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# vi_fin_news

This model is a fine-tuned version of [FPTAI/vibert-base-cased](https://huggingface.co/FPTAI/vibert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7477
- Accuracy: 0.9176

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2248        | 1.0   | 1150  | 0.2021          | 0.9172   |
| 0.182         | 2.0   | 2300  | 0.2216          | 0.9230   |
| 0.1301        | 3.0   | 3450  | 0.2681          | 0.9181   |
| 0.0985        | 4.0   | 4600  | 0.3468          | 0.9226   |
| 0.0651        | 5.0   | 5750  | 0.5141          | 0.9070   |
| 0.0332        | 6.0   | 6900  | 0.5732          | 0.9187   |
| 0.0266        | 7.0   | 8050  | 0.5991          | 0.9161   |
| 0.0129        | 8.0   | 9200  | 0.6872          | 0.9157   |
| 0.0095        | 9.0   | 10350 | 0.7212          | 0.9187   |
| 0.0023        | 10.0  | 11500 | 0.7477          | 0.9176   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.2
- Datasets 2.12.0
- Tokenizers 0.13.3

{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "FPTAI/vibert-base-cased", "model-index": [{"name": "vi_fin_news", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer base_model:FPTAI/vibert-base-cased autotrain_compatible endpoints_compatible region:us