---
license: mit
base_model: roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-large-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-large-sst-2-64-13-smoothed

This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5741
- Accuracy: 0.9375

## 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    | 0.6932          | 0.5      |
| No log        | 2.0   | 8    | 0.6930          | 0.5      |
| 0.6986        | 3.0   | 12   | 0.6928          | 0.5078   |
| 0.6986        | 4.0   | 16   | 0.6926          | 0.5078   |
| 0.7049        | 5.0   | 20   | 0.6926          | 0.5      |
| 0.7049        | 6.0   | 24   | 0.6924          | 0.5      |
| 0.7049        | 7.0   | 28   | 0.6922          | 0.5      |
| 0.6928        | 8.0   | 32   | 0.6918          | 0.5234   |
| 0.6928        | 9.0   | 36   | 0.6912          | 0.5312   |
| 0.6889        | 10.0  | 40   | 0.6905          | 0.5625   |
| 0.6889        | 11.0  | 44   | 0.6895          | 0.5078   |
| 0.6889        | 12.0  | 48   | 0.6880          | 0.5781   |
| 0.6855        | 13.0  | 52   | 0.6823          | 0.6875   |
| 0.6855        | 14.0  | 56   | 0.6590          | 0.8281   |
| 0.6346        | 15.0  | 60   | 0.6187          | 0.8672   |
| 0.6346        | 16.0  | 64   | 0.6192          | 0.8281   |
| 0.6346        | 17.0  | 68   | 0.5983          | 0.9062   |
| 0.5877        | 18.0  | 72   | 0.6030          | 0.875    |
| 0.5877        | 19.0  | 76   | 0.5942          | 0.9141   |
| 0.564         | 20.0  | 80   | 0.5918          | 0.8984   |
| 0.564         | 21.0  | 84   | 0.5860          | 0.9141   |
| 0.564         | 22.0  | 88   | 0.5761          | 0.9375   |
| 0.5505        | 23.0  | 92   | 0.5854          | 0.9297   |
| 0.5505        | 24.0  | 96   | 0.5750          | 0.9141   |
| 0.5462        | 25.0  | 100  | 0.5776          | 0.9141   |
| 0.5462        | 26.0  | 104  | 0.5713          | 0.9453   |
| 0.5462        | 27.0  | 108  | 0.5731          | 0.9375   |
| 0.5414        | 28.0  | 112  | 0.5770          | 0.9297   |
| 0.5414        | 29.0  | 116  | 0.5789          | 0.9141   |
| 0.5382        | 30.0  | 120  | 0.5871          | 0.9062   |
| 0.5382        | 31.0  | 124  | 0.5810          | 0.9141   |
| 0.5382        | 32.0  | 128  | 0.5765          | 0.9297   |
| 0.5383        | 33.0  | 132  | 0.5769          | 0.9297   |
| 0.5383        | 34.0  | 136  | 0.5718          | 0.9453   |
| 0.5385        | 35.0  | 140  | 0.5704          | 0.9453   |
| 0.5385        | 36.0  | 144  | 0.5728          | 0.9453   |
| 0.5385        | 37.0  | 148  | 0.5737          | 0.9297   |
| 0.5381        | 38.0  | 152  | 0.5749          | 0.9375   |
| 0.5381        | 39.0  | 156  | 0.5754          | 0.9375   |
| 0.5389        | 40.0  | 160  | 0.5742          | 0.9375   |
| 0.5389        | 41.0  | 164  | 0.5723          | 0.9375   |
| 0.5389        | 42.0  | 168  | 0.5720          | 0.9375   |
| 0.5372        | 43.0  | 172  | 0.5694          | 0.9453   |
| 0.5372        | 44.0  | 176  | 0.5723          | 0.9375   |
| 0.5384        | 45.0  | 180  | 0.5766          | 0.9375   |
| 0.5384        | 46.0  | 184  | 0.5715          | 0.9375   |
| 0.5384        | 47.0  | 188  | 0.5696          | 0.9453   |
| 0.5379        | 48.0  | 192  | 0.5709          | 0.9453   |
| 0.5379        | 49.0  | 196  | 0.5720          | 0.9453   |
| 0.5372        | 50.0  | 200  | 0.5717          | 0.9453   |
| 0.5372        | 51.0  | 204  | 0.5706          | 0.9453   |
| 0.5372        | 52.0  | 208  | 0.5697          | 0.9453   |
| 0.5371        | 53.0  | 212  | 0.5700          | 0.9453   |
| 0.5371        | 54.0  | 216  | 0.5706          | 0.9453   |
| 0.5368        | 55.0  | 220  | 0.5697          | 0.9453   |
| 0.5368        | 56.0  | 224  | 0.5702          | 0.9453   |
| 0.5368        | 57.0  | 228  | 0.5719          | 0.9453   |
| 0.5371        | 58.0  | 232  | 0.5728          | 0.9453   |
| 0.5371        | 59.0  | 236  | 0.5729          | 0.9375   |
| 0.5371        | 60.0  | 240  | 0.5734          | 0.9375   |
| 0.5371        | 61.0  | 244  | 0.5736          | 0.9375   |
| 0.5371        | 62.0  | 248  | 0.5745          | 0.9375   |
| 0.5369        | 63.0  | 252  | 0.5760          | 0.9375   |
| 0.5369        | 64.0  | 256  | 0.5772          | 0.9375   |
| 0.5365        | 65.0  | 260  | 0.5771          | 0.9375   |
| 0.5365        | 66.0  | 264  | 0.5763          | 0.9375   |
| 0.5365        | 67.0  | 268  | 0.5759          | 0.9375   |
| 0.5365        | 68.0  | 272  | 0.5753          | 0.9375   |
| 0.5365        | 69.0  | 276  | 0.5751          | 0.9375   |
| 0.5369        | 70.0  | 280  | 0.5746          | 0.9375   |
| 0.5369        | 71.0  | 284  | 0.5741          | 0.9375   |
| 0.5369        | 72.0  | 288  | 0.5742          | 0.9375   |
| 0.5367        | 73.0  | 292  | 0.5742          | 0.9375   |
| 0.5367        | 74.0  | 296  | 0.5741          | 0.9375   |
| 0.5368        | 75.0  | 300  | 0.5741          | 0.9375   |


### Framework versions

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