pytorch bert text-classification dataset:glue transformers license:apache-2.0
.gitattributes README.md config.json pytorch_model.bin special_tokens_map.json tokenizer.json tokenizer_config.json vocab.txt
---
license: apache-2.0
datasets:
- glue
metrics:
- accuracy
- f1

model-index:
- name: finetuned-bert
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: mrpc
    metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8651960784313726
      - name: F1
        type: f1
        value: 0.9050086355785838
---

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# finetuned-bert

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3898
- Accuracy: 0.8652
- F1: 0.9050

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5549        | 1.0   | 230  | 0.3806          | 0.8333   | 0.8870 |
| 0.3343        | 2.0   | 460  | 0.3620          | 0.8382   | 0.8896 |
| 0.181         | 3.0   | 690  | 0.3898          | 0.8652   | 0.9050 |


### Framework versions

- Transformers 4.7.0.dev0
- Pytorch 1.8.1+cu111
- Datasets 1.8.0.dev0
- Tokenizers 0.10.1
