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

# bert-large-uncased-sst-2-64-13-smoothed

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6024
- Accuracy: 0.8438

## 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    | 0.8114          | 0.5078   |
| No log        | 2.0   | 8    | 0.7930          | 0.5078   |
| 0.8117        | 3.0   | 12   | 0.7630          | 0.5      |
| 0.8117        | 4.0   | 16   | 0.7257          | 0.5078   |
| 0.7546        | 5.0   | 20   | 0.6872          | 0.5938   |
| 0.7546        | 6.0   | 24   | 0.6706          | 0.6875   |
| 0.7546        | 7.0   | 28   | 0.6589          | 0.7578   |
| 0.6762        | 8.0   | 32   | 0.6473          | 0.7734   |
| 0.6762        | 9.0   | 36   | 0.6369          | 0.7812   |
| 0.6014        | 10.0  | 40   | 0.6282          | 0.7969   |
| 0.6014        | 11.0  | 44   | 0.6232          | 0.8125   |
| 0.6014        | 12.0  | 48   | 0.6226          | 0.8281   |
| 0.5545        | 13.0  | 52   | 0.6205          | 0.8281   |
| 0.5545        | 14.0  | 56   | 0.6191          | 0.7969   |
| 0.5486        | 15.0  | 60   | 0.6288          | 0.8047   |
| 0.5486        | 16.0  | 64   | 0.6184          | 0.8438   |
| 0.5486        | 17.0  | 68   | 0.6241          | 0.8203   |
| 0.5451        | 18.0  | 72   | 0.6098          | 0.8438   |
| 0.5451        | 19.0  | 76   | 0.6090          | 0.875    |
| 0.5418        | 20.0  | 80   | 0.6094          | 0.8672   |
| 0.5418        | 21.0  | 84   | 0.6092          | 0.8594   |
| 0.5418        | 22.0  | 88   | 0.6102          | 0.8594   |
| 0.5414        | 23.0  | 92   | 0.6107          | 0.8594   |
| 0.5414        | 24.0  | 96   | 0.6106          | 0.8281   |
| 0.5394        | 25.0  | 100  | 0.6104          | 0.8359   |
| 0.5394        | 26.0  | 104  | 0.6107          | 0.8359   |
| 0.5394        | 27.0  | 108  | 0.6125          | 0.8359   |
| 0.539         | 28.0  | 112  | 0.6144          | 0.8359   |
| 0.539         | 29.0  | 116  | 0.6139          | 0.8359   |
| 0.5398        | 30.0  | 120  | 0.6149          | 0.8281   |
| 0.5398        | 31.0  | 124  | 0.6174          | 0.8438   |
| 0.5398        | 32.0  | 128  | 0.6216          | 0.8359   |
| 0.5387        | 33.0  | 132  | 0.6200          | 0.8359   |
| 0.5387        | 34.0  | 136  | 0.6151          | 0.8438   |
| 0.5396        | 35.0  | 140  | 0.6138          | 0.8438   |
| 0.5396        | 36.0  | 144  | 0.6140          | 0.8438   |
| 0.5396        | 37.0  | 148  | 0.6147          | 0.8281   |
| 0.5388        | 38.0  | 152  | 0.6111          | 0.8516   |
| 0.5388        | 39.0  | 156  | 0.6097          | 0.8516   |
| 0.5391        | 40.0  | 160  | 0.6088          | 0.8594   |
| 0.5391        | 41.0  | 164  | 0.6090          | 0.8438   |
| 0.5391        | 42.0  | 168  | 0.6109          | 0.8438   |
| 0.5388        | 43.0  | 172  | 0.6102          | 0.8438   |
| 0.5388        | 44.0  | 176  | 0.6088          | 0.8438   |
| 0.5385        | 45.0  | 180  | 0.6091          | 0.8438   |
| 0.5385        | 46.0  | 184  | 0.6127          | 0.8438   |
| 0.5385        | 47.0  | 188  | 0.6167          | 0.8203   |
| 0.5391        | 48.0  | 192  | 0.6143          | 0.8359   |
| 0.5391        | 49.0  | 196  | 0.6071          | 0.8516   |
| 0.5387        | 50.0  | 200  | 0.6061          | 0.8516   |
| 0.5387        | 51.0  | 204  | 0.6054          | 0.8438   |
| 0.5387        | 52.0  | 208  | 0.6037          | 0.8516   |
| 0.5385        | 53.0  | 212  | 0.6019          | 0.8516   |
| 0.5385        | 54.0  | 216  | 0.6008          | 0.8438   |
| 0.5379        | 55.0  | 220  | 0.5998          | 0.8516   |
| 0.5379        | 56.0  | 224  | 0.5992          | 0.8516   |
| 0.5379        | 57.0  | 228  | 0.6001          | 0.8516   |
| 0.5382        | 58.0  | 232  | 0.6026          | 0.8438   |
| 0.5382        | 59.0  | 236  | 0.6039          | 0.8438   |
| 0.5381        | 60.0  | 240  | 0.6043          | 0.8438   |
| 0.5381        | 61.0  | 244  | 0.6032          | 0.8438   |
| 0.5381        | 62.0  | 248  | 0.6030          | 0.8438   |
| 0.5389        | 63.0  | 252  | 0.6023          | 0.8438   |
| 0.5389        | 64.0  | 256  | 0.6019          | 0.8438   |
| 0.5378        | 65.0  | 260  | 0.6024          | 0.8438   |
| 0.5378        | 66.0  | 264  | 0.6025          | 0.8438   |
| 0.5378        | 67.0  | 268  | 0.6020          | 0.8438   |
| 0.5374        | 68.0  | 272  | 0.6016          | 0.8438   |
| 0.5374        | 69.0  | 276  | 0.6017          | 0.8438   |
| 0.5378        | 70.0  | 280  | 0.6023          | 0.8438   |
| 0.5378        | 71.0  | 284  | 0.6025          | 0.8438   |
| 0.5378        | 72.0  | 288  | 0.6024          | 0.8438   |
| 0.5372        | 73.0  | 292  | 0.6023          | 0.8438   |
| 0.5372        | 74.0  | 296  | 0.6024          | 0.8438   |
| 0.5377        | 75.0  | 300  | 0.6024          | 0.8438   |


### Framework versions

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