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span-marker-gelectra-large-germeval14

Token Classification

named-entity-recognition

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span-marker-gelectra-large-germeval14 / trainer.py

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scripts: add training and evaluation helpers

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	from datasets import load_dataset
	from transformers import TrainingArguments
	from span_marker import SpanMarkerModel, Trainer

	def perform_training(learning_rate: float, seed: int) -> None:
	# Load the dataset, ensure "tokens" and "ner_tags" columns, and get a list of labels
	dataset = load_dataset("gwlms/germeval2014")
	labels = dataset["train"].features["ner_tags"].feature.names

	# Initialize a SpanMarker model using a pretrained BERT-style encoder
	model_name = "deepset/gelectra-large"
	model = SpanMarkerModel.from_pretrained(
	model_name,
	labels=labels,
	# SpanMarker hyperparameters:
	model_max_length=256,
	marker_max_length=128,
	entity_max_length=8,
	)

	# Prepare the 🤗 transformers training arguments
	args = TrainingArguments(
	output_dir=f"./span_marker-gelectra-large-bs16-lr{learning_rate}-{seed}",
	# Training Hyperparameters:
	learning_rate=learning_rate,
	per_device_train_batch_size=16,
	per_device_eval_batch_size=16,
	num_train_epochs=3,
	weight_decay=0.01,
	warmup_ratio=0.1,
	fp16=True, # Replace `bf16` with `fp16` if your hardware can't use bf16.
	# Other Training parameters
	logging_first_step=True,
	logging_steps=50,
	evaluation_strategy="epoch",
	save_strategy="epoch",
	save_total_limit=11,
	dataloader_num_workers=2,
	seed=seed,
	load_best_model_at_end=True,
	)

	# 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(f"./span_marker-gelectra-large-bs16-lr{learning_rate}-{seed}/best-checkpoint")

	# 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__":
	for learning_rate in [5e-05]:
	for seed in [1,2,3,4,5]:
	perform_training(learning_rate, seed)