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# bert_gec_detect

This model was trained from scratch on the QALB GEC dataset for a binary classification task, which is classifying whether a generated/given text is grammatically sound/correct. 

## 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: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2169        | 1.0   | 1864  | 0.2219          | 0.9330   |
| 0.1933        | 2.0   | 3728  | 0.2413          | 0.9321   |
| 0.1632        | 3.0   | 5592  | 0.2905          | 0.9295   |
| 0.1323        | 4.0   | 7456  | 0.2807          | 0.9346   |
| 0.1168        | 5.0   | 9320  | 0.3174          | 0.9334   |
| 0.1018        | 6.0   | 11184 | 0.3848          | 0.9346   |
| 0.0688        | 7.0   | 13048 | 0.4739          | 0.9325   |
| 0.0585        | 8.0   | 14912 | 0.4750          | 0.9347   |
| 0.0545        | 9.0   | 16776 | 0.4894          | 0.9337   |
| 0.0497        | 10.0  | 18640 | 0.5135          | 0.9349   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.15.0

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert_gec_detect", "results": []}]}
transformers tensorboard safetensors bert text-classification generated_from_trainer autotrain_compatible endpoints_compatible region:us