---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-base-sst2
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      config: sst2
      split: train
      args: sst2
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9036697247706422
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-base-sst2

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3735
- Accuracy: 0.9037

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.243         | 1.0   | 4210  | 0.3735          | 0.9037   |
| 0.1557        | 2.0   | 8420  | 0.3907          | 0.8922   |
| 0.1248        | 3.0   | 12630 | 0.3690          | 0.8945   |
| 0.1017        | 4.0   | 16840 | 0.5466          | 0.8830   |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1