def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]: if isinstance(mode, Split): mode = mode.value file_path = os.path.join(data_dir, f"{mode}.txt") guid_index = 1 examples = [] with open(file_path, encoding="utf-8") as f: words = [] labels = [] for line in f: if line.startswith("-DOCSTART-") or line == "" or line == "\n": if words: examples.append( InputExample(guid=f"{mode}-{guid_index}", words=words, labels=labels)) guid_index += 1 words = [] labels = [] else: splits = line.split(" ") words.append(splits[0]) if len(splits) > 1: labels.append(splits[self.label_idx].replace("\n", "")) else: # Examples could have no label for mode = "test" labels.append("O") if words: examples.append( InputExample(guid=f"{mode}-{guid_index}", words=words, labels=labels)) return examples
def read_examples_from_line(line): guid_index = 1 examples = [] words = [] labels = [] for word in line: words.append(word) labels.append("O") if words: examples.append( InputExample(guid="{}-{}".format('predict', guid_index), words=words, labels=labels)) return examples
def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]: if isinstance(mode, Split): mode = mode.value file_path = os.path.join(data_dir, f"{mode}.txt") guid_index = 1 examples = [] with open(file_path, encoding="utf-8") as f: for sentence in parse_incr(f): words = [] labels = [] for token in sentence: words.append(token["form"]) labels.append(token["upos"]) assert len(words) == len(labels) if words: examples.append(InputExample(guid=f"{mode}-{guid_index}", words=words, labels=labels)) guid_index += 1 return examples
def set_data(self, tok_sents: List[List[str]]): """Expects a document given as a list of sentences where each sentence is tokenized already.""" examples = [] for guid, sent in enumerate(tok_sents): words = [x + "\n" for x in sent] labels = ["O" for _ in range(len(sent))] examples.append(InputExample(guid=f"pred-{guid}", words=words, labels=labels)) data = NerDataset( tokenizer=self.tokenizer, examples=examples, labels=["B", "O"], model_type="BertForTokenClassification", max_seq_length=256, mode=Split.pred ) self.data = data
def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""): #eval_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode=mode) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # multi-gpu evaluate if args.n_gpu > 1: model = torch.nn.DataParallel(model) # Eval! logger.info("***** Running evaluation %s *****", prefix) logger.info(" Batch size = %d", args.eval_batch_size) eval_loss = 0.0 nb_eval_steps = 0 preds = None out_label_ids = None model.eval() if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Load data features from cache or dataset file logger.info("Creating features from dataset file at %s", args.data_dir) examples = read_examples_from_file(args.data_dir, mode) print(len(examples)) print(examples[0]) # list of words in one document (sentence) out_label_listX=[] preds_listX=[] for example in tqdm(examples, desc="Evaluating"): max_length = 500 min_context = 128 l = len(example.words) #TODO number of segments for each word? word_tokens = tokenizer.tokenize(word) word_tokens_lengths=[len(tokenizer.tokenize(word)) for word in example.words] ws=windows(word_tokens_lengths, max_length, min_context) print(ws) text_examples=[] for start_all, start_content, end_content, end_all in ws: ex=InputExample(guid=example.guid, words=example.words[start_all:end_all], labels=example.labels[start_all:end_all]) text_examples.append(ex) # przed tym trzeba podzielić features = convert_examples_to_features( text_examples, labels, 512, #args.max_seq_length, tokenizer, cls_token_at_end=bool(args.model_type in ["xlnet"]), # xlnet has a cls token at the end cls_token=tokenizer.cls_token, cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0, sep_token=tokenizer.sep_token, sep_token_extra=bool(args.model_type in ["roberta"]), # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805 pad_on_left=bool(args.model_type in ["xlnet"]), # pad on the left for xlnet pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0], pad_token_segment_id=4 if args.model_type in ["xlnet"] else 0, pad_token_label_id=pad_token_label_id, ) if args.local_rank == 0 and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long) eval_dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # Eval! logger.info(" Num examples = %d", len(eval_dataset)) # batch = next(iter(eval_dataloader)) a = [] b = [] for batch, (start_all, start_content, end_content, end_all) in tqdm(zip(eval_dataloader, ws), desc="Evaluating"): preds = None out_label_ids = None batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]} if args.model_type != "distilbert": inputs["token_type_ids"] = ( batch[2] if args.model_type in ["bert", "xlnet"] else None ) # XLM and RoBERTa don"t use segment_ids outputs = model(**inputs) tmp_eval_loss, logits = outputs[:2] if args.n_gpu > 1: tmp_eval_loss = tmp_eval_loss.mean() # mean() to average on multi-gpu parallel evaluating eval_loss += tmp_eval_loss.item() nb_eval_steps += 1 if preds is None: preds = logits.detach().cpu().numpy() out_label_ids = inputs["labels"].detach().cpu().numpy() else: preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0) preds = np.argmax(preds, axis=2) label_map = {i: label for i, label in enumerate(labels)} out_label_list = [[] for _ in range(out_label_ids.shape[0])] preds_list = [[] for _ in range(out_label_ids.shape[0])] for i in range(out_label_ids.shape[0]): for j in range(out_label_ids.shape[1]): if out_label_ids[i, j] != pad_token_label_id: out_label_list[i].append(label_map[out_label_ids[i][j]]) preds_list[i].append(label_map[preds[i][j]]) #join for i in range(len(out_label_list)): a.extend(out_label_list[i][start_content-start_all:end_content-start_all]) b.extend(preds_list[i][start_content-start_all:end_content-start_all]) out_label_listX.append(a) preds_listX.append(b) # results = { # "loss": eval_loss, # "precision": precision_score(out_label_list, preds_list), # "recall": recall_score(out_label_list, preds_list), # "f1": f1_score(out_label_list, preds_list), # } eval_loss = eval_loss / nb_eval_steps try: results = { "loss": eval_loss, "precision": precision_score(out_label_listX, preds_listX), "recall": recall_score(out_label_listX, preds_listX), "f1": f1_score(out_label_listX, preds_listX), } logger.info("***** Eval results %s *****", prefix) for key in sorted(results.keys()): logger.info(" %s = %s", key, str(results[key])) return results, preds_listX except IndexError: #no output labels in file return {}, preds_listX