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# bert-base-uncased_news_ft

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8121
- Accuracy: 0.8433
- F1: 0.8433
- Precision: 0.8400

## 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: 0.0002
- train_batch_size: 128
- eval_batch_size: 128
- 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 | Accuracy | F1     | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
| No log        | 1.0   | 322  | 0.6194          | 0.8121   | 0.8121 | 0.8278    |
| 0.5669        | 2.0   | 644  | 0.5412          | 0.8365   | 0.8361 | 0.8354    |
| 0.5669        | 3.0   | 966  | 0.5892          | 0.8403   | 0.8405 | 0.8412    |
| 0.1391        | 4.0   | 1288 | 0.7124          | 0.8392   | 0.8392 | 0.8333    |
| 0.1391        | 5.0   | 1610 | 0.8121          | 0.8433   | 0.8433 | 0.8400    |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision"], "base_model": "bert-base-uncased", "model-index": [{"name": "bert-base-uncased_news_ft", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer base_model:bert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us