transformers pytorch tensorboard openvino bert text-classification generated_from_trainer dataset:glue base_model:google/bert_uncased_L-6_H-768_A-12 base_model:finetune:google/bert_uncased_L-6_H-768_A-12 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: google/bert_uncased_L-6_H-768_A-12
datasets:
- glue
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert_uncased_qat
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: glue
      type: glue
      config: sst2
      split: validation
      args: sst2
    metrics:
    - type: accuracy
      value: 0.9094036697247706
      name: Accuracy
---

<!-- 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_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: 0.2984
- Accuracy: 0.9094

## 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: 7.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2453        | 1.0   | 527  | 0.2552          | 0.8979   |
| 0.1257        | 2.0   | 1054 | 0.2997          | 0.8933   |
| 0.0818        | 3.0   | 1581 | 0.2984          | 0.9094   |
| 0.057         | 4.0   | 2108 | 0.3181          | 0.9048   |
| 0.0403        | 5.0   | 2635 | 0.3299          | 0.9083   |
| 0.0274        | 6.0   | 3162 | 0.4222          | 0.9060   |
| 0.0192        | 7.0   | 3689 | 0.4797          | 0.9083   |


### Framework versions

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