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# sentence-classifier

This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3291
- Precision: 0.9236
- Recall: 0.9217
- Accuracy: 0.9219
- F1: 0.9221

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
| No log        | 1.0   | 154  | 0.3536          | 0.8783    | 0.8745 | 0.8747   | 0.8753 |
| No log        | 2.0   | 308  | 0.2784          | 0.9132    | 0.9105 | 0.9105   | 0.9109 |
| No log        | 3.0   | 462  | 0.2928          | 0.9189    | 0.9160 | 0.9162   | 0.9165 |
| 0.3402        | 4.0   | 616  | 0.3098          | 0.9239    | 0.9223 | 0.9227   | 0.9228 |
| 0.3402        | 5.0   | 770  | 0.3291          | 0.9236    | 0.9217 | 0.9219   | 0.9221 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1

{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "accuracy", "f1"], "base_model": "dmis-lab/biobert-v1.1", "model-index": [{"name": "sentence-classifier", "results": []}]}
transformers tensorboard safetensors bert text-classification generated_from_trainer base_model:dmis-lab/biobert-v1.1 autotrain_compatible endpoints_compatible region:us