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import sys
from datasets import load_dataset
from transformers import TrainingArguments
from span_marker import SpanMarkerModel, Trainer
# 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 = sys.argv[1]
model = SpanMarkerModel.from_pretrained(
model_name,
labels=labels,
# SpanMarker hyperparameters:
model_max_length=256,
marker_max_length=128,
entity_max_length=8,
)
args = TrainingArguments(
output_dir="/tmp",
per_device_eval_batch_size=64,
)
# 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"],
)
print("Evaluating on development set...")
dev_metrics = trainer.evaluate(dataset["validation"], metric_key_prefix="eval")
print(dev_metrics)
print("Evaluating on test set...")
test_metrics = trainer.evaluate(dataset["test"], metric_key_prefix="test")
print(test_metrics)
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