---
base_model: bert-base-uncased
datasets:
- glue
language: en
library_name: transformers
license: apache-2.0
metrics:
- accuracy
- f1
model_name: bert-base-uncased-mrpc
tags:
- text-classification
- glue
- mrpc
- bert
---

# bert-base-uncased fine-tuned on GLUE MRPC

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased)
on the [GLUE MRPC](https://huggingface.co/datasets/glue) dataset
(paraphrase detection — predict whether two sentences are equivalent).

## Evaluation results

On the MRPC validation set:

| Metric   | Value  |
|----------|--------|
| Accuracy | 0.8750 |
| F1       | 0.9119 |

## Training hyperparameters

- Optimizer: AdamW (lr=5e-5, weight_decay=0.01)
- LR scheduler: linear, 0 warmup steps
- Epochs: 3
- Batch size: 8
- Gradient clipping: max_norm=1.0
- Hardware: GPU (CUDA)

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

tokenizer = AutoTokenizer.from_pretrained("shimogerald/bert-base-uncased-mrpc")
model = AutoModelForSequenceClassification.from_pretrained("shimogerald/bert-base-uncased-mrpc")

inputs = tokenizer("The cat sat on the mat.", "A cat is sitting on a mat.",
                   return_tensors="pt", truncation=True)
with torch.no_grad():
    logits = model(**inputs).logits
pred = torch.argmax(logits, dim=1).item()
print("equivalent" if pred == 1 else "not equivalent")
```