---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-sst-2-64-13-smoothed
  results: []
---

<!-- 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. -->

# roberta-base-sst-2-64-13-smoothed

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6071
- Accuracy: 0.8672

## 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: 1e-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
- lr_scheduler_warmup_steps: 50
- num_epochs: 75
- label_smoothing_factor: 0.45

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 4    | 0.6941          | 0.5      |
| No log        | 2.0   | 8    | 0.6939          | 0.5      |
| 0.694         | 3.0   | 12   | 0.6936          | 0.5      |
| 0.694         | 4.0   | 16   | 0.6932          | 0.5      |
| 0.6948        | 5.0   | 20   | 0.6929          | 0.5      |
| 0.6948        | 6.0   | 24   | 0.6925          | 0.5      |
| 0.6948        | 7.0   | 28   | 0.6922          | 0.5469   |
| 0.6948        | 8.0   | 32   | 0.6919          | 0.6719   |
| 0.6948        | 9.0   | 36   | 0.6914          | 0.7266   |
| 0.6908        | 10.0  | 40   | 0.6907          | 0.75     |
| 0.6908        | 11.0  | 44   | 0.6894          | 0.6719   |
| 0.6908        | 12.0  | 48   | 0.6866          | 0.6328   |
| 0.6835        | 13.0  | 52   | 0.6789          | 0.7891   |
| 0.6835        | 14.0  | 56   | 0.6514          | 0.8828   |
| 0.637         | 15.0  | 60   | 0.6004          | 0.875    |
| 0.637         | 16.0  | 64   | 0.6097          | 0.8984   |
| 0.637         | 17.0  | 68   | 0.6147          | 0.8516   |
| 0.5653        | 18.0  | 72   | 0.5973          | 0.8672   |
| 0.5653        | 19.0  | 76   | 0.6056          | 0.875    |
| 0.544         | 20.0  | 80   | 0.6077          | 0.875    |
| 0.544         | 21.0  | 84   | 0.5947          | 0.8672   |
| 0.544         | 22.0  | 88   | 0.6029          | 0.8828   |
| 0.5384        | 23.0  | 92   | 0.6067          | 0.8828   |
| 0.5384        | 24.0  | 96   | 0.5998          | 0.8828   |
| 0.5361        | 25.0  | 100  | 0.5978          | 0.8906   |
| 0.5361        | 26.0  | 104  | 0.6004          | 0.875    |
| 0.5361        | 27.0  | 108  | 0.6055          | 0.8672   |
| 0.5364        | 28.0  | 112  | 0.6064          | 0.8672   |
| 0.5364        | 29.0  | 116  | 0.5991          | 0.8906   |
| 0.5364        | 30.0  | 120  | 0.5973          | 0.8906   |
| 0.5364        | 31.0  | 124  | 0.6019          | 0.8828   |
| 0.5364        | 32.0  | 128  | 0.6085          | 0.8594   |
| 0.5358        | 33.0  | 132  | 0.6069          | 0.8672   |
| 0.5358        | 34.0  | 136  | 0.6075          | 0.8594   |
| 0.5357        | 35.0  | 140  | 0.6022          | 0.8828   |
| 0.5357        | 36.0  | 144  | 0.5980          | 0.8906   |
| 0.5357        | 37.0  | 148  | 0.5983          | 0.8984   |
| 0.5359        | 38.0  | 152  | 0.5962          | 0.8984   |
| 0.5359        | 39.0  | 156  | 0.5965          | 0.8984   |
| 0.5358        | 40.0  | 160  | 0.6007          | 0.8984   |
| 0.5358        | 41.0  | 164  | 0.6010          | 0.8984   |
| 0.5358        | 42.0  | 168  | 0.5975          | 0.8984   |
| 0.5355        | 43.0  | 172  | 0.5975          | 0.8906   |
| 0.5355        | 44.0  | 176  | 0.6012          | 0.8906   |
| 0.5354        | 45.0  | 180  | 0.6027          | 0.8828   |
| 0.5354        | 46.0  | 184  | 0.6027          | 0.8828   |
| 0.5354        | 47.0  | 188  | 0.6018          | 0.8828   |
| 0.5355        | 48.0  | 192  | 0.6070          | 0.875    |
| 0.5355        | 49.0  | 196  | 0.6090          | 0.8672   |
| 0.5352        | 50.0  | 200  | 0.6090          | 0.8672   |
| 0.5352        | 51.0  | 204  | 0.6079          | 0.8672   |
| 0.5352        | 52.0  | 208  | 0.6072          | 0.8906   |
| 0.5354        | 53.0  | 212  | 0.6063          | 0.8906   |
| 0.5354        | 54.0  | 216  | 0.6045          | 0.8672   |
| 0.5353        | 55.0  | 220  | 0.6094          | 0.8672   |
| 0.5353        | 56.0  | 224  | 0.6167          | 0.8438   |
| 0.5353        | 57.0  | 228  | 0.6176          | 0.8516   |
| 0.5353        | 58.0  | 232  | 0.6188          | 0.8516   |
| 0.5353        | 59.0  | 236  | 0.6204          | 0.8516   |
| 0.5353        | 60.0  | 240  | 0.6218          | 0.8438   |
| 0.5353        | 61.0  | 244  | 0.6222          | 0.8516   |
| 0.5353        | 62.0  | 248  | 0.6208          | 0.8516   |
| 0.5352        | 63.0  | 252  | 0.6194          | 0.8516   |
| 0.5352        | 64.0  | 256  | 0.6167          | 0.8438   |
| 0.5351        | 65.0  | 260  | 0.6144          | 0.8438   |
| 0.5351        | 66.0  | 264  | 0.6128          | 0.8516   |
| 0.5351        | 67.0  | 268  | 0.6117          | 0.8594   |
| 0.5349        | 68.0  | 272  | 0.6112          | 0.8594   |
| 0.5349        | 69.0  | 276  | 0.6114          | 0.8672   |
| 0.5351        | 70.0  | 280  | 0.6089          | 0.8672   |
| 0.5351        | 71.0  | 284  | 0.6077          | 0.875    |
| 0.5351        | 72.0  | 288  | 0.6073          | 0.875    |
| 0.5352        | 73.0  | 292  | 0.6072          | 0.8672   |
| 0.5352        | 74.0  | 296  | 0.6071          | 0.8672   |
| 0.5355        | 75.0  | 300  | 0.6071          | 0.8672   |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.4.0
- Tokenizers 0.13.3