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Add cli argument for configuring labels

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This commit is contained in:
Marianne Stecklina
2019-09-19 11:29:20 +02:00
committed by thomwolf
parent 3e9420add1
commit 99b189df6d
2 changed files with 24 additions and 17 deletions
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30
examples/run_ner.py
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@@ -55,7 +55,7 @@ def set_seed(args):
torch.cuda.manual_seed_all(args.seed)
def train(args, train_dataset, model, tokenizer, pad_token_label_id):
def train(args, train_dataset, model, tokenizer, labels, 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, 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, pad_token_label_id)
results = evaluate(args, model, tokenizer, labels, 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)
@@ -160,8 +160,7 @@ def train(args, train_dataset, model, tokenizer, 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)
@@ -179,8 +178,8 @@ def train(args, train_dataset, model, tokenizer, pad_token_label_id):
return global_step, tr_loss / global_step
def evaluate(args, model, tokenizer, pad_token_label_id, prefix=""):
eval_dataset = load_and_cache_examples(args, tokenizer, pad_token_label_id, evaluate=True)
def evaluate(args, model, tokenizer, labels, pad_token_label_id, prefix=""):
eval_dataset = load_and_cache_examples(args, tokenizer, labels, 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
@@ -220,7 +219,7 @@ def evaluate(args, model, tokenizer, pad_token_label_id, prefix=""):
eval_loss = eval_loss / nb_eval_steps
preds = np.argmax(preds, axis=2)
label_map = {i: label for i, label in enumerate(get_labels())}
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])]
@@ -245,7 +244,7 @@ def evaluate(args, model, tokenizer, pad_token_label_id, prefix=""):
return results
def load_and_cache_examples(args, tokenizer, pad_token_label_id, evaluate=False):
def load_and_cache_examples(args, tokenizer, labels, 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
@@ -258,9 +257,8 @@ def load_and_cache_examples(args, tokenizer, pad_token_label_id, evaluate=False)
features = torch.load(cached_features_file)
else:
logger.info("Creating features from dataset file at %s", args.data_dir)
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,
features = convert_examples_to_features(examples, labels, 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,
@@ -305,6 +303,8 @@ 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,
@@ -406,8 +406,8 @@ def main():
set_seed(args)
# Prepare CONLL-2003 task
label_list = get_labels()
num_labels = len(label_list)
labels = get_labels(args.labels)
num_labels = len(labels)
# 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
@@ -433,8 +433,8 @@ def main():
# Training
if args.do_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, pad_token_label_id)
train_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, evaluate=False)
global_step, tr_loss = train(args, train_dataset, model, tokenizer, labels, 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()
@@ -466,7 +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, pad_token_label_id, prefix=global_step)
result = evaluate(args, model, tokenizer, labels, 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)

11
examples/utils_ner.py
View File

@@ -202,5 +202,12 @@ def convert_examples_to_features(examples,
return features
def get_labels():
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
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"]