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from datasets import load_dataset
from span_marker import SpanMarkerModel, Trainer
from transformers import TrainingArguments
def main() -> None:
# Load the dataset, ensure "tokens", "ner_tags", "document_id" and "sentence_id" columns,
# and get a list of labels
dataset = load_dataset("tomaarsen/conll2003")
labels = dataset["train"].features["ner_tags"].feature.names
# Initialize a SpanMarker model using a pretrained BERT-style encoder
model_name = "xlm-roberta-large"
model = SpanMarkerModel.from_pretrained(
model_name,
labels=labels,
# SpanMarker hyperparameters:
model_max_length=512,
marker_max_length=128,
entity_max_length=8,
)
# Prepare the 🤗 transformers training arguments
args = TrainingArguments(
output_dir="models/span_marker_xlm_roberta_large_conll03_doc_context",
# Training Hyperparameters:
learning_rate=1e-5,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
gradient_accumulation_steps=2,
num_train_epochs=3,
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_xlm_roberta_large_conll03_doc_context/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() |