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

# bert-base-uncased-sst-2-32-13-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: 0.6688
- Accuracy: 0.7188

## 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    | 0.7003          | 0.4844   |
| No log        | 2.0   | 4    | 0.6998          | 0.5      |
| No log        | 3.0   | 6    | 0.6991          | 0.5      |
| No log        | 4.0   | 8    | 0.6980          | 0.5      |
| 0.6958        | 5.0   | 10   | 0.6967          | 0.4844   |
| 0.6958        | 6.0   | 12   | 0.6953          | 0.4844   |
| 0.6958        | 7.0   | 14   | 0.6940          | 0.5312   |
| 0.6958        | 8.0   | 16   | 0.6928          | 0.5312   |
| 0.6958        | 9.0   | 18   | 0.6915          | 0.5625   |
| 0.6939        | 10.0  | 20   | 0.6904          | 0.625    |
| 0.6939        | 11.0  | 22   | 0.6894          | 0.625    |
| 0.6939        | 12.0  | 24   | 0.6885          | 0.6094   |
| 0.6939        | 13.0  | 26   | 0.6881          | 0.5938   |
| 0.6939        | 14.0  | 28   | 0.6874          | 0.5938   |
| 0.6707        | 15.0  | 30   | 0.6870          | 0.6094   |
| 0.6707        | 16.0  | 32   | 0.6869          | 0.6562   |
| 0.6707        | 17.0  | 34   | 0.6871          | 0.6562   |
| 0.6707        | 18.0  | 36   | 0.6867          | 0.625    |
| 0.6707        | 19.0  | 38   | 0.6858          | 0.625    |
| 0.6407        | 20.0  | 40   | 0.6848          | 0.6094   |
| 0.6407        | 21.0  | 42   | 0.6840          | 0.5781   |
| 0.6407        | 22.0  | 44   | 0.6850          | 0.5781   |
| 0.6407        | 23.0  | 46   | 0.6852          | 0.5625   |
| 0.6407        | 24.0  | 48   | 0.6845          | 0.5625   |
| 0.6016        | 25.0  | 50   | 0.6853          | 0.5938   |
| 0.6016        | 26.0  | 52   | 0.6859          | 0.5938   |
| 0.6016        | 27.0  | 54   | 0.6851          | 0.5781   |
| 0.6016        | 28.0  | 56   | 0.6812          | 0.5938   |
| 0.6016        | 29.0  | 58   | 0.6793          | 0.6094   |
| 0.5645        | 30.0  | 60   | 0.6786          | 0.6094   |
| 0.5645        | 31.0  | 62   | 0.6774          | 0.625    |
| 0.5645        | 32.0  | 64   | 0.6763          | 0.6719   |
| 0.5645        | 33.0  | 66   | 0.6754          | 0.6719   |
| 0.5645        | 34.0  | 68   | 0.6751          | 0.6562   |
| 0.5434        | 35.0  | 70   | 0.6748          | 0.6719   |
| 0.5434        | 36.0  | 72   | 0.6741          | 0.7031   |
| 0.5434        | 37.0  | 74   | 0.6745          | 0.6875   |
| 0.5434        | 38.0  | 76   | 0.6752          | 0.7031   |
| 0.5434        | 39.0  | 78   | 0.6756          | 0.6719   |
| 0.5383        | 40.0  | 80   | 0.6755          | 0.6719   |
| 0.5383        | 41.0  | 82   | 0.6760          | 0.6875   |
| 0.5383        | 42.0  | 84   | 0.6778          | 0.6406   |
| 0.5383        | 43.0  | 86   | 0.6802          | 0.6406   |
| 0.5383        | 44.0  | 88   | 0.6823          | 0.6406   |
| 0.5379        | 45.0  | 90   | 0.6827          | 0.5938   |
| 0.5379        | 46.0  | 92   | 0.6815          | 0.6094   |
| 0.5379        | 47.0  | 94   | 0.6804          | 0.625    |
| 0.5379        | 48.0  | 96   | 0.6790          | 0.6406   |
| 0.5379        | 49.0  | 98   | 0.6756          | 0.6562   |
| 0.5371        | 50.0  | 100  | 0.6739          | 0.6562   |
| 0.5371        | 51.0  | 102  | 0.6726          | 0.6406   |
| 0.5371        | 52.0  | 104  | 0.6718          | 0.6719   |
| 0.5371        | 53.0  | 106  | 0.6710          | 0.7031   |
| 0.5371        | 54.0  | 108  | 0.6705          | 0.6875   |
| 0.5365        | 55.0  | 110  | 0.6701          | 0.6875   |
| 0.5365        | 56.0  | 112  | 0.6698          | 0.6875   |
| 0.5365        | 57.0  | 114  | 0.6696          | 0.7031   |
| 0.5365        | 58.0  | 116  | 0.6694          | 0.6875   |
| 0.5365        | 59.0  | 118  | 0.6693          | 0.6875   |
| 0.5369        | 60.0  | 120  | 0.6690          | 0.7031   |
| 0.5369        | 61.0  | 122  | 0.6687          | 0.7188   |
| 0.5369        | 62.0  | 124  | 0.6687          | 0.7344   |
| 0.5369        | 63.0  | 126  | 0.6688          | 0.7031   |
| 0.5369        | 64.0  | 128  | 0.6688          | 0.6875   |
| 0.5365        | 65.0  | 130  | 0.6688          | 0.7031   |
| 0.5365        | 66.0  | 132  | 0.6688          | 0.7188   |
| 0.5365        | 67.0  | 134  | 0.6688          | 0.7188   |
| 0.5365        | 68.0  | 136  | 0.6688          | 0.7188   |
| 0.5365        | 69.0  | 138  | 0.6688          | 0.7344   |
| 0.5364        | 70.0  | 140  | 0.6687          | 0.7344   |
| 0.5364        | 71.0  | 142  | 0.6687          | 0.7188   |
| 0.5364        | 72.0  | 144  | 0.6687          | 0.7188   |
| 0.5364        | 73.0  | 146  | 0.6687          | 0.7188   |
| 0.5364        | 74.0  | 148  | 0.6688          | 0.7188   |
| 0.5366        | 75.0  | 150  | 0.6688          | 0.7188   |


### Framework versions

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