---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-sst-2-32-13-30
  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-2-32-13-30

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: 0.6080
- Accuracy: 0.6875

## 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: 1.5e-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: 5
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 2    | 0.7378          | 0.4844   |
| No log        | 2.0   | 4    | 0.7276          | 0.4844   |
| No log        | 3.0   | 6    | 0.7104          | 0.4844   |
| No log        | 4.0   | 8    | 0.6924          | 0.5469   |
| 0.7068        | 5.0   | 10   | 0.6837          | 0.5625   |
| 0.7068        | 6.0   | 12   | 0.6782          | 0.5938   |
| 0.7068        | 7.0   | 14   | 0.6755          | 0.5781   |
| 0.7068        | 8.0   | 16   | 0.6701          | 0.625    |
| 0.7068        | 9.0   | 18   | 0.6644          | 0.625    |
| 0.5803        | 10.0  | 20   | 0.6574          | 0.6406   |
| 0.5803        | 11.0  | 22   | 0.6505          | 0.625    |
| 0.5803        | 12.0  | 24   | 0.6436          | 0.6875   |
| 0.5803        | 13.0  | 26   | 0.6370          | 0.6562   |
| 0.5803        | 14.0  | 28   | 0.6316          | 0.6719   |
| 0.4412        | 15.0  | 30   | 0.6267          | 0.6875   |
| 0.4412        | 16.0  | 32   | 0.6232          | 0.6719   |
| 0.4412        | 17.0  | 34   | 0.6205          | 0.6875   |
| 0.4412        | 18.0  | 36   | 0.6175          | 0.6875   |
| 0.4412        | 19.0  | 38   | 0.6141          | 0.6875   |
| 0.3438        | 20.0  | 40   | 0.6113          | 0.6875   |
| 0.3438        | 21.0  | 42   | 0.6099          | 0.6875   |
| 0.3438        | 22.0  | 44   | 0.6109          | 0.6875   |
| 0.3438        | 23.0  | 46   | 0.6112          | 0.6875   |
| 0.3438        | 24.0  | 48   | 0.6107          | 0.6875   |
| 0.2832        | 25.0  | 50   | 0.6097          | 0.6875   |
| 0.2832        | 26.0  | 52   | 0.6088          | 0.6875   |
| 0.2832        | 27.0  | 54   | 0.6082          | 0.6875   |
| 0.2832        | 28.0  | 56   | 0.6080          | 0.6875   |
| 0.2832        | 29.0  | 58   | 0.6080          | 0.6875   |
| 0.2532        | 30.0  | 60   | 0.6080          | 0.6875   |


### Framework versions

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