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from pathlib import Path
import shutil
from datasets import load_dataset, concatenate_datasets
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 = "midas/inspec"
dataset_name = "Inspec"
dataset = load_dataset(dataset_id, "extraction")
dataset = dataset.rename_columns({"document": "tokens", "doc_bio_tags": "ner_tags"})
# Map string labels to integer labels instead
real_labels = ["O", "B", "I"]
dataset = dataset.map(lambda sample: {"ner_tags": [real_labels.index(tag) for tag in sample]}, input_columns="ner_tags")
# Use more readable labels
labels = ["O", "B-KEY", "I-KEY"]
# Train using train + validation set.
train_dataset = concatenate_datasets((dataset["train"], dataset["validation"]))
# Initialize a SpanMarker model using a pretrained BERT-style encoder
encoder_id = "bert-base-uncased"
model_id = "tomaarsen/span-marker_bert-base-uncased-keyphrase-inspec"
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="apache-2.0",
language="en",
),
)
# Prepare the 🤗 transformers training arguments
output_dir = Path("models") / model_id
args = TrainingArguments(
output_dir=output_dir,
hub_model_id=model_id,
run_name=f"bbu_keyphrase",
# 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="no",
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=train_dataset
)
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() |