Evaluate on MNLI:
from transformers import (
default_data_collator,
AutoTokenizer,
AutoModelForSequenceClassification,
Trainer,
)
from datasets import load_dataset
import functools
from utils import compute_metrics, preprocess_function
model_name = "George-Ogden/gpt2-medium-finetuned-mnli"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
trainer = Trainer(
model=model,
eval_dataset="mnli",
tokenizer=tokenizer,
compute_metrics=compute_metrics,
data_collator=default_data_collator,
)
raw_datasets = load_dataset(
"glue",
"mnli",
).map(functools.partial(preprocess_function, tokenizer), batched=True)
tasks = ["mnli", "mnli-mm"]
eval_datasets = [
raw_datasets["validation_matched"],
raw_datasets["validation_mismatched"],
]
for layers in reversed(range(model.num_layers + 1)):
for eval_dataset, task in zip(eval_datasets, tasks):
metrics = trainer.evaluate(eval_dataset=eval_dataset)
metrics["eval_samples"] = len(eval_dataset)
if task == "mnli-mm":
metrics = {k + "_mm": v for k, v in metrics.items()}
trainer.log_metrics(metrics)
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