Without training, how do I use a test dataset to evaluate a pretrained model?
Hello
Assuming you’re using PyTorch, you can wrap your model inside a Trainer
and then call trainer.evaluate()
. An example (taken from here):
from transformers import TrainingArguments
training_args = TrainingArguments("test_trainer"),
import numpy as np
from datasets import load_metric
metric = load_metric("accuracy")
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return metric.compute(predictions=predictions, references=labels)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=small_train_dataset,
eval_dataset=small_eval_dataset,
compute_metrics=compute_metrics,
)
trainer.evaluate()
If you don’t want to use a Trainer
, check out the examples here and check the files ending with _no_trainer.py
.
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