecular_dysfunction	77
-therapeutic_or_preventive_procedure	78
-sign_or_symptom	79
-occupational_activity	80
-anatomical_abnormality	81
-hormone	82
-fully_formed_anatomical_structure	83
-educational_activity	84
-quantitative_concept	85
-tissue	86
-organism_function	87
-social_behavior	88
-mental_or_behavioral_dysfunction	89
-governmental_or_regulatory_activity	90
-molecular_biology_research_technique	91
-occupation_or_discipline	92
-conceptual_entity	93
-body_location_or_region	94
-pharmacologic_substance	95
-clinical_drug	96
-food	97
-substance	98
-genetic_function	99
-congenital_abnormality	100
-medical_device	101
-carbohydrate	102
-health_care_activity	103
-eicosanoid	104
-element_ion_or_isotope	105
-diagnostic_procedure	106
-entity	107
-event	108
-laboratory_procedure	109
-environmental_effect_of_humans	110
-body_part_organ_or_organ_component	111
-molecular_sequence	112
-mental_process	113
-research_device	114
-alga	115
-natural_phenomenon_or_process	116
-anatomical_structure	117
-animal	118
-body_system	119
-behavior	120
-carbohydrate_sequence	121
-archaeon	122
-research_activity	123
-organization	124
-individual_behavior	125
-organic_chemical	126
-finding	127
-age_group	128
-activity	129
-machine_activity	130
-plant	131
-body_substance	132
-amino_acid_sequence	133
-neuroreactive_substance_or_biogenic_amine	134

dataset/umls/get_neighbor/relation2id.txt

@@ -1,46 +0,0 @@
-measures	0
-derivative_of	1
-disrupts	2
-prevents	3
-conceptually_related_to	4
-manifestation_of	5
-diagnoses	6
-evaluation_of	7
-contains	8
-co-occurs_with	9
-conceptual_part_of	10
-performs	11
-degree_of	12
-interacts_with	13
-uses	14
-issue_in	15
-assesses_effect_of	16
-property_of	17
-precedes	18
-result_of	19
-causes	20
-practices	21
-ingredient_of	22
-analyzes	23
-surrounds	24
-indicates	25
-associated_with	26
-affects	27
-location_of	28
-produces	29
-process_of	30
-measurement_of	31
-connected_to	32
-carries_out	33
-method_of	34
-adjacent_to	35
-occurs_in	36
-consists_of	37
-interconnects	38
-manages	39
-complicates	40
-part_of	41
-treats	42
-isa	43
-developmental_form_of	44
-exhibits	45

dataset/umls/get_neighbor/test2id.txt

@@ -1,661 +0,0 @@
-7 13 104
-42 43 93
-94 28 21
-57 43 19
-102 27 59
-19 27 5
-98 15 92
-75 7 99
-37 43 107
-30 22 96
-89 27 88
-3 19 73
-15 13 34
-82 27 50
-34 27 28
-13 41 25
-86 29 126
-99 30 5
-100 41 58
-11 20 77
-53 2 32
-40 28 109
-87 9 28
-10 20 77
-70 27 57
-34 40 81
-85 31 21
-73 27 63
-100 15 92
-86 35 24
-70 2 5
-53 43 98
-113 43 87
-9 45 125
-94 28 78
-18 15 43
-11 27 28
-57 27 9
-28 27 58
-22 14 101
-3 19 59
-95 42 57
-13 28 77
-50 19 109
-29 2 28
-73 40 52
-89 12 19
-131 13 118
-19 30 73
-73 19 106
-81 5 63
-75 5 73
-16 15 43
-102 27 77
-63 27 1
-113 18 28
-50 36 89
-100 41 27
-3 19 21
-32 28 78
-50 30 116
-57 19 45
-134 27 87
-89 30 122
-100 41 1
-73 5 99
-20 20 89
-95 42 19
-24 28 28
-99 27 1
-49 19 106
-21 27 122
-65 28 73
-91 0 37
-59 19 3
-51 14 67
-53 2 59
-5 30 63
-91 0 105
-21 19 50
-21 29 20
-83 41 74
-29 40 87
-86 28 1
-12 43 118
-113 9 99
-81 28 27
-83 28 77
-5 9 99
-21 19 3
-59 19 77
-106 26 73
-21 19 57
-60 13 95
-65 29 53
-21 19 100
-128 11 130
-100 26 76
-109 23 15
-70 27 113
-65 41 56
-68 43 93
-87 19 73
-87 29 70
-109 6 89
-102 27 73
-65 43 107
-73 27 56
-113 27 27
-75 25 5
-86 29 132
-65 41 111
-59 27 89
-102 13 10
-6 26 49
-59 19 19
-57 36 52
-83 41 122
-102 27 113
-105 13 55
-52 2 59
-51 13 22
-89 27 122
-126 27 57
-98 20 19
-20 20 19
-52 2 86
-73 15 92
-89 30 17
-49 5 73
-105 15 43
-120 26 128
-19 40 77
-83 29 53
-64 20 52
-89 27 17
-63 27 87
-34 13 134
-20 27 89
-109 16 99
-123 0 15
-19 27 77
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-19 36 89
-88 26 39
-34 6 89
-73 19 87
-131 13 17
-89 27 74
-79 6 89
-63 19 89
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-81 41 9
-46 13 23
-50 30 16
-89 27 116
-87 27 115
-65 28 24
-111 28 99
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-33 15 43
-111 28 5
-123 43 80
-106 23 55
-50 36 23
-31 43 107
-106 26 100
-80 26 77
-5 19 49
-59 27 28
-47 22 96
-89 19 120
-47 13 102
-28 27 57
-73 36 89
-46 11 78
-46 29 114
-15 27 113
-82 40 63
-81 19 103
-87 30 63
-10 40 19
-62 17 31
-124 28 80
-42 19 99
-70 20 81
-89 27 5
-57 19 89
-111 29 126
-1 43 56
-5 30 21
-125 26 92
-55 27 19
-77 27 21
-82 2 86
-59 27 118
-21 27 59
-63 27 21
-75 25 113
-47 27 5
-28 19 19
-15 27 63
-50 27 63
-8 27 63
-91 0 15
-50 27 27
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-4 43 58
-22 11 6
-65 10 119
-32 41 17
-5 27 4
-35 13 64
-77 19 87
-91 0 104
-91 0 116
-76 19 19
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-3 19 45
-34 27 99
-82 13 29
-73 30 122
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-61 17 46
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-83 41 131
-47 13 60
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-1 28 82
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-115 43 18
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-22 14 48
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-3 19 52
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-36 20 73
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-20 13 134
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-89 9 52
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-126 13 8
-21 27 116
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-57 40 81
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-42 5 5
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-11 13 29
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-5 9 59
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-20 2 71
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-28 19 21
-113 30 4
-64 15 43
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-110 19 49
-19 19 3
-126 13 7
-77 30 116
-81 41 118
-106 14 54
-91 34 106
-20 20 52
-81 27 131
-59 30 12
-106 0 95
-105 27 59
-89 19 57
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dataset/umls/get_neighbor/valid2id.txt

@@ -1,652 +0,0 @@
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dataset/umls/relation2text.txt

@@ -1,46 +0,0 @@
-measures	measures
-derivative_of	derivative of
-disrupts	disrupts
-prevents	prevents
-conceptually_related_to	conceptually related to
-manifestation_of	manifestation of
-diagnoses	diagnoses
-evaluation_of	evaluation of
-contains	contains
-co-occurs_with	co-occurs with
-conceptual_part_of	conceptual part of
-performs	performs
-degree_of	degree of
-interacts_with	interacts with
-uses	uses
-issue_in	issue in
-assesses_effect_of	assesses effect of
-property_of	property of
-precedes	precedes
-result_of	result of
-causes	causes
-practices	practices
-ingredient_of	ingredient of
-analyzes	analyzes
-surrounds	surrounds
-indicates	indicates
-associated_with	associated with
-affects	affects
-location_of	location of
-produces	produces
-process_of	process of
-measurement_of	measurement of
-connected_to	connected to
-carries_out	carries out
-method_of	method of
-adjacent_to	adjacent to
-occurs_in	occurs in
-consists_of	consists of
-interconnects	interconnects
-manages	manages
-complicates	complicates
-part_of	part of
-treats	treats
-isa	is a
-developmental_form_of	developmental form of
-exhibits	exhibits

dataset/umls/relations.txt

@@ -1,46 +0,0 @@
-measures
-derivative_of
-disrupts
-prevents
-conceptually_related_to
-manifestation_of
-diagnoses
-evaluation_of
-contains
-co-occurs_with
-conceptual_part_of
-performs
-degree_of
-interacts_with
-uses
-issue_in
-assesses_effect_of
-property_of
-precedes
-result_of
-causes
-practices
-ingredient_of
-analyzes
-surrounds
-indicates
-associated_with
-affects
-location_of
-produces
-process_of
-measurement_of
-connected_to
-carries_out
-method_of
-adjacent_to
-occurs_in
-consists_of
-interconnects
-manages
-complicates
-part_of
-treats
-isa
-developmental_form_of
-exhibits

layers.py

@@ -0,0 +1,22 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+class PreNormResidual(torch.nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.fn = fn
+        self.norm = torch.nn.LayerNorm(dim)
+
+    def forward(self, x):
+        return self.fn(self.norm(x)) + x
+
+def FeedForward(dim, expansion_factor=4, dropout=0., dense=torch.nn.Linear):
+    inner_dim = int(dim * expansion_factor)
+    return torch.nn.Sequential(
+        dense(dim, inner_dim),
+        torch.nn.GELU(),
+        torch.nn.Dropout(dropout*2),
+        dense(inner_dim, dim),
+        torch.nn.Dropout(dropout)
+    )

lit_models/__init__.py

@@ -1,2 +0,0 @@
-from .transformer import *
-from .base import *

lit_models/base.py

@@ -1,97 +0,0 @@
-import argparse
-import pytorch_lightning as pl
-import torch
-from typing import Dict, Any
-
-
-OPTIMIZER = "AdamW"
-LR = 5e-5
-LOSS = "cross_entropy"
-ONE_CYCLE_TOTAL_STEPS = 100
-
-class Config(dict):
-    def __getattr__(self, name):
-        return self.get(name)
-
-    def __setattr__(self, name, val):
-        self[name] = val
-
-
-class BaseLitModel(pl.LightningModule):
-    """
-    Generic PyTorch-Lightning class that must be initialized with a PyTorch module.
-    """
-
-    def __init__(self, model, args: argparse.Namespace = None):
-        super().__init__()
-        self.model = model
-        self.args = Config(vars(args)) if args is not None else {}
-
-        optimizer = self.args.get("optimizer", OPTIMIZER)
-        self.optimizer_class = getattr(torch.optim, optimizer)
-        self.lr = self.args.get("lr", LR)
-
-
-    @staticmethod
-    def add_to_argparse(parser):
-        parser.add_argument("--optimizer", type=str, default=OPTIMIZER, help="optimizer class from torch.optim")
-        parser.add_argument("--lr", type=float, default=LR)
-        parser.add_argument("--weight_decay", type=float, default=0.01)
-        return parser
-
-    def configure_optimizers(self):
-        optimizer = self.optimizer_class(self.parameters(), lr=self.lr)
-        if self.one_cycle_max_lr is None:
-            return optimizer
-        scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer=optimizer, max_lr=self.one_cycle_max_lr, total_steps=self.one_cycle_total_steps)
-        return {"optimizer": optimizer, "lr_scheduler": scheduler, "monitor": "val_loss"}
-
-    def forward(self, x):
-        return self.model(x)
-
-    def training_step(self, batch, batch_idx):  # pylint: disable=unused-argument
-        x, y = batch
-        logits = self(x)
-        loss = self.loss_fn(logits, y)
-        self.log("train_loss", loss)
-        self.train_acc(logits, y)
-        self.log("train_acc", self.train_acc, on_step=False, on_epoch=True)
-        return loss
-
-    def validation_step(self, batch, batch_idx):  # pylint: disable=unused-argument
-        x, y = batch
-        logits = self(x)
-        loss = self.loss_fn(logits, y)
-        self.log("val_loss", loss, prog_bar=True)
-        self.val_acc(logits, y)
-        self.log("val_acc", self.val_acc, on_step=False, on_epoch=True, prog_bar=True)
-
-    def test_step(self, batch, batch_idx):  # pylint: disable=unused-argument
-        x, y = batch
-        logits = self(x)
-        self.test_acc(logits, y)
-        self.log("test_acc", self.test_acc, on_step=False, on_epoch=True)
-
-    @property
-    def num_training_steps(self) -> int:
-        """Total training steps inferred from datamodule and devices."""
-        if isinstance(self.trainer.limit_train_batches, int) and self.trainer.limit_train_batches != 0:
-            dataset_size = self.trainer.limit_train_batches
-        elif isinstance(self.trainer.limit_train_batches, float):
-            # limit_train_batches is a percentage of batches
-            dataset_size = len(self.trainer.datamodule.train_dataloader())
-            dataset_size = int(dataset_size * self.trainer.limit_train_batches)
-        else:
-            dataset_size = len(self.trainer.datamodule.train_dataloader())
-
-        num_devices = max(1, self.trainer.num_gpus, self.trainer.num_processes)
-        if self.trainer.tpu_cores:
-            num_devices = max(num_devices, self.trainer.tpu_cores)
-
-        effective_batch_size = self.trainer.accumulate_grad_batches * num_devices
-        max_estimated_steps = (dataset_size // effective_batch_size) * self.trainer.max_epochs
-
-        if self.trainer.max_steps and self.trainer.max_steps < max_estimated_steps:
-            return self.trainer.max_steps
-        return max_estimated_steps
-    

lit_models/transformer.py

@@ -1,521 +0,0 @@
-from logging import debug
-import random
-from turtle import distance
-import pytorch_lightning as pl
-import torch
-import pickle
-import torch.nn as nn
-import torch.nn.functional as F
-import numpy as np
-import json
-# from transformers.utils.dummy_pt_objects import PrefixConstrainedLogitsProcessor
-
-from .base import BaseLitModel
-from transformers.optimization import get_linear_schedule_with_warmup, get_cosine_schedule_with_warmup
-
-from functools import partial
-from .utils import rank_score, acc, LabelSmoothSoftmaxCEV1
-
-from typing import Callable, Iterable, List
-
-def pad_distance(pad_length, distance):
-    pad = nn.ConstantPad2d(padding=(0, pad_length, 0, pad_length), value=float('-inf'))
-    distance = pad(distance)
-    return distance
-
-def lmap(f: Callable, x: Iterable) -> List:
-    """list(map(f, x))"""
-    return list(map(f, x))
-
-def multilabel_categorical_crossentropy(y_pred, y_true):
-    y_pred = (1 - 2 * y_true) * y_pred
-    y_pred_neg = y_pred - y_true * 1e12
-    y_pred_pos = y_pred - (1 - y_true) * 1e12
-    zeros = torch.zeros_like(y_pred[..., :1])
-    y_pred_neg = torch.cat([y_pred_neg, zeros], dim=-1)
-    y_pred_pos = torch.cat([y_pred_pos, zeros], dim=-1)
-    neg_loss = torch.logsumexp(y_pred_neg, dim=-1)
-    pos_loss = torch.logsumexp(y_pred_pos, dim=-1)
-    return (neg_loss + pos_loss).mean()
-
-def decode(output_ids, tokenizer):
-    return lmap(str.strip, tokenizer.batch_decode(output_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True))
-
-class TransformerLitModel(BaseLitModel):
-    def __init__(self, model, args, tokenizer=None, data_config={}):
-        super().__init__(model, args)
-        self.save_hyperparameters(args)
-        if args.bce:
-            self.loss_fn = torch.nn.BCEWithLogitsLoss()
-        elif args.label_smoothing != 0.0:
-            self.loss_fn = LabelSmoothSoftmaxCEV1(lb_smooth=args.label_smoothing)
-        else:
-            self.loss_fn = nn.CrossEntropyLoss()
-        self.best_acc = 0
-        self.first = True
-        
-        self.tokenizer = tokenizer
-        self.num_heads = 12
-        self.__dict__.update(data_config)
-        # resize the word embedding layer
-        self.model.resize_token_embeddings(len(self.tokenizer))
-        self.decode = partial(decode, tokenizer=self.tokenizer)
-        if args.pretrain:
-            self._freaze_attention()
-        elif "ind" in args.data_dir:
-            # for inductive setting, use feeaze the word embedding
-            self._freaze_word_embedding()
-        
-        self.spatial_pos_encoder = nn.Embedding(5, self.num_heads, padding_idx=0)
-        self.graph_token_virtual_distance = nn.Embedding(1, self.num_heads)
-        
-
-    def forward(self, x):
-        return self.model(x)
-
-    def training_step(self, batch, batch_idx):  # pylint: disable=unused-argument
-        # embed();exit()
-        # print(self.optimizers().param_groups[1]['lr'])
-        labels = batch.pop("labels")
-        label = batch.pop("label")
-        pos = batch.pop("pos")
-        try:
-            en = batch.pop("en")
-            rel = batch.pop("rel")
-        except KeyError:
-            pass
-        input_ids = batch['input_ids']
-
-        distance_attention = torch.stack([pad_distance(len(input_ids[i]) - len(distance) - 1, distance) for i, distance in enumerate(batch['distance_attention'])])
-        distance = batch.pop("distance_attention")
-        graph_attn_bias = torch.zeros(input_ids.size(0), input_ids.size(1), input_ids.size(1), device='cuda')
-        graph_attn_bias[:, 1:, 1:][distance_attention == float('-inf')] = float('-inf')
-        graph_attn_bias = graph_attn_bias.unsqueeze(1).repeat(1, self.num_heads, 1, 1)
-        distance_attention = self.spatial_pos_encoder(distance_attention.long()).permute(0, 3, 1, 2)
-        graph_attn_bias[:, :, 1:, 1:] = graph_attn_bias[:, :, 1:, 1:] + distance_attention
-
-        if self.args.use_global_node:
-            t = self.graph_token_virtual_distance.weight.view(1, self.num_heads, 1)
-            graph_attn_bias[:, :, 1:, 0] = graph_attn_bias[:, :, 1:, 0] + t
-            graph_attn_bias[:, :, 0, :] = graph_attn_bias[:, :, 0, :] + t
-        
-        if self.args.add_attn_bias:
-            logits = self.model(**batch, return_dict=True, distance_attention=graph_attn_bias).logits
-        else:
-            logits = self.model(**batch, return_dict=True, distance_attention=None).logits
-
-        _, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True)
-        bs = input_ids.shape[0]
-        mask_logits = logits[torch.arange(bs), mask_idx][:, self.entity_id_st:self.entity_id_ed]
-
-        assert mask_idx.shape[0] == bs, "only one mask in sequence!"
-        if self.args.bce:
-            loss = self.loss_fn(mask_logits, labels)
-        else:
-            loss = self.loss_fn(mask_logits, label)
-
-        if batch_idx == 0:
-            print('\n'.join(self.decode(batch['input_ids'][:4])))
-        
-
-        return loss
-
-    def _eval(self, batch, batch_idx, ):
-        labels = batch.pop("labels")
-        input_ids = batch['input_ids']
-        # single label
-        label = batch.pop('label')
-        pos = batch.pop('pos')
-        distance_attention = torch.stack([pad_distance(len(input_ids[i]) - len(distance) - 1, distance) for i, distance in enumerate(batch['distance_attention'])])
-        distance = batch.pop("distance_attention")
-        graph_attn_bias = torch.zeros(input_ids.size(0), input_ids.size(1), input_ids.size(1), device='cuda')
-        graph_attn_bias[:, 1:, 1:][distance_attention == float('-inf')] = float('-inf')
-        graph_attn_bias = graph_attn_bias.unsqueeze(1).repeat(1, self.num_heads, 1, 1)
-        distance_attention = self.spatial_pos_encoder(distance_attention.long()).permute(0, 3, 1, 2)
-        graph_attn_bias[:, :, 1:, 1:] = graph_attn_bias[:, :, 1:, 1:] + distance_attention
-        # distance_attention = torch.stack([pad_distance(len(input_ids[i]) - len(distance), distance) for i, distance in enumerate(batch['distance_attention'])])
-        # distance = batch.pop("distance_attention")
-        # distance_attention = self.spatial_pos_encoder(distance_attention.long()).permute(0, 3, 1, 2)
-        my_keys = list(batch.keys())
-        for k in my_keys:
-            if k not in ["input_ids", "attention_mask", "token_type_ids"]:
-                batch.pop(k)
-        
-        if self.args.add_attn_bias:
-            logits = self.model(**batch, return_dict=True, distance_attention=graph_attn_bias).logits[:, :, self.entity_id_st:self.entity_id_ed]
-        else:
-            logits = self.model(**batch, return_dict=True, distance_attention=None).logits[:, :, self.entity_id_st:self.entity_id_ed]
-        _, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True)
-        bsz = input_ids.shape[0]
-        logits = logits[torch.arange(bsz), mask_idx]
-        # get the entity ranks
-        # filter the entity
-        assert labels[0][label[0]], "correct ids must in filiter!"
-        labels[torch.arange(bsz), label] = 0
-        assert logits.shape == labels.shape
-        logits += labels * -100 # mask entityj
-        # for i in range(bsz):
-        #     logits[i][labels]
-
-        _, outputs = torch.sort(logits, dim=1, descending=True)
-        _, outputs = torch.sort(outputs, dim=1)
-        ranks = outputs[torch.arange(bsz), label].detach().cpu() + 1
-        
-
-        return dict(ranks = np.array(ranks))
-
-    def validation_step(self, batch, batch_idx):
-        result = self._eval(batch, batch_idx)
-        return result
-
-    def validation_epoch_end(self, outputs) -> None:
-        ranks = np.concatenate([_['ranks'] for _ in outputs])
-        total_ranks = ranks.shape[0]
-
-        if not self.args.pretrain:
-            l_ranks = ranks[np.array(list(np.arange(0, total_ranks, 2)))]
-            r_ranks = ranks[np.array(list(np.arange(0, total_ranks, 2))) + 1]
-            self.log("Eval/lhits10", (l_ranks<=10).mean())
-            self.log("Eval/rhits10", (r_ranks<=10).mean())
-
-        hits20 = (ranks<=20).mean()
-        hits10 = (ranks<=10).mean()
-        hits3 = (ranks<=3).mean()
-        hits1 = (ranks<=1).mean()
-
-        self.log("Eval/hits10", hits10)
-        self.log("Eval/hits20", hits20)
-        self.log("Eval/hits3", hits3)
-        self.log("Eval/hits1", hits1)
-        self.log("Eval/mean_rank", ranks.mean())
-        self.log("Eval/mrr", (1. / ranks).mean())
-        self.log("hits10", hits10, prog_bar=True)
-        self.log("hits1", hits1, prog_bar=True)
-   
-
-    def test_step(self, batch, batch_idx):  # pylint: disable=unused-argument
-        # ranks = self._eval(batch, batch_idx)
-        result = self._eval(batch, batch_idx)
-        # self.log("Test/ranks", np.mean(ranks))
-
-        return result
-
-    def test_epoch_end(self, outputs) -> None:
-        ranks = np.concatenate([_['ranks'] for _ in outputs])
-
-        hits20 = (ranks<=20).mean()
-        hits10 = (ranks<=10).mean()
-        hits3 = (ranks<=3).mean()
-        hits1 = (ranks<=1).mean()
-
-        self.log("Test/hits10", hits10)
-        self.log("Test/hits20", hits20)
-        self.log("Test/hits3", hits3)
-        self.log("Test/hits1", hits1)
-        self.log("Test/mean_rank", ranks.mean())
-        self.log("Test/mrr", (1. / ranks).mean())
-
-    def configure_optimizers(self):
-        no_decay_param = ["bias", "LayerNorm.weight"]
-
-        optimizer_group_parameters = [
-            {"params": [p for n, p in self.model.named_parameters() if p.requires_grad and not any(nd in n for nd in no_decay_param)], "weight_decay": self.args.weight_decay},
-            {"params": [p for n, p in self.model.named_parameters() if p.requires_grad and any(nd in n for nd in no_decay_param)], "weight_decay": 0}
-        ]
-
-        optimizer = self.optimizer_class(optimizer_group_parameters, lr=self.lr, eps=1e-8)
-        scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=self.num_training_steps * self.args.warm_up_radio, num_training_steps=self.num_training_steps)
-        return {
-            "optimizer": optimizer, 
-            "lr_scheduler":{
-                'scheduler': scheduler,
-                'interval': 'step',  # or 'epoch'
-                'frequency': 1,
-            }
-        }
-    
-    def _freaze_attention(self):
-        for k, v in self.model.named_parameters():
-            if "word" not in k:
-                v.requires_grad = False
-            else:
-                print(k)
-    
-    def _freaze_word_embedding(self):
-        for k, v in self.model.named_parameters():
-            if "word" in k:
-                print(k)
-                v.requires_grad = False
-
-    @staticmethod
-    def add_to_argparse(parser):
-        parser = BaseLitModel.add_to_argparse(parser)
-
-        parser.add_argument("--label_smoothing", type=float, default=0.1, help="")
-        parser.add_argument("--bce", type=int, default=0, help="")
-        return parser
-
-
-
-import faiss
-import os
-class GetEntityEmbeddingLitModel(TransformerLitModel):
-    def __init__(self, model, args, tokenizer, data_config={}):
-        super().__init__(model, args, tokenizer, data_config)
-
-        self.faissid2entityid = {}
-        # self.index = faiss.IndexFlatL2(d)   # build the index
-
-        d, measure = self.model.config.hidden_size, faiss.METRIC_L2   
-        # param =  'HNSW64' 
-        # self.index = faiss.index_factory(d, param, measure)  
-        self.index = faiss.IndexFlatL2(d)   # build the index
-        # print(self.index.is_trained)                          # 此时输出为True 
-        # index.add(xb)
-        self.cnt_batch = 0
-        self.total_embedding = []
-
-    def test_step(self, batch, batch_idx):
-        labels = batch.pop("labels")
-        mask_idx = batch.pop("pos")
-        input_ids = batch['input_ids']
-        # single label
-        label = batch.pop('label')
-        # last layer 
-        hidden_states = self.model(**batch, return_dict=True, output_hidden_states=True).hidden_states[-1]
-        # _, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True)
-        bsz = input_ids.shape[0]
-        entity_embedding = hidden_states[torch.arange(bsz), mask_idx].cpu()
-        # use normalize or not ?
-        # entity_embedding = F.normalize(entity_embedding, dim=-1, p = 2)
-        self.total_embedding.append(entity_embedding)
-        # self.index.add(np.array(entity_embedding, dtype=np.float32))
-        for i, l in zip(range(bsz), label):
-            self.faissid2entityid[i+self.cnt_batch] = l.cpu()
-        self.cnt_batch += bsz
-
-
-    def test_epoch_end(self, outputs) -> None:
-        self.total_embedding = np.concatenate(self.total_embedding, axis=0)
-        # self.index.train(self.total_embedding)
-        print(faiss.MatrixStats(self.total_embedding).comments)
-        self.index.add(self.total_embedding)
-        faiss.write_index(self.index, os.path.join(self.args.data_dir, "faiss_dump.index"))
-        with open(os.path.join(self.args.data_dir, "faissid2entityid.pkl") ,'wb') as file:
-            pickle.dump(self.faissid2entityid, file)
-
-        with open(os.path.join(self.args.data_dir, "total_embedding.pkl") ,'wb') as file:
-            pickle.dump(self.total_embedding, file)
-        # print(f"number of  entity embedding : {len(self.faissid2entityid)}")
-
-    @staticmethod
-    def add_to_argparse(parser):
-        parser = TransformerLitModel.add_to_argparse(parser)
-        parser.add_argument("--faiss_init", type=int, default=1, help="get the embedding and save it the file.")
-        return parser
-
-class UseEntityEmbeddingLitModel(TransformerLitModel):
-    def __init__(self, model, args, tokenizer, data_config={}):
-        super().__init__(model, args, tokenizer, data_config)
-
-        self.faissid2entityid = pickle.load(open(os.path.join(self.args.data_dir, "faissid2entityid.pkl") ,'rb'))
-        self.index = faiss.read_index(os.path.join(self.args.data_dir, "faiss_dump.index"))
-        
-
-        self.dis2logits = distance2logits_2
-    
-    def _eval(self, batch, batch_idx, ):
-        labels = batch.pop("labels")
-        pos = batch.pop("pos")
-        input_ids = batch['input_ids']
-        # single label
-        label = batch.pop('label')
-
-        hidden_states = self.model(**batch, return_dict=True, output_hidden_states=True).hidden_states[-1]
-        _, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True)
-        bsz = input_ids.shape[0]
-        mask_embedding = np.array(hidden_states[torch.arange(bsz), mask_idx].cpu(), dtype=np.float32)
-        topk = 200
-        D, I = self.index.search(mask_embedding, topk)
-        labels[torch.arange(bsz), label] = 0
-
-        entity_logits = torch.full(labels.shape, -100.).to(self.device)
-        D = self.dis2logits(D)
-        for i in range(bsz):
-            for j in range(topk):
-                # filter entity in labels
-                if I[i][j] not in self.faissid2entityid: 
-                    print(I[i][j])
-                    break
-                # assert I[i][j] in self.faissid2entityid, print(I[i][j])
-                if labels[i][self.faissid2entityid[I[i][j]]]: continue
-                if entity_logits[i][self.faissid2entityid[I[i][j]]] == -100.:
-                    entity_logits[i][self.faissid2entityid[I[i][j]]] = D[i][j]
-                # no added together
-                # else:
-                #     entity_logits[i][self.faissid2entityid[I[i][j]]] += D[i][j]
-        # get the entity ranks
-        # filter the entity
-
-        assert entity_logits.shape == labels.shape
-
-        _, outputs = torch.sort(entity_logits, dim=1, descending=True)
-        _, outputs = torch.sort(outputs, dim=1)
-        ranks = outputs[torch.arange(bsz), label].detach().cpu() + 1
-        
-
-        return dict(ranks = np.array(ranks))
-    
-
-    @staticmethod
-    def add_to_argparse(parser):
-        parser = TransformerLitModel.add_to_argparse(parser)
-        parser.add_argument("--faiss_init", type=int, default=0, help="get the embedding and save it the file.")
-        parser.add_argument("--faiss_use", type=int, default=1, help="get the embedding and save it the file.")
-        return parser
-
-
-class CombineEntityEmbeddingLitModel(UseEntityEmbeddingLitModel):
-    def __init__(self, model, args, tokenizer, data_config={}):
-        super().__init__(model, args, tokenizer, data_config=data_config)
-        self.dis2logits = distance2logits_2
-        self.id2entity = {}
-        with open("./dataset/FB15k-237/entity2textlong.txt", 'r') as file:
-            cnt = 0
-            for line in file.readlines():
-                e, d = line.strip().split("\t")
-                self.id2entity[cnt] = e
-                cnt += 1
-        self.id2entity_t = {}
-        with open("./dataset/FB15k-237/entity2text.txt", 'r') as file:
-            for line in file.readlines():
-                e, d = line.strip().split("\t")
-                self.id2entity_t[e] = d
-        for k, v in self.id2entity.items():
-            self.id2entity[k] = self.id2entity_t[v]
-    def _eval(self, batch, batch_idx, ):
-        labels = batch.pop("labels")
-        input_ids = batch['input_ids']
-        # single label
-        label = batch.pop('label')
-        pos = batch.pop("pos")
-
-        result = self.model(**batch, return_dict=True, output_hidden_states=True)
-        hidden_states = result.hidden_states[-1]
-        _, mask_idx = (input_ids == self.tokenizer.mask_token_id).nonzero(as_tuple=True)
-        bsz = input_ids.shape[0]
-        mask_embedding = np.array(hidden_states[torch.arange(bsz), mask_idx].cpu(), dtype=np.float32)
-        # mask_embedding = np.array(hidden_states[torch.arange(bsz), mask_idx].cpu(), dtype=np.float32)
-        topk = self.args.knn_topk
-        D, I = self.index.search(mask_embedding, topk)
-        D = torch.from_numpy(D).to(self.device)
-        assert labels[0][label[0]], "correct ids must in filiter!"
-        labels[torch.arange(bsz), label] = 0
-
-
-        mask_logits = result.logits[:, :, self.entity_id_st:self.entity_id_ed]
-        mask_logits = mask_logits[torch.arange(bsz), mask_idx]
-        entity_logits = torch.full(labels.shape, 1000.).to(self.device)
-        # D = self.dis2logits(D)
-        for i in range(bsz):
-            for j in range(topk):
-                # filter entity in labels
-                if labels[i][self.faissid2entityid[I[i][j]]]: continue
-                if entity_logits[i][self.faissid2entityid[I[i][j]]] == 1000.:
-                    entity_logits[i][self.faissid2entityid[I[i][j]]] = D[i][j]
-                # else:
-                #     entity_logits[i][self.faissid2entityid[I[i][j]]] += D[i][j]
-        entity_logits = self.dis2logits(entity_logits)
-        # get the entity ranks
-        # filter the entity
-        assert entity_logits.shape == labels.shape
-        assert mask_logits.shape == labels.shape
-        # entity_logits = torch.softmax(entity_logits + labels * -100, dim=-1) # mask entityj
-        entity_logits = entity_logits + labels* -100.
-        mask_logits = torch.softmax(mask_logits + labels* -100, dim=-1)
-        # logits = mask_logits
-        logits = combine_knn_and_vocab_probs(entity_logits, mask_logits, self.args.knn_lambda)
-        # logits = entity_logits + mask_logits
-
-
-        knn_topk_logits, knn_topk_id  = entity_logits.topk(20)
-        mask_topk_logits, mask_topk_id  = mask_logits.topk(20)
-        union_topk = []
-        for i in range(bsz):
-            num_same = len(list(set(knn_topk_id[i].cpu().tolist()) & set(mask_topk_id[i].cpu().tolist())))
-            union_topk.append(num_same/ 20.)
-        
-        knn_topk_id = knn_topk_id.to("cpu")
-        mask_topk_id = mask_topk_id.to("cpu")
-        mask_topk_logits = mask_topk_logits.to("cpu")
-        knn_topk_logits = knn_topk_logits.to("cpu")
-        label = label.to("cpu")
-
-
-
-        for t in range(bsz):
-            if knn_topk_id[t][0] == label[t] and knn_topk_logits[t][0] > mask_topk_logits[t][0] and mask_topk_logits[t][0] <= 0.4:
-                print(knn_topk_logits[t], knn_topk_id[t])
-                print(lmap(lambda x: self.id2entity[x.item()], knn_topk_id[t]))
-                print(mask_topk_logits[t], mask_topk_id[t])
-                print(lmap(lambda x: self.id2entity[x.item()], mask_topk_id[t]))
-                print(label[t])
-                print()
-
-        _, outputs = torch.sort(logits, dim=1, descending=True)
-        _, outputs = torch.sort(outputs, dim=1)
-        ranks = outputs[torch.arange(bsz), label].detach().cpu() + 1
-        
-
-        return dict(ranks = np.array(ranks), knn_topk_id=knn_topk_id, knn_topk_logits=knn_topk_logits,
-            mask_topk_id=mask_topk_id, mask_topk_logits=mask_topk_logits, num_same = np.array(union_topk))
-    
-    def test_epoch_end(self, outputs) -> None:
-
-        ranks = np.concatenate([_['ranks'] for _ in outputs])
-        num_same = np.concatenate([_['num_same'] for _ in outputs])
-        results_keys = list(outputs[0].keys())
-        results = {}
-        # for k in results_keys:
-        #     results.
-
-        self.log("Test/num_same", num_same.mean())
-
-        hits20 = (ranks<=20).mean()
-        hits10 = (ranks<=10).mean()
-        hits3 = (ranks<=3).mean()
-        hits1 = (ranks<=1).mean()
-
-       
-        self.log("Test/hits10", hits10)
-        self.log("Test/hits20", hits20)
-        self.log("Test/hits3", hits3)
-        self.log("Test/hits1", hits1)
-        self.log("Test/mean_rank", ranks.mean())
-        self.log("Test/mrr", (1. / ranks).mean())
-
-    def add_to_argparse(parser):
-        parser = TransformerLitModel.add_to_argparse(parser)
-        parser.add_argument("--knn_lambda", type=float, default=0.5, help="lambda * knn + (1-lambda) * mask logits , lambda of knn logits and mask logits.")
-        parser.add_argument("--knn_topk", type=int, default=100, help="")
-
-        return parser
-
-def combine_knn_and_vocab_probs(knn_p, vocab_p, coeff=0.5):
-    combine_probs = torch.stack([vocab_p, knn_p], dim=0)
-    coeffs = torch.ones_like(combine_probs)
-    coeffs[0] = np.log(1 - coeff)
-    coeffs[1] = np.log(coeff)
-    curr_prob = torch.logsumexp(combine_probs + coeffs, dim=0)
-
-    return curr_prob
-
-def distance2logits(D):
-    return torch.softmax( -1. * torch.tensor(D) / 30., dim=-1)
-
-def distance2logits_2(D, n=10):
-    if not isinstance(D, torch.Tensor):
-        D = torch.tensor(D)
-    if torch.sum(D) != 0.0:
-        distances = torch.exp(-D/n) / torch.sum(torch.exp(-D/n), dim=-1, keepdim=True)
-    return distances

lit_models/utils.py

@@ -1,66 +0,0 @@
-import json
-import numpy as np
-
-def rank_score(ranks):
-	# prepare the dataset
-	len_samples = len(ranks)
-	hits10 = [0] * len_samples
-	hits5 = [0] * len_samples
-	hits1 = [0] * len_samples
-	mrr = []
-
-
-	for idx, rank in enumerate(ranks):
-		if rank <= 10:
-			hits10[idx] = 1.
-			if rank <= 5:
-				hits5[idx] = 1.
-				if rank <= 1:
-					hits1[idx] = 1.
-		mrr.append(1./rank)
-	
-
-	return np.mean(hits10), np.mean(hits5), np.mean(hits1), np.mean(mrr)
-
-def acc(logits, labels):
-    preds = np.argmax(logits, axis=-1)
-    return (preds == labels).mean()
-import torch.nn as nn
-import torch
-class LabelSmoothSoftmaxCEV1(nn.Module):
-    '''
-    This is the autograd version, you can also try the LabelSmoothSoftmaxCEV2 that uses derived gradients
-    '''
-
-    def __init__(self, lb_smooth=0.1, reduction='mean', ignore_index=-100):
-        super(LabelSmoothSoftmaxCEV1, self).__init__()
-        self.lb_smooth = lb_smooth
-        self.reduction = reduction
-        self.lb_ignore = ignore_index
-        self.log_softmax = nn.LogSoftmax(dim=1)
-
-    def forward(self, logits, label):
-        '''
-        args: logits: tensor of shape (N, C, H, W)
-        args: label: tensor of shape(N, H, W)
-        '''
-        # overcome ignored label
-        with torch.no_grad():
-            num_classes = logits.size(1)
-            label = label.clone().detach()
-            ignore = label == self.lb_ignore
-            n_valid = (ignore == 0).sum()
-            label[ignore] = 0
-            lb_pos, lb_neg = 1. - self.lb_smooth, self.lb_smooth / num_classes
-            label = torch.empty_like(logits).fill_(
-                lb_neg).scatter_(1, label.unsqueeze(1), lb_pos).detach()
-
-        logs = self.log_softmax(logits)
-        loss = -torch.sum(logs * label, dim=1)
-        loss[ignore] = 0
-        if self.reduction == 'mean':
-            loss = loss.sum() / n_valid
-        if self.reduction == 'sum':
-            loss = loss.sum()
-
-        return loss

log/fb2

@@ -0,0 +1,389 @@
+2023-05-02 04:18:49,398 - fb2 - [INFO] - {'dataset': 'FB15k-237', 'name': 'fb2', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 1e-05, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.1, 'drop': 0.2, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': False}
+2023-05-02 04:19:01,259 - fb2 - [INFO] - [E:0| 0]: Train Loss:0.69689,  Val MRR:0.0, 	fb2
+2023-05-02 04:20:32,640 - fb2 - [INFO] - [E:0| 100]: Train Loss:0.36744,  Val MRR:0.0, 	fb2
+2023-05-02 04:22:04,584 - fb2 - [INFO] - [E:0| 200]: Train Loss:0.24098,  Val MRR:0.0, 	fb2
+2023-05-02 04:23:36,730 - fb2 - [INFO] - [E:0| 300]: Train Loss:0.17938,  Val MRR:0.0, 	fb2
+2023-05-02 04:25:05,852 - fb2 - [INFO] - [E:0| 400]: Train Loss:0.14323,  Val MRR:0.0, 	fb2
+2023-05-02 04:26:37,938 - fb2 - [INFO] - [E:0| 500]: Train Loss:0.11952,  Val MRR:0.0, 	fb2
+2023-05-02 04:28:09,889 - fb2 - [INFO] - [E:0| 600]: Train Loss:0.10275,  Val MRR:0.0, 	fb2
+2023-05-02 04:29:41,867 - fb2 - [INFO] - [E:0| 700]: Train Loss:0.090272,  Val MRR:0.0, 	fb2
+2023-05-02 04:31:07,818 - fb2 - [INFO] - [E:0| 800]: Train Loss:0.080642,  Val MRR:0.0, 	fb2
+2023-05-02 04:32:39,703 - fb2 - [INFO] - [E:0| 900]: Train Loss:0.07296,  Val MRR:0.0, 	fb2
+2023-05-02 04:34:11,338 - fb2 - [INFO] - [E:0| 1000]: Train Loss:0.066702,  Val MRR:0.0, 	fb2
+2023-05-02 04:35:42,626 - fb2 - [INFO] - [E:0| 1100]: Train Loss:0.061509,  Val MRR:0.0, 	fb2
+2023-05-02 04:36:45,446 - fb2 - [INFO] - [Epoch:0]:  Training Loss:0.05842
+
+2023-05-02 04:36:45,724 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 04:37:14,248 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 04:37:24,208 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 04:37:57,309 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 04:38:09,080 - fb2 - [INFO] - [Evaluating Epoch 0 valid]: 
+	MRR: Tail : 0.05276, Head : 0.00751, Avg : 0.03014
+
+2023-05-02 04:38:10,219 - fb2 - [INFO] - [Epoch 0]:  Training Loss: 0.058417,  Valid MRR: 0.03014, 
+
+
+
+2023-05-02 04:38:11,161 - fb2 - [INFO] - [E:1| 0]: Train Loss:0.0083711,  Val MRR:0.03014, 	fb2
+2023-05-02 04:39:42,529 - fb2 - [INFO] - [E:1| 100]: Train Loss:0.0085887,  Val MRR:0.03014, 	fb2
+2023-05-02 04:41:14,306 - fb2 - [INFO] - [E:1| 200]: Train Loss:0.0084227,  Val MRR:0.03014, 	fb2
+2023-05-02 04:42:45,633 - fb2 - [INFO] - [E:1| 300]: Train Loss:0.0082433,  Val MRR:0.03014, 	fb2
+2023-05-02 04:44:11,829 - fb2 - [INFO] - [E:1| 400]: Train Loss:0.0080888,  Val MRR:0.03014, 	fb2
+2023-05-02 04:45:43,750 - fb2 - [INFO] - [E:1| 500]: Train Loss:0.007955,  Val MRR:0.03014, 	fb2
+2023-05-02 04:47:15,788 - fb2 - [INFO] - [E:1| 600]: Train Loss:0.0078681,  Val MRR:0.03014, 	fb2
+2023-05-02 04:48:48,084 - fb2 - [INFO] - [E:1| 700]: Train Loss:0.0077871,  Val MRR:0.03014, 	fb2
+2023-05-02 04:50:18,308 - fb2 - [INFO] - [E:1| 800]: Train Loss:0.0077091,  Val MRR:0.03014, 	fb2
+2023-05-02 04:51:49,440 - fb2 - [INFO] - [E:1| 900]: Train Loss:0.00764,  Val MRR:0.03014, 	fb2
+2023-05-02 04:53:21,113 - fb2 - [INFO] - [E:1| 1000]: Train Loss:0.007576,  Val MRR:0.03014, 	fb2
+2023-05-02 04:54:52,877 - fb2 - [INFO] - [E:1| 1100]: Train Loss:0.0075201,  Val MRR:0.03014, 	fb2
+2023-05-02 04:55:55,526 - fb2 - [INFO] - [Epoch:1]:  Training Loss:0.007478
+
+2023-05-02 04:55:55,884 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 04:56:25,912 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 04:56:36,106 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 04:57:04,951 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 04:57:16,876 - fb2 - [INFO] - [Evaluating Epoch 1 valid]: 
+	MRR: Tail : 0.05304, Head : 0.00764, Avg : 0.03034
+
+2023-05-02 04:57:19,016 - fb2 - [INFO] - [Epoch 1]:  Training Loss: 0.007478,  Valid MRR: 0.03034, 
+
+
+
+2023-05-02 04:57:19,964 - fb2 - [INFO] - [E:2| 0]: Train Loss:0.0077412,  Val MRR:0.03034, 	fb2
+2023-05-02 04:58:51,692 - fb2 - [INFO] - [E:2| 100]: Train Loss:0.006784,  Val MRR:0.03034, 	fb2
+2023-05-02 05:00:23,348 - fb2 - [INFO] - [E:2| 200]: Train Loss:0.0067657,  Val MRR:0.03034, 	fb2
+2023-05-02 05:01:55,157 - fb2 - [INFO] - [E:2| 300]: Train Loss:0.0067572,  Val MRR:0.03034, 	fb2
+2023-05-02 05:03:25,371 - fb2 - [INFO] - [E:2| 400]: Train Loss:0.0067142,  Val MRR:0.03034, 	fb2
+2023-05-02 05:04:55,830 - fb2 - [INFO] - [E:2| 500]: Train Loss:0.0067221,  Val MRR:0.03034, 	fb2
+2023-05-02 05:06:27,700 - fb2 - [INFO] - [E:2| 600]: Train Loss:0.0067123,  Val MRR:0.03034, 	fb2
+2023-05-02 05:07:59,354 - fb2 - [INFO] - [E:2| 700]: Train Loss:0.0066851,  Val MRR:0.03034, 	fb2
+2023-05-02 05:09:31,441 - fb2 - [INFO] - [E:2| 800]: Train Loss:0.0066677,  Val MRR:0.03034, 	fb2
+2023-05-02 05:10:58,593 - fb2 - [INFO] - [E:2| 900]: Train Loss:0.0066402,  Val MRR:0.03034, 	fb2
+2023-05-02 05:12:30,077 - fb2 - [INFO] - [E:2| 1000]: Train Loss:0.0066205,  Val MRR:0.03034, 	fb2
+2023-05-02 05:14:01,720 - fb2 - [INFO] - [E:2| 1100]: Train Loss:0.0065907,  Val MRR:0.03034, 	fb2
+2023-05-02 05:15:04,976 - fb2 - [INFO] - [Epoch:2]:  Training Loss:0.006572
+
+2023-05-02 05:15:05,303 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 05:15:35,236 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 05:15:46,422 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 05:16:18,357 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 05:16:29,632 - fb2 - [INFO] - [Evaluating Epoch 2 valid]: 
+	MRR: Tail : 0.05261, Head : 0.00737, Avg : 0.02999
+
+2023-05-02 05:16:29,632 - fb2 - [INFO] - [Epoch 2]:  Training Loss: 0.0065725,  Valid MRR: 0.03034, 
+
+
+
+2023-05-02 05:16:30,314 - fb2 - [INFO] - [E:3| 0]: Train Loss:0.0058567,  Val MRR:0.03034, 	fb2
+2023-05-02 05:18:01,365 - fb2 - [INFO] - [E:3| 100]: Train Loss:0.0062464,  Val MRR:0.03034, 	fb2
+2023-05-02 05:19:33,019 - fb2 - [INFO] - [E:3| 200]: Train Loss:0.0061913,  Val MRR:0.03034, 	fb2
+2023-05-02 05:21:04,158 - fb2 - [INFO] - [E:3| 300]: Train Loss:0.006163,  Val MRR:0.03034, 	fb2
+2023-05-02 05:22:35,273 - fb2 - [INFO] - [E:3| 400]: Train Loss:0.0061644,  Val MRR:0.03034, 	fb2
+2023-05-02 05:24:00,356 - fb2 - [INFO] - [E:3| 500]: Train Loss:0.0061382,  Val MRR:0.03034, 	fb2
+2023-05-02 05:25:31,092 - fb2 - [INFO] - [E:3| 600]: Train Loss:0.0061044,  Val MRR:0.03034, 	fb2
+2023-05-02 05:27:01,421 - fb2 - [INFO] - [E:3| 700]: Train Loss:0.0060781,  Val MRR:0.03034, 	fb2
+2023-05-02 05:28:31,802 - fb2 - [INFO] - [E:3| 800]: Train Loss:0.0060643,  Val MRR:0.03034, 	fb2
+2023-05-02 05:29:59,573 - fb2 - [INFO] - [E:3| 900]: Train Loss:0.0060521,  Val MRR:0.03034, 	fb2
+2023-05-02 05:31:29,674 - fb2 - [INFO] - [E:3| 1000]: Train Loss:0.0060237,  Val MRR:0.03034, 	fb2
+2023-05-02 05:32:59,535 - fb2 - [INFO] - [E:3| 1100]: Train Loss:0.0059936,  Val MRR:0.03034, 	fb2
+2023-05-02 05:34:00,924 - fb2 - [INFO] - [Epoch:3]:  Training Loss:0.005975
+
+2023-05-02 05:34:01,312 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 05:34:31,261 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 05:34:42,271 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 05:35:15,204 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 05:35:26,943 - fb2 - [INFO] - [Evaluating Epoch 3 valid]: 
+	MRR: Tail : 0.05358, Head : 0.00717, Avg : 0.03037
+
+2023-05-02 05:35:28,082 - fb2 - [INFO] - [Epoch 3]:  Training Loss: 0.0059753,  Valid MRR: 0.03037, 
+
+
+
+2023-05-02 05:35:28,897 - fb2 - [INFO] - [E:4| 0]: Train Loss:0.0053117,  Val MRR:0.03037, 	fb2
+2023-05-02 05:36:53,156 - fb2 - [INFO] - [E:4| 100]: Train Loss:0.0056516,  Val MRR:0.03037, 	fb2
+2023-05-02 05:38:22,908 - fb2 - [INFO] - [E:4| 200]: Train Loss:0.0056337,  Val MRR:0.03037, 	fb2
+2023-05-02 05:39:52,263 - fb2 - [INFO] - [E:4| 300]: Train Loss:0.0055847,  Val MRR:0.03037, 	fb2
+2023-05-02 05:41:21,010 - fb2 - [INFO] - [E:4| 400]: Train Loss:0.0055579,  Val MRR:0.03037, 	fb2
+2023-05-02 05:42:47,682 - fb2 - [INFO] - [E:4| 500]: Train Loss:0.0055457,  Val MRR:0.03037, 	fb2
+2023-05-02 05:44:16,669 - fb2 - [INFO] - [E:4| 600]: Train Loss:0.0055229,  Val MRR:0.03037, 	fb2
+2023-05-02 05:45:46,114 - fb2 - [INFO] - [E:4| 700]: Train Loss:0.0055053,  Val MRR:0.03037, 	fb2
+2023-05-02 05:47:14,834 - fb2 - [INFO] - [E:4| 800]: Train Loss:0.0054886,  Val MRR:0.03037, 	fb2
+2023-05-02 05:48:43,883 - fb2 - [INFO] - [E:4| 900]: Train Loss:0.0054641,  Val MRR:0.03037, 	fb2
+2023-05-02 05:50:07,436 - fb2 - [INFO] - [E:4| 1000]: Train Loss:0.0054569,  Val MRR:0.03037, 	fb2
+2023-05-02 05:51:36,436 - fb2 - [INFO] - [E:4| 1100]: Train Loss:0.0054476,  Val MRR:0.03037, 	fb2
+2023-05-02 05:52:37,532 - fb2 - [INFO] - [Epoch:4]:  Training Loss:0.005427
+
+2023-05-02 05:52:37,851 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 05:53:07,350 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 05:53:18,338 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 05:53:50,675 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 05:54:02,474 - fb2 - [INFO] - [Evaluating Epoch 4 valid]: 
+	MRR: Tail : 0.05373, Head : 0.00834, Avg : 0.03104
+
+2023-05-02 05:54:03,608 - fb2 - [INFO] - [Epoch 4]:  Training Loss: 0.0054269,  Valid MRR: 0.03104, 
+
+
+
+2023-05-02 05:54:04,547 - fb2 - [INFO] - [E:5| 0]: Train Loss:0.011799,  Val MRR:0.03104, 	fb2
+2023-05-02 05:55:32,136 - fb2 - [INFO] - [E:5| 100]: Train Loss:0.0052331,  Val MRR:0.03104, 	fb2
+2023-05-02 05:56:59,892 - fb2 - [INFO] - [E:5| 200]: Train Loss:0.0051725,  Val MRR:0.03104, 	fb2
+2023-05-02 05:58:28,729 - fb2 - [INFO] - [E:5| 300]: Train Loss:0.005151,  Val MRR:0.03104, 	fb2
+2023-05-02 05:59:57,684 - fb2 - [INFO] - [E:5| 400]: Train Loss:0.0051306,  Val MRR:0.03104, 	fb2
+2023-05-02 06:01:26,978 - fb2 - [INFO] - [E:5| 500]: Train Loss:0.0050962,  Val MRR:0.03104, 	fb2
+2023-05-02 06:02:50,567 - fb2 - [INFO] - [E:5| 600]: Train Loss:0.0050567,  Val MRR:0.03104, 	fb2
+2023-05-02 06:04:19,772 - fb2 - [INFO] - [E:5| 700]: Train Loss:0.005027,  Val MRR:0.03104, 	fb2
+2023-05-02 06:05:48,574 - fb2 - [INFO] - [E:5| 800]: Train Loss:0.0050262,  Val MRR:0.03104, 	fb2
+2023-05-02 06:07:17,606 - fb2 - [INFO] - [E:5| 900]: Train Loss:0.0050039,  Val MRR:0.03104, 	fb2
+2023-05-02 06:08:44,259 - fb2 - [INFO] - [E:5| 1000]: Train Loss:0.0049983,  Val MRR:0.03104, 	fb2
+2023-05-02 06:10:12,405 - fb2 - [INFO] - [E:5| 1100]: Train Loss:0.0049798,  Val MRR:0.03104, 	fb2
+2023-05-02 06:11:13,490 - fb2 - [INFO] - [Epoch:5]:  Training Loss:0.004961
+
+2023-05-02 06:11:13,729 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 06:11:43,033 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 06:11:53,943 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 06:12:26,527 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 06:12:38,133 - fb2 - [INFO] - [Evaluating Epoch 5 valid]: 
+	MRR: Tail : 0.04854, Head : 0.00726, Avg : 0.0279
+
+2023-05-02 06:12:38,133 - fb2 - [INFO] - [Epoch 5]:  Training Loss: 0.0049607,  Valid MRR: 0.03104, 
+
+
+
+2023-05-02 06:12:39,021 - fb2 - [INFO] - [E:6| 0]: Train Loss:0.0061745,  Val MRR:0.03104, 	fb2
+2023-05-02 06:14:08,189 - fb2 - [INFO] - [E:6| 100]: Train Loss:0.0046955,  Val MRR:0.03104, 	fb2
+2023-05-02 06:15:31,880 - fb2 - [INFO] - [E:6| 200]: Train Loss:0.0046845,  Val MRR:0.03104, 	fb2
+2023-05-02 06:17:00,642 - fb2 - [INFO] - [E:6| 300]: Train Loss:0.0047069,  Val MRR:0.03104, 	fb2
+2023-05-02 06:18:29,603 - fb2 - [INFO] - [E:6| 400]: Train Loss:0.0046892,  Val MRR:0.03104, 	fb2
+2023-05-02 06:19:58,015 - fb2 - [INFO] - [E:6| 500]: Train Loss:0.0046968,  Val MRR:0.03104, 	fb2
+2023-05-02 06:21:26,175 - fb2 - [INFO] - [E:6| 600]: Train Loss:0.0046998,  Val MRR:0.03104, 	fb2
+2023-05-02 06:22:53,128 - fb2 - [INFO] - [E:6| 700]: Train Loss:0.0046795,  Val MRR:0.03104, 	fb2
+2023-05-02 06:24:22,260 - fb2 - [INFO] - [E:6| 800]: Train Loss:0.0046533,  Val MRR:0.03104, 	fb2
+2023-05-02 06:25:51,084 - fb2 - [INFO] - [E:6| 900]: Train Loss:0.0046311,  Val MRR:0.03104, 	fb2
+2023-05-02 06:27:19,752 - fb2 - [INFO] - [E:6| 1000]: Train Loss:0.0046129,  Val MRR:0.03104, 	fb2
+2023-05-02 06:28:43,764 - fb2 - [INFO] - [E:6| 1100]: Train Loss:0.004602,  Val MRR:0.03104, 	fb2
+2023-05-02 06:29:44,669 - fb2 - [INFO] - [Epoch:6]:  Training Loss:0.004588
+
+2023-05-02 06:29:45,063 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 06:30:14,453 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 06:30:25,422 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 06:30:57,700 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 06:31:09,385 - fb2 - [INFO] - [Evaluating Epoch 6 valid]: 
+	MRR: Tail : 0.05203, Head : 0.00718, Avg : 0.0296
+
+2023-05-02 06:31:09,385 - fb2 - [INFO] - [Epoch 6]:  Training Loss: 0.0045885,  Valid MRR: 0.03104, 
+
+
+
+2023-05-02 06:31:10,424 - fb2 - [INFO] - [E:7| 0]: Train Loss:0.0040115,  Val MRR:0.03104, 	fb2
+2023-05-02 06:32:38,839 - fb2 - [INFO] - [E:7| 100]: Train Loss:0.0043937,  Val MRR:0.03104, 	fb2
+2023-05-02 06:34:07,833 - fb2 - [INFO] - [E:7| 200]: Train Loss:0.0043624,  Val MRR:0.03104, 	fb2
+2023-05-02 06:35:34,371 - fb2 - [INFO] - [E:7| 300]: Train Loss:0.0043875,  Val MRR:0.03104, 	fb2
+2023-05-02 06:37:03,680 - fb2 - [INFO] - [E:7| 400]: Train Loss:0.0043683,  Val MRR:0.03104, 	fb2
+2023-05-02 06:38:32,695 - fb2 - [INFO] - [E:7| 500]: Train Loss:0.0043403,  Val MRR:0.03104, 	fb2
+2023-05-02 06:40:01,879 - fb2 - [INFO] - [E:7| 600]: Train Loss:0.0043325,  Val MRR:0.03104, 	fb2
+2023-05-02 06:41:25,014 - fb2 - [INFO] - [E:7| 700]: Train Loss:0.0043239,  Val MRR:0.03104, 	fb2
+2023-05-02 06:42:53,634 - fb2 - [INFO] - [E:7| 800]: Train Loss:0.0043244,  Val MRR:0.03104, 	fb2
+2023-05-02 06:44:23,433 - fb2 - [INFO] - [E:7| 900]: Train Loss:0.0043176,  Val MRR:0.03104, 	fb2
+2023-05-02 06:46:45,886 - fb2 - [INFO] - [E:7| 1000]: Train Loss:0.0043261,  Val MRR:0.03104, 	fb2
+2023-05-02 06:48:56,506 - fb2 - [INFO] - [E:7| 1100]: Train Loss:0.0043094,  Val MRR:0.03104, 	fb2
+2023-05-02 06:49:57,310 - fb2 - [INFO] - [Epoch:7]:  Training Loss:0.004304
+
+2023-05-02 06:49:57,584 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 06:50:27,212 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 06:50:38,099 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 06:51:10,813 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 06:51:22,553 - fb2 - [INFO] - [Evaluating Epoch 7 valid]: 
+	MRR: Tail : 0.05444, Head : 0.00794, Avg : 0.03119
+
+2023-05-02 06:51:23,715 - fb2 - [INFO] - [Epoch 7]:  Training Loss: 0.0043042,  Valid MRR: 0.03119, 
+
+
+
+2023-05-02 06:51:24,590 - fb2 - [INFO] - [E:8| 0]: Train Loss:0.0037442,  Val MRR:0.03119, 	fb2
+2023-05-02 06:52:53,796 - fb2 - [INFO] - [E:8| 100]: Train Loss:0.0043246,  Val MRR:0.03119, 	fb2
+2023-05-02 06:54:21,907 - fb2 - [INFO] - [E:8| 200]: Train Loss:0.0042743,  Val MRR:0.03119, 	fb2
+2023-05-02 06:55:46,156 - fb2 - [INFO] - [E:8| 300]: Train Loss:0.004254,  Val MRR:0.03119, 	fb2
+2023-05-02 06:57:15,114 - fb2 - [INFO] - [E:8| 400]: Train Loss:0.0042282,  Val MRR:0.03119, 	fb2
+2023-05-02 06:58:43,879 - fb2 - [INFO] - [E:8| 500]: Train Loss:0.0042196,  Val MRR:0.03119, 	fb2
+2023-05-02 07:00:12,524 - fb2 - [INFO] - [E:8| 600]: Train Loss:0.0042126,  Val MRR:0.03119, 	fb2
+2023-05-02 07:01:33,515 - fb2 - [INFO] - [E:8| 700]: Train Loss:0.004186,  Val MRR:0.03119, 	fb2
+2023-05-02 07:02:38,712 - fb2 - [INFO] - [E:8| 800]: Train Loss:0.0041633,  Val MRR:0.03119, 	fb2
+2023-05-02 07:03:43,849 - fb2 - [INFO] - [E:8| 900]: Train Loss:0.0041458,  Val MRR:0.03119, 	fb2
+2023-05-02 07:04:49,035 - fb2 - [INFO] - [E:8| 1000]: Train Loss:0.0041378,  Val MRR:0.03119, 	fb2
+2023-05-02 07:05:54,251 - fb2 - [INFO] - [E:8| 1100]: Train Loss:0.0041268,  Val MRR:0.03119, 	fb2
+2023-05-02 07:06:38,939 - fb2 - [INFO] - [Epoch:8]:  Training Loss:0.004119
+
+2023-05-02 07:06:39,157 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 07:07:00,572 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 07:07:08,535 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 07:07:32,722 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 07:07:41,389 - fb2 - [INFO] - [Evaluating Epoch 8 valid]: 
+	MRR: Tail : 0.04819, Head : 0.00725, Avg : 0.02772
+
+2023-05-02 07:07:41,390 - fb2 - [INFO] - [Epoch 8]:  Training Loss: 0.0041194,  Valid MRR: 0.03119, 
+
+
+
+2023-05-02 07:07:42,060 - fb2 - [INFO] - [E:9| 0]: Train Loss:0.003654,  Val MRR:0.03119, 	fb2
+2023-05-02 07:08:47,270 - fb2 - [INFO] - [E:9| 100]: Train Loss:0.0040588,  Val MRR:0.03119, 	fb2
+2023-05-02 07:09:52,478 - fb2 - [INFO] - [E:9| 200]: Train Loss:0.0040426,  Val MRR:0.03119, 	fb2
+2023-05-02 07:10:57,634 - fb2 - [INFO] - [E:9| 300]: Train Loss:0.0040221,  Val MRR:0.03119, 	fb2
+2023-05-02 07:12:02,837 - fb2 - [INFO] - [E:9| 400]: Train Loss:0.0039971,  Val MRR:0.03119, 	fb2
+2023-05-02 07:13:08,035 - fb2 - [INFO] - [E:9| 500]: Train Loss:0.0039898,  Val MRR:0.03119, 	fb2
+2023-05-02 07:14:13,321 - fb2 - [INFO] - [E:9| 600]: Train Loss:0.0039806,  Val MRR:0.03119, 	fb2
+2023-05-02 07:15:18,533 - fb2 - [INFO] - [E:9| 700]: Train Loss:0.0039814,  Val MRR:0.03119, 	fb2
+2023-05-02 07:16:23,815 - fb2 - [INFO] - [E:9| 800]: Train Loss:0.003962,  Val MRR:0.03119, 	fb2
+2023-05-02 07:17:29,003 - fb2 - [INFO] - [E:9| 900]: Train Loss:0.0039572,  Val MRR:0.03119, 	fb2
+2023-05-02 07:18:34,329 - fb2 - [INFO] - [E:9| 1000]: Train Loss:0.0039594,  Val MRR:0.03119, 	fb2
+2023-05-02 07:19:39,427 - fb2 - [INFO] - [E:9| 1100]: Train Loss:0.0039596,  Val MRR:0.03119, 	fb2
+2023-05-02 07:20:24,100 - fb2 - [INFO] - [Epoch:9]:  Training Loss:0.003968
+
+2023-05-02 07:20:24,318 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 07:20:45,699 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 07:20:53,656 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 07:21:17,766 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 07:21:26,504 - fb2 - [INFO] - [Evaluating Epoch 9 valid]: 
+	MRR: Tail : 0.05162, Head : 0.0051, Avg : 0.02836
+	MR: Tail : 3521.8, Head : 4816.5, Avg : 4169.1
+	Hit-1: Tail : 0.01745, Head : 0.0, Avg : 0.00873
+	Hit-3: Tail : 0.05252, Head : 0.00473, Avg : 0.02863
+	Hit-10: Tail : 0.13162, Head : 0.00941, Avg : 0.07052
+2023-05-02 07:21:26,504 - fb2 - [INFO] - [Epoch 9]:  Training Loss: 0.0039679,  Valid MRR: 0.03119, 
+
+
+
+2023-05-02 07:21:27,168 - fb2 - [INFO] - [E:10| 0]: Train Loss:0.0034523,  Val MRR:0.03119, 	fb2
+2023-05-02 07:22:32,379 - fb2 - [INFO] - [E:10| 100]: Train Loss:0.0039456,  Val MRR:0.03119, 	fb2
+2023-05-02 07:23:37,573 - fb2 - [INFO] - [E:10| 200]: Train Loss:0.0039008,  Val MRR:0.03119, 	fb2
+2023-05-02 07:24:42,704 - fb2 - [INFO] - [E:10| 300]: Train Loss:0.0039139,  Val MRR:0.03119, 	fb2
+2023-05-02 07:25:47,783 - fb2 - [INFO] - [E:10| 400]: Train Loss:0.0038921,  Val MRR:0.03119, 	fb2
+2023-05-02 07:26:52,916 - fb2 - [INFO] - [E:10| 500]: Train Loss:0.0038931,  Val MRR:0.03119, 	fb2
+2023-05-02 07:27:58,012 - fb2 - [INFO] - [E:10| 600]: Train Loss:0.0038964,  Val MRR:0.03119, 	fb2
+2023-05-02 07:29:03,119 - fb2 - [INFO] - [E:10| 700]: Train Loss:0.0039048,  Val MRR:0.03119, 	fb2
+2023-05-02 07:30:08,304 - fb2 - [INFO] - [E:10| 800]: Train Loss:0.0038894,  Val MRR:0.03119, 	fb2
+2023-05-02 07:31:13,434 - fb2 - [INFO] - [E:10| 900]: Train Loss:0.0038943,  Val MRR:0.03119, 	fb2
+2023-05-02 07:32:18,598 - fb2 - [INFO] - [E:10| 1000]: Train Loss:0.0038952,  Val MRR:0.03119, 	fb2
+2023-05-02 07:33:23,724 - fb2 - [INFO] - [E:10| 1100]: Train Loss:0.0039006,  Val MRR:0.03119, 	fb2
+2023-05-02 07:34:08,316 - fb2 - [INFO] - [Epoch:10]:  Training Loss:0.003898
+
+2023-05-02 07:34:08,533 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 07:34:29,851 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 07:34:37,787 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 07:35:01,819 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 07:35:10,489 - fb2 - [INFO] - [Evaluating Epoch 10 valid]: 
+	MRR: Tail : 0.05283, Head : 0.00678, Avg : 0.0298
+
+2023-05-02 07:35:10,490 - fb2 - [INFO] - [Epoch 10]:  Training Loss: 0.0038978,  Valid MRR: 0.03119, 
+
+
+
+2023-05-02 07:35:11,153 - fb2 - [INFO] - [E:11| 0]: Train Loss:0.0037874,  Val MRR:0.03119, 	fb2
+2023-05-02 07:36:16,325 - fb2 - [INFO] - [E:11| 100]: Train Loss:0.0039696,  Val MRR:0.03119, 	fb2
+2023-05-02 07:37:21,496 - fb2 - [INFO] - [E:11| 200]: Train Loss:0.0038908,  Val MRR:0.03119, 	fb2
+2023-05-02 07:38:26,681 - fb2 - [INFO] - [E:11| 300]: Train Loss:0.0038632,  Val MRR:0.03119, 	fb2
+2023-05-02 07:39:31,748 - fb2 - [INFO] - [E:11| 400]: Train Loss:0.0038869,  Val MRR:0.03119, 	fb2
+2023-05-02 07:40:36,924 - fb2 - [INFO] - [E:11| 500]: Train Loss:0.0038894,  Val MRR:0.03119, 	fb2
+2023-05-02 07:41:42,072 - fb2 - [INFO] - [E:11| 600]: Train Loss:0.0038788,  Val MRR:0.03119, 	fb2
+2023-05-02 07:42:47,120 - fb2 - [INFO] - [E:11| 700]: Train Loss:0.0038572,  Val MRR:0.03119, 	fb2
+2023-05-02 07:43:52,259 - fb2 - [INFO] - [E:11| 800]: Train Loss:0.0038689,  Val MRR:0.03119, 	fb2
+2023-05-02 07:44:57,400 - fb2 - [INFO] - [E:11| 900]: Train Loss:0.0038614,  Val MRR:0.03119, 	fb2
+2023-05-02 07:46:02,534 - fb2 - [INFO] - [E:11| 1000]: Train Loss:0.003855,  Val MRR:0.03119, 	fb2
+2023-05-02 07:47:07,616 - fb2 - [INFO] - [E:11| 1100]: Train Loss:0.0038534,  Val MRR:0.03119, 	fb2
+2023-05-02 07:47:52,272 - fb2 - [INFO] - [Epoch:11]:  Training Loss:0.003852
+
+2023-05-02 07:47:52,489 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 07:48:13,780 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 07:48:21,688 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 07:48:45,765 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 07:48:54,380 - fb2 - [INFO] - [Evaluating Epoch 11 valid]: 
+	MRR: Tail : 0.04712, Head : 0.00767, Avg : 0.0274
+
+2023-05-02 07:48:54,380 - fb2 - [INFO] - [Epoch 11]:  Training Loss: 0.0038525,  Valid MRR: 0.03119, 
+
+
+
+2023-05-02 07:48:55,042 - fb2 - [INFO] - [E:12| 0]: Train Loss:0.0037471,  Val MRR:0.03119, 	fb2
+2023-05-02 07:50:00,130 - fb2 - [INFO] - [E:12| 100]: Train Loss:0.0037951,  Val MRR:0.03119, 	fb2
+2023-05-02 07:51:05,193 - fb2 - [INFO] - [E:12| 200]: Train Loss:0.0038364,  Val MRR:0.03119, 	fb2
+2023-05-02 07:52:10,256 - fb2 - [INFO] - [E:12| 300]: Train Loss:0.003826,  Val MRR:0.03119, 	fb2
+2023-05-02 07:53:15,321 - fb2 - [INFO] - [E:12| 400]: Train Loss:0.0038137,  Val MRR:0.03119, 	fb2
+2023-05-02 07:54:20,366 - fb2 - [INFO] - [E:12| 500]: Train Loss:0.0038102,  Val MRR:0.03119, 	fb2
+2023-05-02 07:55:25,458 - fb2 - [INFO] - [E:12| 600]: Train Loss:0.0038274,  Val MRR:0.03119, 	fb2
+2023-05-02 07:56:30,547 - fb2 - [INFO] - [E:12| 700]: Train Loss:0.003833,  Val MRR:0.03119, 	fb2
+2023-05-02 07:57:35,610 - fb2 - [INFO] - [E:12| 800]: Train Loss:0.0038263,  Val MRR:0.03119, 	fb2
+2023-05-02 07:58:40,626 - fb2 - [INFO] - [E:12| 900]: Train Loss:0.00383,  Val MRR:0.03119, 	fb2
+2023-05-02 07:59:45,777 - fb2 - [INFO] - [E:12| 1000]: Train Loss:0.0038273,  Val MRR:0.03119, 	fb2
+2023-05-02 08:00:50,702 - fb2 - [INFO] - [E:12| 1100]: Train Loss:0.0038211,  Val MRR:0.03119, 	fb2
+2023-05-02 08:01:35,279 - fb2 - [INFO] - [Epoch:12]:  Training Loss:0.003815
+
+2023-05-02 08:01:35,494 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 08:01:56,640 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 08:02:04,508 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 08:02:28,485 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 08:02:37,052 - fb2 - [INFO] - [Evaluating Epoch 12 valid]: 
+	MRR: Tail : 0.05335, Head : 0.0075, Avg : 0.03043
+
+2023-05-02 08:02:37,052 - fb2 - [INFO] - [Epoch 12]:  Training Loss: 0.0038146,  Valid MRR: 0.03119, 
+
+
+
+2023-05-02 08:02:37,718 - fb2 - [INFO] - [E:13| 0]: Train Loss:0.0033164,  Val MRR:0.03119, 	fb2
+2023-05-02 08:03:42,647 - fb2 - [INFO] - [E:13| 100]: Train Loss:0.0037194,  Val MRR:0.03119, 	fb2
+2023-05-02 08:04:47,608 - fb2 - [INFO] - [E:13| 200]: Train Loss:0.003769,  Val MRR:0.03119, 	fb2
+2023-05-02 08:05:52,600 - fb2 - [INFO] - [E:13| 300]: Train Loss:0.0037668,  Val MRR:0.03119, 	fb2
+2023-05-02 08:06:57,520 - fb2 - [INFO] - [E:13| 400]: Train Loss:0.0037588,  Val MRR:0.03119, 	fb2
+2023-05-02 08:08:02,439 - fb2 - [INFO] - [E:13| 500]: Train Loss:0.0037793,  Val MRR:0.03119, 	fb2
+2023-05-02 08:09:07,395 - fb2 - [INFO] - [E:13| 600]: Train Loss:0.0037882,  Val MRR:0.03119, 	fb2
+2023-05-02 08:10:12,351 - fb2 - [INFO] - [E:13| 700]: Train Loss:0.0037882,  Val MRR:0.03119, 	fb2
+2023-05-02 08:11:18,698 - fb2 - [INFO] - [E:13| 800]: Train Loss:0.0037905,  Val MRR:0.03119, 	fb2
+2023-05-02 08:12:28,320 - fb2 - [INFO] - [E:13| 900]: Train Loss:0.0037888,  Val MRR:0.03119, 	fb2
+2023-05-02 08:13:38,365 - fb2 - [INFO] - [E:13| 1000]: Train Loss:0.0037947,  Val MRR:0.03119, 	fb2
+2023-05-02 08:14:47,505 - fb2 - [INFO] - [E:13| 1100]: Train Loss:0.0037969,  Val MRR:0.03119, 	fb2
+2023-05-02 08:15:34,543 - fb2 - [INFO] - [Epoch:13]:  Training Loss:0.003798
+
+2023-05-02 08:15:34,776 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 08:15:57,326 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 08:16:05,504 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 08:16:33,537 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 08:16:42,828 - fb2 - [INFO] - [Evaluating Epoch 13 valid]: 
+	MRR: Tail : 0.0532, Head : 0.00766, Avg : 0.03043
+
+2023-05-02 08:16:42,829 - fb2 - [INFO] - [Epoch 13]:  Training Loss: 0.0037978,  Valid MRR: 0.03119, 
+
+
+
+2023-05-02 08:16:43,534 - fb2 - [INFO] - [E:14| 0]: Train Loss:0.0041683,  Val MRR:0.03119, 	fb2
+2023-05-02 08:17:53,001 - fb2 - [INFO] - [E:14| 100]: Train Loss:0.0040882,  Val MRR:0.03119, 	fb2
+2023-05-02 08:19:02,369 - fb2 - [INFO] - [E:14| 200]: Train Loss:0.0039416,  Val MRR:0.03119, 	fb2
+2023-05-02 08:20:13,079 - fb2 - [INFO] - [E:14| 300]: Train Loss:0.0038661,  Val MRR:0.03119, 	fb2
+2023-05-02 08:21:23,507 - fb2 - [INFO] - [E:14| 400]: Train Loss:0.0038449,  Val MRR:0.03119, 	fb2
+2023-05-02 08:22:33,698 - fb2 - [INFO] - [E:14| 500]: Train Loss:0.0038566,  Val MRR:0.03119, 	fb2
+2023-05-02 08:23:39,541 - fb2 - [INFO] - [E:14| 600]: Train Loss:0.0038399,  Val MRR:0.03119, 	fb2
+2023-05-02 08:24:44,366 - fb2 - [INFO] - [E:14| 700]: Train Loss:0.0038305,  Val MRR:0.03119, 	fb2
+2023-05-02 08:25:49,869 - fb2 - [INFO] - [E:14| 800]: Train Loss:0.0038338,  Val MRR:0.03119, 	fb2
+2023-05-02 08:26:57,839 - fb2 - [INFO] - [E:14| 900]: Train Loss:0.0037821,  Val MRR:0.03119, 	fb2
+2023-05-02 08:28:03,354 - fb2 - [INFO] - [E:14| 1000]: Train Loss:0.0037456,  Val MRR:0.03119, 	fb2
+2023-05-02 08:29:10,644 - fb2 - [INFO] - [E:14| 1100]: Train Loss:0.0037076,  Val MRR:0.03119, 	fb2
+2023-05-02 08:29:57,907 - fb2 - [INFO] - [Epoch:14]:  Training Loss:0.003683
+
+2023-05-02 08:29:58,121 - fb2 - [INFO] - [Valid, Tail_Batch Step 0]	fb2
+2023-05-02 08:30:19,870 - fb2 - [INFO] - [Valid, Tail_Batch Step 100]	fb2
+2023-05-02 08:30:28,247 - fb2 - [INFO] - [Valid, Head_Batch Step 0]	fb2
+2023-05-02 08:30:53,528 - fb2 - [INFO] - [Valid, Head_Batch Step 100]	fb2
+2023-05-02 08:31:03,475 - fb2 - [INFO] - [Evaluating Epoch 14 valid]: 
+	MRR: Tail : 0.05567, Head : 0.00908, Avg : 0.03237
+
+2023-05-02 08:31:05,395 - fb2 - [INFO] - [Epoch 14]:  Training Loss: 0.0036829,  Valid MRR: 0.03237, 
+
+
+
+2023-05-02 08:31:06,199 - fb2 - [INFO] - [E:15| 0]: Train Loss:0.0039411,  Val MRR:0.03237, 	fb2
+2023-05-02 08:32:16,283 - fb2 - [INFO] - [E:15| 100]: Train Loss:0.0033314,  Val MRR:0.03237, 	fb2
+2023-05-02 08:33:25,619 - fb2 - [INFO] - [E:15| 200]: Train Loss:0.0034024,  Val MRR:0.03237, 	fb2
+2023-05-02 08:34:34,742 - fb2 - [INFO] - [E:15| 300]: Train Loss:0.0034511,  Val MRR:0.03237, 	fb2
+2023-05-02 08:35:44,359 - fb2 - [INFO] - [E:15| 400]: Train Loss:0.0034416,  Val MRR:0.03237, 	fb2
+2023-05-02 08:36:53,600 - fb2 - [INFO] - [E:15| 500]: Train Loss:0.0034373,  Val MRR:0.03237, 	fb2
+2023-05-02 08:38:02,489 - fb2 - [INFO] - [E:15| 600]: Train Loss:0.003452,  Val MRR:0.03237, 	fb2
+2023-05-02 08:39:11,979 - fb2 - [INFO] - [E:15| 700]: Train Loss:0.0034426,  Val MRR:0.03237, 	fb2
+2023-05-02 08:40:21,547 - fb2 - [INFO] - [E:15| 800]: Train Loss:0.0034303,  Val MRR:0.03237, 	fb2
+2023-05-02 08:41:30,473 - fb2 - [INFO] - [E:15| 900]: Train Loss:0.0034168,  Val MRR:0.03237, 	fb2

log/fourier_wn_400_400

@@ -0,0 +1,10 @@
+2023-05-02 03:31:33,715 - fourier_wn_400_400 - [INFO] - {'dataset': 'WN18RR', 'name': 'fourier_wn_400_400', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 600, 'ent_vec_dim': 600, 'rel_vec_dim': 300, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.1, 'drop': 0.2, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': True}
+2023-05-02 03:32:22,939 - fourier_wn_400_400 - [INFO] - {'dataset': 'WN18RR', 'name': 'fourier_wn_400_400', 'gpu': '1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 400, 'rel_vec_dim': 400, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.1, 'drop': 0.2, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/', 'test_only': True}
+2023-05-02 03:32:36,858 - fourier_wn_400_400 - [INFO] - [Test, Tail_Batch Step 0]	fourier_wn_400_400
+2023-05-02 03:32:55,123 - fourier_wn_400_400 - [INFO] - [Test, Head_Batch Step 0]	fourier_wn_400_400
+2023-05-02 03:33:15,785 - fourier_wn_400_400 - [INFO] - [Evaluating Epoch 0 test]: 
+	MRR: Tail : 0.4518, Head : 0.42163, Avg : 0.43672
+	MR: Tail : 5866.5, Head : 5955.9, Avg : 5911.2
+	Hit-1: Tail : 0.42884, Head : 0.39789, Avg : 0.41337
+	Hit-3: Tail : 0.46171, Head : 0.43235, Avg : 0.44703
+	Hit-10: Tail : 0.49394, Head : 0.46362, Avg : 0.47878

log/testrun_02f34acb

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+2023-04-28 18:22:54,643 - testrun_02f34acb - [INFO] - {'dataset': 'FB15k-237', 'name': 'testrun_02f34acb', 'gpu': '-1', 'train_strategy': 'one_to_n', 'opt': 'adam', 'neg_num': 1000, 'batch_size': 128, 'l2': 0.0, 'lr': 0.0001, 'max_epochs': 500, 'num_workers': 0, 'seed': 42, 'restore': False, 'lbl_smooth': 0.1, 'embed_dim': 400, 'ent_vec_dim': 600, 'rel_vec_dim': 300, 'bias': False, 'form': 'plain', 'k_w': 10, 'k_h': 20, 'num_filt': 96, 'ker_sz': 9, 'perm': 1, 'hid_drop': 0.5, 'feat_drop': 0.2, 'inp_drop': 0.2, 'drop_path': 0.1, 'drop': 0.2, 'in_channels': 1, 'out_channels': 32, 'filt_h': 1, 'filt_w': 9, 'image_h': 128, 'image_w': 128, 'patch_size': 8, 'mixer_dim': 256, 'expansion_factor': 4, 'expansion_factor_token': 0.5, 'mixer_depth': 16, 'mixer_dropout': 0.2, 'log_dir': './log/', 'config_dir': './config/'}