File size: 2,310 Bytes
4acb972 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
from datasets import load_dataset
from span_marker import SpanMarkerModel, Trainer
from transformers import TrainingArguments
def main() -> None:
# Load the dataset, ensure "tokens" and "ner_tags" columns, and get a list of labels
dataset = load_dataset("tner/ontonotes5")
dataset = dataset.rename_column("tags", "ner_tags")
labels = ['O', 'B-CARDINAL', 'B-DATE', 'I-DATE', 'B-PERSON', 'I-PERSON', 'B-NORP', 'B-GPE', 'I-GPE', 'B-LAW', 'I-LAW', 'B-ORG', 'I-ORG', 'B-PERCENT', 'I-PERCENT', 'B-ORDINAL', 'B-MONEY', 'I-MONEY', 'B-WORK_OF_ART', 'I-WORK_OF_ART', 'B-FAC', 'B-TIME', 'I-CARDINAL', 'B-LOC', 'B-QUANTITY', 'I-QUANTITY', 'I-NORP', 'I-LOC', 'B-PRODUCT', 'I-TIME', 'B-EVENT', 'I-EVENT', 'I-FAC', 'B-LANGUAGE', 'I-PRODUCT', 'I-ORDINAL', 'I-LANGUAGE']
# Initialize a SpanMarker model using a pretrained BERT-style encoder
model_name = "roberta-large"
model = SpanMarkerModel.from_pretrained(
model_name,
labels=labels,
# SpanMarker hyperparameters:
model_max_length=256,
marker_max_length=128,
entity_max_length=10,
)
# Prepare the 🤗 transformers training arguments
args = TrainingArguments(
output_dir="models/span_marker_roberta_large_ontonotes5",
# Training Hyperparameters:
learning_rate=1e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
gradient_accumulation_steps=2,
num_train_epochs=4,
weight_decay=0.01,
warmup_ratio=0.1,
bf16=True,
# Other Training parameters
logging_first_step=True,
logging_steps=50,
evaluation_strategy="steps",
save_strategy="steps",
eval_steps=1000,
dataloader_num_workers=2,
)
# Initialize the trainer using our model, training args & dataset, and train
trainer = Trainer(
model=model,
args=args,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("models/span_marker_roberta_large_ontonotes5/checkpoint-final")
# Compute & save the metrics on the test set
metrics = trainer.evaluate(dataset["test"], metric_key_prefix="test")
trainer.save_metrics("test", metrics)
if __name__ == "__main__":
main() |