---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-sst-2-32-13-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-2-32-13-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: 0.6023
- Accuracy: 0.8906

## 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   | 2    | 0.6943          | 0.5      |
| No log        | 2.0   | 4    | 0.6942          | 0.5      |
| No log        | 3.0   | 6    | 0.6941          | 0.5      |
| No log        | 4.0   | 8    | 0.6939          | 0.5      |
| 0.695         | 5.0   | 10   | 0.6937          | 0.5      |
| 0.695         | 6.0   | 12   | 0.6935          | 0.5      |
| 0.695         | 7.0   | 14   | 0.6933          | 0.5      |
| 0.695         | 8.0   | 16   | 0.6932          | 0.5      |
| 0.695         | 9.0   | 18   | 0.6930          | 0.5      |
| 0.6959        | 10.0  | 20   | 0.6928          | 0.5      |
| 0.6959        | 11.0  | 22   | 0.6927          | 0.5156   |
| 0.6959        | 12.0  | 24   | 0.6926          | 0.6094   |
| 0.6959        | 13.0  | 26   | 0.6925          | 0.5781   |
| 0.6959        | 14.0  | 28   | 0.6923          | 0.5625   |
| 0.6919        | 15.0  | 30   | 0.6922          | 0.5625   |
| 0.6919        | 16.0  | 32   | 0.6920          | 0.5625   |
| 0.6919        | 17.0  | 34   | 0.6917          | 0.6094   |
| 0.6919        | 18.0  | 36   | 0.6913          | 0.5938   |
| 0.6919        | 19.0  | 38   | 0.6908          | 0.6406   |
| 0.6896        | 20.0  | 40   | 0.6902          | 0.7188   |
| 0.6896        | 21.0  | 42   | 0.6892          | 0.7812   |
| 0.6896        | 22.0  | 44   | 0.6878          | 0.6719   |
| 0.6896        | 23.0  | 46   | 0.6855          | 0.7344   |
| 0.6896        | 24.0  | 48   | 0.6816          | 0.7344   |
| 0.6745        | 25.0  | 50   | 0.6737          | 0.7812   |
| 0.6745        | 26.0  | 52   | 0.6571          | 0.8438   |
| 0.6745        | 27.0  | 54   | 0.6290          | 0.8438   |
| 0.6745        | 28.0  | 56   | 0.6161          | 0.8438   |
| 0.6745        | 29.0  | 58   | 0.6202          | 0.8594   |
| 0.5833        | 30.0  | 60   | 0.6190          | 0.875    |
| 0.5833        | 31.0  | 62   | 0.6210          | 0.8594   |
| 0.5833        | 32.0  | 64   | 0.6147          | 0.8594   |
| 0.5833        | 33.0  | 66   | 0.6056          | 0.9062   |
| 0.5833        | 34.0  | 68   | 0.6082          | 0.9062   |
| 0.5433        | 35.0  | 70   | 0.6194          | 0.875    |
| 0.5433        | 36.0  | 72   | 0.6035          | 0.9062   |
| 0.5433        | 37.0  | 74   | 0.5986          | 0.8906   |
| 0.5433        | 38.0  | 76   | 0.5970          | 0.8906   |
| 0.5433        | 39.0  | 78   | 0.6038          | 0.8906   |
| 0.5402        | 40.0  | 80   | 0.6061          | 0.8906   |
| 0.5402        | 41.0  | 82   | 0.6018          | 0.8906   |
| 0.5402        | 42.0  | 84   | 0.6013          | 0.9062   |
| 0.5402        | 43.0  | 86   | 0.6018          | 0.8906   |
| 0.5402        | 44.0  | 88   | 0.6086          | 0.8594   |
| 0.5384        | 45.0  | 90   | 0.6100          | 0.8594   |
| 0.5384        | 46.0  | 92   | 0.6044          | 0.8906   |
| 0.5384        | 47.0  | 94   | 0.6022          | 0.8906   |
| 0.5384        | 48.0  | 96   | 0.6007          | 0.8906   |
| 0.5384        | 49.0  | 98   | 0.6003          | 0.8906   |
| 0.5368        | 50.0  | 100  | 0.6013          | 0.8906   |
| 0.5368        | 51.0  | 102  | 0.6012          | 0.8906   |
| 0.5368        | 52.0  | 104  | 0.6006          | 0.8906   |
| 0.5368        | 53.0  | 106  | 0.6005          | 0.8906   |
| 0.5368        | 54.0  | 108  | 0.6011          | 0.8906   |
| 0.537         | 55.0  | 110  | 0.6013          | 0.8906   |
| 0.537         | 56.0  | 112  | 0.6014          | 0.8906   |
| 0.537         | 57.0  | 114  | 0.6013          | 0.9062   |
| 0.537         | 58.0  | 116  | 0.6011          | 0.9062   |
| 0.537         | 59.0  | 118  | 0.6006          | 0.9062   |
| 0.5364        | 60.0  | 120  | 0.5999          | 0.9062   |
| 0.5364        | 61.0  | 122  | 0.5994          | 0.9062   |
| 0.5364        | 62.0  | 124  | 0.5991          | 0.9062   |
| 0.5364        | 63.0  | 126  | 0.5992          | 0.9062   |
| 0.5364        | 64.0  | 128  | 0.5996          | 0.9062   |
| 0.5362        | 65.0  | 130  | 0.6000          | 0.9062   |
| 0.5362        | 66.0  | 132  | 0.6004          | 0.9062   |
| 0.5362        | 67.0  | 134  | 0.6007          | 0.9062   |
| 0.5362        | 68.0  | 136  | 0.6015          | 0.9062   |
| 0.5362        | 69.0  | 138  | 0.6020          | 0.9062   |
| 0.5362        | 70.0  | 140  | 0.6020          | 0.9062   |
| 0.5362        | 71.0  | 142  | 0.6021          | 0.9062   |
| 0.5362        | 72.0  | 144  | 0.6023          | 0.8906   |
| 0.5362        | 73.0  | 146  | 0.6023          | 0.8906   |
| 0.5362        | 74.0  | 148  | 0.6023          | 0.8906   |
| 0.536         | 75.0  | 150  | 0.6023          | 0.8906   |


### Framework versions

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