File size: 1,373 Bytes
58c7106 a758773 bd66f28 a758773 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
---
license: apache-2.0
datasets:
- wikipedia
- bookcorpus
- glue
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
---
Evaluate on MNLI:
```python
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-base-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)
``` |