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from pathlib import Path
import shutil
from datasets import load_dataset
from transformers import TrainingArguments
from span_marker import SpanMarkerModel, Trainer
from span_marker.model_card import SpanMarkerModelCardData
import os
os.environ["CODECARBON_LOG_LEVEL"] = "error"
def main() -> None:
# Load the dataset, ensure "tokens" and "ner_tags" columns, and get a list of labels
dataset_id = "tner/bionlp2004"
dataset_name = "BioNLP2004"
dataset = load_dataset(dataset_id).rename_column("tags", "ner_tags")
labels = [
"O",
"B-DNA",
"I-DNA",
"B-protein",
"I-protein",
"B-cell_type",
"I-cell_type",
"B-cell_line",
"I-cell_line",
"B-RNA",
"I-RNA",
]
# Initialize a SpanMarker model using a pretrained BERT-style encoder
encoder_id = "bert-base-uncased"
model_id = f"tomaarsen/span-marker-{encoder_id}-bionlp"
model = SpanMarkerModel.from_pretrained(
encoder_id,
labels=labels,
# SpanMarker hyperparameters:
model_max_length=256,
marker_max_length=128,
entity_max_length=8,
# Model card variables
model_card_data=SpanMarkerModelCardData(
model_id=model_id,
encoder_id=encoder_id,
dataset_name=dataset_name,
dataset_id=dataset_id,
license="other",
language="en",
),
)
# Prepare the 🤗 transformers training arguments
output_dir = Path("models") / model_id
args = TrainingArguments(
output_dir=output_dir,
run_name=model_id,
# Training Hyperparameters:
learning_rate=5e-5,
per_device_train_batch_size=32,
per_device_eval_batch_size=32,
num_train_epochs=3,
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=300,
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()
# Compute & save the metrics on the test set
metrics = trainer.evaluate(dataset["test"], metric_key_prefix="test")
trainer.save_metrics("test", metrics)
trainer.save_model(output_dir / "checkpoint-final")
shutil.copy2(__file__, output_dir / "checkpoint-final" / "train.py")
if __name__ == "__main__":
main()
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