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/bert-large-cased-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)