---
license: apache-2.0
base_model: google/bert_uncased_L-6_H-768_A-12
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_uncased_L-6_H-768_A-12-QAT
  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: 0.8302752293577982
---

<!-- 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_uncased_L-6_H-768_A-12-QAT

This model is a fine-tuned version of [google/bert_uncased_L-6_H-768_A-12](https://huggingface.co/google/bert_uncased_L-6_H-768_A-12) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4989
- Accuracy: 0.8303

## 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: 6e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3055        | 1.0   | 8    | 0.4989          | 0.8303   |
| 0.192         | 2.0   | 16   | 0.4659          | 0.8108   |
| 0.0994        | 3.0   | 24   | 0.5389          | 0.8177   |
| 0.0324        | 4.0   | 32   | 0.7313          | 0.8096   |
| 0.0164        | 5.0   | 40   | 0.6689          | 0.8211   |
| 0.0137        | 6.0   | 48   | 0.7148          | 0.8154   |
| 0.0041        | 7.0   | 56   | 0.7339          | 0.8177   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0