From 383ef9674736ed6c97296ab7e2d2f05b2c41f3eb Mon Sep 17 00:00:00 2001 From: Marianne Stecklina Date: Tue, 17 Sep 2019 15:18:57 +0200 Subject: [PATCH] Implement fine-tuning BERT on CoNLL-2003 named entity recognition task --- examples/run_ner.py | 64 ++++++++++++------------------------------- examples/utils_ner.py | 30 ++++++++------------ 2 files changed, 30 insertions(+), 64 deletions(-) diff --git a/examples/run_ner.py b/examples/run_ner.py index 6c6b0f8336..ce048ade18 100644 --- a/examples/run_ner.py +++ b/examples/run_ner.py @@ -55,7 +55,7 @@ def set_seed(args): torch.cuda.manual_seed_all(args.seed) -def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id): +def train(args, train_dataset, model, tokenizer, pad_token_label_id): """ Train the model """ if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() @@ -148,7 +148,7 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id): if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: # Log metrics if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well - results, _ = evaluate(args, model, tokenizer, labels, pad_token_label_id) + results = evaluate(args, model, tokenizer, pad_token_label_id) for key, value in results.items(): tb_writer.add_scalar("eval_{}".format(key), value, global_step) tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step) @@ -160,7 +160,8 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id): output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) - model_to_save = model.module if hasattr(model, "module") else model # Take care of distributed/parallel training + model_to_save = model.module if hasattr(model, + "module") else model # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) @@ -178,8 +179,8 @@ def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id): return global_step, tr_loss / global_step -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) +def evaluate(args, model, tokenizer, pad_token_label_id, prefix=""): + eval_dataset = load_and_cache_examples(args, tokenizer, pad_token_label_id, evaluate=True) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly @@ -219,7 +220,7 @@ def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix="" eval_loss = eval_loss / nb_eval_steps preds = np.argmax(preds, axis=2) - label_map = {i: label for i, label in enumerate(labels)} + label_map = {i: label for i, label in enumerate(get_labels())} out_label_list = [[] for _ in range(out_label_ids.shape[0])] preds_list = [[] for _ in range(out_label_ids.shape[0])] @@ -241,15 +242,15 @@ def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix="" for key in sorted(results.keys()): logger.info(" %s = %s", key, str(results[key])) - return results, preds_list + return results -def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode): +def load_and_cache_examples(args, tokenizer, pad_token_label_id, evaluate=False): 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 - cached_features_file = os.path.join(args.data_dir, "cached_{}_{}_{}".format(mode, + cached_features_file = os.path.join(args.data_dir, "cached_{}_{}_{}".format("dev" if evaluate else "train", list(filter(None, args.model_name_or_path.split("/"))).pop(), str(args.max_seq_length))) if os.path.exists(cached_features_file): @@ -257,8 +258,9 @@ def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode): features = torch.load(cached_features_file) else: logger.info("Creating features from dataset file at %s", args.data_dir) - examples = read_examples_from_file(args.data_dir, mode) - features = convert_examples_to_features(examples, labels, args.max_seq_length, tokenizer, + label_list = get_labels() + examples = read_examples_from_file(args.data_dir, evaluate=evaluate) + features = convert_examples_to_features(examples, label_list, 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, @@ -303,8 +305,6 @@ def main(): help="The output directory where the model predictions and checkpoints will be written.") ## Other parameters - parser.add_argument("--labels", default="", type=str, - help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.") parser.add_argument("--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name") parser.add_argument("--tokenizer_name", default="", type=str, @@ -318,8 +318,6 @@ def main(): help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") - parser.add_argument("--do_predict", action="store_true", - help="Whether to run predictions on the test set.") parser.add_argument("--evaluate_during_training", action="store_true", help="Whether to run evaluation during training at each logging step.") parser.add_argument("--do_lower_case", action="store_true", @@ -408,8 +406,8 @@ def main(): set_seed(args) # Prepare CONLL-2003 task - labels = get_labels(args.labels) - num_labels = len(labels) + label_list = get_labels() + num_labels = len(label_list) # Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later pad_token_label_id = CrossEntropyLoss().ignore_index @@ -435,8 +433,8 @@ def main(): # Training if args.do_train: - train_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode="train") - global_step, tr_loss = train(args, train_dataset, model, tokenizer, labels, pad_token_label_id) + train_dataset = load_and_cache_examples(args, tokenizer, pad_token_label_id, evaluate=False) + global_step, tr_loss = train(args, train_dataset, model, tokenizer, pad_token_label_id) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained() @@ -468,7 +466,7 @@ def main(): global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" model = model_class.from_pretrained(checkpoint) model.to(args.device) - result, _ = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="dev", prefix=global_step) + result = evaluate(args, model, tokenizer, pad_token_label_id, prefix=global_step) if global_step: result = {"{}_{}".format(global_step, k): v for k, v in result.items()} results.update(result) @@ -477,32 +475,6 @@ def main(): for key in sorted(results.keys()): writer.write("{} = {}\n".format(key, str(results[key]))) - if args.do_predict and args.local_rank in [-1, 0]: - tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case) - model = model_class.from_pretrained(args.output_dir) - model.to(args.device) - result, predictions = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="test") - # Save results - output_test_results_file = os.path.join(args.output_dir, "test_results.txt") - with open(output_test_results_file, "w") as writer: - for key in sorted(result.keys()): - writer.write("{} = {}\n".format(key, str(result[key]))) - # Save predictions - output_test_predictions_file = os.path.join(args.output_dir, "test_predictions.txt") - with open(output_test_predictions_file, "w") as writer: - with open(os.path.join(args.data_dir, "test.txt"), "r") as f: - example_id = 0 - for line in f: - if line.startswith("-DOCSTART-") or line == "" or line == "\n": - writer.write(line) - if not predictions[example_id]: - example_id += 1 - elif predictions[example_id]: - output_line = line.split()[0] + " " + predictions[example_id].pop(0) + "\n" - writer.write(output_line) - else: - logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]) - return results diff --git a/examples/utils_ner.py b/examples/utils_ner.py index c20d7b0d1f..0d3af3e061 100644 --- a/examples/utils_ner.py +++ b/examples/utils_ner.py @@ -51,8 +51,13 @@ class InputFeatures(object): self.label_ids = label_ids -def read_examples_from_file(data_dir, mode): - file_path = os.path.join(data_dir, "{}.txt".format(mode)) +def read_examples_from_file(data_dir, evaluate=False): + if evaluate: + file_path = os.path.join(data_dir, "dev.txt") + guid_prefix = "dev" + else: + file_path = os.path.join(data_dir, "train.txt") + guid_prefix = "train" guid_index = 1 examples = [] with open(file_path, encoding="utf-8") as f: @@ -61,7 +66,7 @@ def read_examples_from_file(data_dir, mode): for line in f: if line.startswith("-DOCSTART-") or line == "" or line == "\n": if words: - examples.append(InputExample(guid="{}-{}".format(mode, guid_index), + examples.append(InputExample(guid="{}-{}".format(guid_prefix, guid_index), words=words, labels=labels)) guid_index += 1 @@ -70,13 +75,9 @@ def read_examples_from_file(data_dir, mode): else: splits = line.split(" ") words.append(splits[0]) - if len(splits) > 1: - labels.append(splits[-1].replace("\n", "")) - else: - # Examples could have no label for mode = "test" - labels.append("O") + labels.append(splits[-1][:-1]) if words: - examples.append(InputExample(guid="%s-%d".format(mode, guid_index), + examples.append(InputExample(guid="%s-%d".format(guid_prefix, guid_index), words=words, labels=labels)) return examples @@ -201,12 +202,5 @@ def convert_examples_to_features(examples, return features -def get_labels(path): - if path: - with open(path, "r") as f: - labels = f.read().splitlines() - if "O" not in labels: - labels = ["O"] + labels - return labels - else: - return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"] +def get_labels(): + return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]