---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-sst-2-16-42-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. -->

# bert-base-uncased-sst-2-16-42-smoothed

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co./bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6339
- Accuracy: 0.7812

## 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    | 0.7048          | 0.5      |
| No log        | 2.0   | 8    | 0.7021          | 0.5      |
| 0.7078        | 3.0   | 12   | 0.6982          | 0.5      |
| 0.7078        | 4.0   | 16   | 0.6936          | 0.4922   |
| 0.6935        | 5.0   | 20   | 0.6898          | 0.5625   |
| 0.6935        | 6.0   | 24   | 0.6878          | 0.625    |
| 0.6935        | 7.0   | 28   | 0.6856          | 0.6484   |
| 0.682         | 8.0   | 32   | 0.6818          | 0.6797   |
| 0.682         | 9.0   | 36   | 0.6759          | 0.7109   |
| 0.6567        | 10.0  | 40   | 0.6692          | 0.6562   |
| 0.6567        | 11.0  | 44   | 0.6662          | 0.6562   |
| 0.6567        | 12.0  | 48   | 0.6669          | 0.6484   |
| 0.6177        | 13.0  | 52   | 0.6612          | 0.6719   |
| 0.6177        | 14.0  | 56   | 0.6589          | 0.6641   |
| 0.5787        | 15.0  | 60   | 0.6565          | 0.6797   |
| 0.5787        | 16.0  | 64   | 0.6519          | 0.7109   |
| 0.5787        | 17.0  | 68   | 0.6506          | 0.7266   |
| 0.5487        | 18.0  | 72   | 0.6496          | 0.75     |
| 0.5487        | 19.0  | 76   | 0.6489          | 0.7344   |
| 0.5398        | 20.0  | 80   | 0.6492          | 0.75     |
| 0.5398        | 21.0  | 84   | 0.6467          | 0.7578   |
| 0.5398        | 22.0  | 88   | 0.6439          | 0.75     |
| 0.5384        | 23.0  | 92   | 0.6419          | 0.75     |
| 0.5384        | 24.0  | 96   | 0.6442          | 0.75     |
| 0.5389        | 25.0  | 100  | 0.6442          | 0.7422   |
| 0.5389        | 26.0  | 104  | 0.6391          | 0.75     |
| 0.5389        | 27.0  | 108  | 0.6428          | 0.7734   |
| 0.5373        | 28.0  | 112  | 0.6449          | 0.7656   |
| 0.5373        | 29.0  | 116  | 0.6412          | 0.7812   |
| 0.5379        | 30.0  | 120  | 0.6359          | 0.7578   |
| 0.5379        | 31.0  | 124  | 0.6348          | 0.7734   |
| 0.5379        | 32.0  | 128  | 0.6343          | 0.7891   |
| 0.5366        | 33.0  | 132  | 0.6340          | 0.7969   |
| 0.5366        | 34.0  | 136  | 0.6340          | 0.7891   |
| 0.5366        | 35.0  | 140  | 0.6339          | 0.7734   |
| 0.5366        | 36.0  | 144  | 0.6337          | 0.7734   |
| 0.5366        | 37.0  | 148  | 0.6335          | 0.7734   |
| 0.5363        | 38.0  | 152  | 0.6334          | 0.7969   |
| 0.5363        | 39.0  | 156  | 0.6347          | 0.7734   |
| 0.5367        | 40.0  | 160  | 0.6355          | 0.7656   |
| 0.5367        | 41.0  | 164  | 0.6363          | 0.7578   |
| 0.5367        | 42.0  | 168  | 0.6374          | 0.7656   |
| 0.5359        | 43.0  | 172  | 0.6375          | 0.7656   |
| 0.5359        | 44.0  | 176  | 0.6357          | 0.7578   |
| 0.5358        | 45.0  | 180  | 0.6351          | 0.7656   |
| 0.5358        | 46.0  | 184  | 0.6339          | 0.7734   |
| 0.5358        | 47.0  | 188  | 0.6334          | 0.7812   |
| 0.5362        | 48.0  | 192  | 0.6330          | 0.7891   |
| 0.5362        | 49.0  | 196  | 0.6327          | 0.7891   |
| 0.5364        | 50.0  | 200  | 0.6329          | 0.7891   |
| 0.5364        | 51.0  | 204  | 0.6341          | 0.7734   |
| 0.5364        | 52.0  | 208  | 0.6337          | 0.7734   |
| 0.5365        | 53.0  | 212  | 0.6326          | 0.7891   |
| 0.5365        | 54.0  | 216  | 0.6325          | 0.7891   |
| 0.5361        | 55.0  | 220  | 0.6326          | 0.7969   |
| 0.5361        | 56.0  | 224  | 0.6328          | 0.7891   |
| 0.5361        | 57.0  | 228  | 0.6328          | 0.7891   |
| 0.5361        | 58.0  | 232  | 0.6330          | 0.7891   |
| 0.5361        | 59.0  | 236  | 0.6335          | 0.7812   |
| 0.5363        | 60.0  | 240  | 0.6340          | 0.7812   |
| 0.5363        | 61.0  | 244  | 0.6343          | 0.7812   |
| 0.5363        | 62.0  | 248  | 0.6346          | 0.7734   |
| 0.536         | 63.0  | 252  | 0.6348          | 0.7734   |
| 0.536         | 64.0  | 256  | 0.6349          | 0.7734   |
| 0.5362        | 65.0  | 260  | 0.6353          | 0.7656   |
| 0.5362        | 66.0  | 264  | 0.6358          | 0.7656   |
| 0.5362        | 67.0  | 268  | 0.6361          | 0.7578   |
| 0.536         | 68.0  | 272  | 0.6355          | 0.7656   |
| 0.536         | 69.0  | 276  | 0.6349          | 0.7734   |
| 0.5358        | 70.0  | 280  | 0.6344          | 0.7734   |
| 0.5358        | 71.0  | 284  | 0.6342          | 0.7812   |
| 0.5358        | 72.0  | 288  | 0.6340          | 0.7812   |
| 0.5357        | 73.0  | 292  | 0.6340          | 0.7812   |
| 0.5357        | 74.0  | 296  | 0.6339          | 0.7812   |
| 0.5358        | 75.0  | 300  | 0.6339          | 0.7812   |


### Framework versions

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