---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: jpqd-bert-base-ft-sst2
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: GLUE SST2
      type: glue
      args: sst2
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9254587155963303
---

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

> **Note**
> This model was trained for only 1 epoch and is shared for testing purposes

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2181
- Accuracy: 0.9255

## 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4129        | 0.12  | 250  | 0.4416          | 0.8761   |
| 0.412         | 0.24  | 500  | 0.4969          | 0.8899   |
| 0.3191        | 0.36  | 750  | 0.2717          | 0.9163   |
| 0.2688        | 0.48  | 1000 | 0.2432          | 0.9117   |
| 0.3306        | 0.59  | 1250 | 0.2033          | 0.9243   |
| 0.224         | 0.71  | 1500 | 0.2383          | 0.9243   |
| 0.2082        | 0.83  | 1750 | 0.2233          | 0.9255   |
| 0.2161        | 0.95  | 2000 | 0.2207          | 0.9255   |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2