---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: bert-base-sst-2
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      config: sst2
      split: validation
      args: sst2
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.930045871559633
    - name: F1
      type: f1
      value: 0.9299971705127952
    - name: Precision
      type: precision
      value: 0.9302394783826914
    - name: Recall
      type: recall
      value: 0.9298749684263703
---

<!-- 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-sst-2

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: 0.4216
- Accuracy: 0.9300
- F1: 0.9300
- Precision: 0.9302
- Recall: 0.9299

## 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: 0.0001
- train_batch_size: 160
- eval_batch_size: 160
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 640
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2366        | 1.0   | 105  | 0.2193          | 0.9117   | 0.9115 | 0.9139    | 0.9111 |
| 0.1104        | 2.0   | 210  | 0.2174          | 0.9243   | 0.9243 | 0.9243    | 0.9243 |
| 0.0685        | 2.99  | 315  | 0.2441          | 0.9186   | 0.9185 | 0.9186    | 0.9185 |
| 0.0476        | 4.0   | 421  | 0.2524          | 0.9232   | 0.9232 | 0.9233    | 0.9234 |
| 0.0319        | 5.0   | 526  | 0.2832          | 0.9220   | 0.9219 | 0.9226    | 0.9217 |
| 0.0227        | 6.0   | 631  | 0.3093          | 0.9289   | 0.9289 | 0.9289    | 0.9289 |
| 0.0169        | 6.99  | 736  | 0.3755          | 0.9209   | 0.9209 | 0.9208    | 0.9210 |
| 0.0112        | 8.0   | 842  | 0.3793          | 0.9220   | 0.9219 | 0.9234    | 0.9215 |
| 0.0079        | 9.0   | 947  | 0.3980          | 0.9255   | 0.9254 | 0.9255    | 0.9254 |
| 0.007         | 9.98  | 1050 | 0.4216          | 0.9300   | 0.9300 | 0.9302    | 0.9299 |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3