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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)
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