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Implement fine-tuning BERT on CoNLL-2003 named entity recognition task

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Marianne Stecklina
2019-09-17 15:18:57 +02:00
committed by thomwolf
parent 5adb39e757
commit 383ef96747
2 changed files with 30 additions and 64 deletions
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64
examples/run_ner.py
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@@ -55,7 +55,7 @@ def set_seed(args):
torch.cuda.manual_seed_all(args.seed) 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 """ """ Train the model """
if args.local_rank in [-1, 0]: if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter() 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: if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics # Log metrics
if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well 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(): for key, value in results.items():
tb_writer.add_scalar("eval_{}".format(key), value, global_step) tb_writer.add_scalar("eval_{}".format(key), value, global_step)
tb_writer.add_scalar("lr", scheduler.get_lr()[0], 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)) output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
if not os.path.exists(output_dir): if not os.path.exists(output_dir):
os.makedirs(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) model_to_save.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, "training_args.bin")) torch.save(args, os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to %s", output_dir) 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 return global_step, tr_loss / global_step
def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""): def evaluate(args, model, tokenizer, pad_token_label_id, prefix=""):
eval_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode=mode) 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) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly # 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 eval_loss = eval_loss / nb_eval_steps
preds = np.argmax(preds, axis=2) 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])] out_label_list = [[] for _ in range(out_label_ids.shape[0])]
preds_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()): for key in sorted(results.keys()):
logger.info(" %s = %s", key, str(results[key])) 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: 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 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 # 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(), list(filter(None, args.model_name_or_path.split("/"))).pop(),
str(args.max_seq_length))) str(args.max_seq_length)))
if os.path.exists(cached_features_file): 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) features = torch.load(cached_features_file)
else: else:
logger.info("Creating features from dataset file at %s", args.data_dir) logger.info("Creating features from dataset file at %s", args.data_dir)
examples = read_examples_from_file(args.data_dir, mode) label_list = get_labels()
features = convert_examples_to_features(examples, labels, args.max_seq_length, tokenizer, 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"]), cls_token_at_end=bool(args.model_type in ["xlnet"]),
# xlnet has a cls token at the end # xlnet has a cls token at the end
cls_token=tokenizer.cls_token, cls_token=tokenizer.cls_token,
@@ -303,8 +305,6 @@ def main():
help="The output directory where the model predictions and checkpoints will be written.") help="The output directory where the model predictions and checkpoints will be written.")
## Other parameters ## 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, parser.add_argument("--config_name", default="", type=str,
help="Pretrained config name or path if not the same as model_name") help="Pretrained config name or path if not the same as model_name")
parser.add_argument("--tokenizer_name", default="", type=str, parser.add_argument("--tokenizer_name", default="", type=str,
@@ -318,8 +318,6 @@ def main():
help="Whether to run training.") help="Whether to run training.")
parser.add_argument("--do_eval", action="store_true", parser.add_argument("--do_eval", action="store_true",
help="Whether to run eval on the dev set.") 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", parser.add_argument("--evaluate_during_training", action="store_true",
help="Whether to run evaluation during training at each logging step.") help="Whether to run evaluation during training at each logging step.")
parser.add_argument("--do_lower_case", action="store_true", parser.add_argument("--do_lower_case", action="store_true",
@@ -408,8 +406,8 @@ def main():
set_seed(args) set_seed(args)
# Prepare CONLL-2003 task # Prepare CONLL-2003 task
labels = get_labels(args.labels) label_list = get_labels()
num_labels = len(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 # 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 pad_token_label_id = CrossEntropyLoss().ignore_index
@@ -435,8 +433,8 @@ def main():
# Training # Training
if args.do_train: if args.do_train:
train_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode="train") train_dataset = load_and_cache_examples(args, tokenizer, pad_token_label_id, evaluate=False)
global_step, tr_loss = train(args, train_dataset, model, tokenizer, labels, pad_token_label_id) 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) 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() # 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 "" global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
model = model_class.from_pretrained(checkpoint) model = model_class.from_pretrained(checkpoint)
model.to(args.device) 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: if global_step:
result = {"{}_{}".format(global_step, k): v for k, v in result.items()} result = {"{}_{}".format(global_step, k): v for k, v in result.items()}
results.update(result) results.update(result)
@@ -477,32 +475,6 @@ def main():
for key in sorted(results.keys()): for key in sorted(results.keys()):
writer.write("{} = {}\n".format(key, str(results[key]))) 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 return results

30
examples/utils_ner.py
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@@ -51,8 +51,13 @@ class InputFeatures(object):
self.label_ids = label_ids self.label_ids = label_ids
def read_examples_from_file(data_dir, mode): def read_examples_from_file(data_dir, evaluate=False):
file_path = os.path.join(data_dir, "{}.txt".format(mode)) 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 guid_index = 1
examples = [] examples = []
with open(file_path, encoding="utf-8") as f: 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: for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n": if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if words: if words:
examples.append(InputExample(guid="{}-{}".format(mode, guid_index), examples.append(InputExample(guid="{}-{}".format(guid_prefix, guid_index),
words=words, words=words,
labels=labels)) labels=labels))
guid_index += 1 guid_index += 1
@@ -70,13 +75,9 @@ def read_examples_from_file(data_dir, mode):
else: else:
splits = line.split(" ") splits = line.split(" ")
words.append(splits[0]) words.append(splits[0])
if len(splits) > 1: labels.append(splits[-1][:-1])
labels.append(splits[-1].replace("\n", ""))
else:
# Examples could have no label for mode = "test"
labels.append("O")
if words: 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, words=words,
labels=labels)) labels=labels))
return examples return examples
@@ -201,12 +202,5 @@ def convert_examples_to_features(examples,
return features return features
def get_labels(path): def get_labels():
if path: return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
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"]