Create README.md

README.md

@@ -0,0 +1,57 @@
+---
+license: mit
+datasets:
+- bookcorpus
+- wikipedia
+language:
+- en
+metrics:
+- glue
+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/roberta-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)
+```