
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# test

This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4018
- Accuracy: 0.8207
- Precision: 0.8202
- Recall: 0.8207
- F1: 0.8202

## 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: 3e-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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.4749        | 1.0   | 417  | 0.4018          | 0.8207   | 0.8202    | 0.8207 | 0.8202 |
| 0.0976        | 2.0   | 834  | 0.4443          | 0.8189   | 0.8234    | 0.8189 | 0.8197 |
| 0.0061        | 3.0   | 1251 | 0.7378          | 0.8213   | 0.8233    | 0.8213 | 0.8219 |
| 0.3159        | 4.0   | 1668 | 0.9154          | 0.8094   | 0.8092    | 0.8094 | 0.8092 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract", "model-index": [{"name": "test", "results": []}]}
transformers tensorboard safetensors bert text-classification generated_from_trainer base_model:microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract license:mit autotrain_compatible endpoints_compatible region:us