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ontonotesv5.py
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ontonotesv5.py
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from datasets import load_dataset
from transformers import TrainingArguments
from span_marker import SpanMarkerModel, SpanMarkerModelCardData, Trainer
def main() -> None:
# Load the dataset, ensure "tokens" and "ner_tags" columns, and get a list of labels
dataset_id = "tner/ontonotes5"
dataset_name = "OntoNotes v5"
dataset = load_dataset(dataset_id)
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
encoder_id = "roberta-large"
model = SpanMarkerModel.from_pretrained(
encoder_id,
labels=labels,
# SpanMarker hyperparameters:
model_max_length=256,
marker_max_length=128,
entity_max_length=10,
# Model card arguments
model_card_data=SpanMarkerModelCardData(
model_id=f"tomaarsen/span-marker-{encoder_id}-ontonotes5",
encoder_id=encoder_id,
dataset_name=dataset_name,
dataset_id=dataset_id,
license="other",
language="en",
),
)
# 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, # Replace `bf16` with `fp16` if your hardware can't use bf16.
# Other Training parameters
logging_first_step=True,
logging_steps=50,
evaluation_strategy="steps",
save_strategy="steps",
eval_steps=1000,
save_total_limit=2,
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()