transformers tensorboard safetensors bert text-classification generated_from_trainer en dataset:glue base_model:google/bert_uncased_L-4_H-256_A-4 base_model:finetune:google/bert_uncased_L-4_H-256_A-4 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: google/bert_uncased_L-4_H-256_A-4
datasets:
- glue
language:
- en
library_name: transformers
license: apache-2.0
metrics:
- matthews_correlation
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert_uncased_L-4_H-256_A-4_cola
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: GLUE COLA
      type: glue
      args: cola
    metrics:
    - type: matthews_correlation
      value: 0.2650812590803394
      name: Matthews Correlation
    - type: accuracy
      value: 0.7027804255485535
      name: Accuracy
---

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# bert_uncased_L-4_H-256_A-4_cola

This model is a fine-tuned version of [google/bert_uncased_L-4_H-256_A-4](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5943
- Matthews Correlation: 0.2651
- Accuracy: 0.7028

## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------:|
| 0.6358        | 1.0   | 34   | 0.6182          | 0.0                  | 0.6913   |
| 0.6077        | 2.0   | 68   | 0.6184          | 0.0                  | 0.6913   |
| 0.5982        | 3.0   | 102  | 0.6035          | 0.0                  | 0.6913   |
| 0.575         | 4.0   | 136  | 0.5997          | 0.1458               | 0.7009   |
| 0.5391        | 5.0   | 170  | 0.5992          | 0.2018               | 0.7028   |
| 0.4999        | 6.0   | 204  | 0.6159          | 0.2088               | 0.7085   |
| 0.4722        | 7.0   | 238  | 0.5974          | 0.2782               | 0.7248   |
| 0.4437        | 8.0   | 272  | 0.5943          | 0.2651               | 0.7028   |
| 0.4204        | 9.0   | 306  | 0.6239          | 0.2618               | 0.7210   |
| 0.3956        | 10.0  | 340  | 0.6360          | 0.2655               | 0.7191   |
| 0.3671        | 11.0  | 374  | 0.6876          | 0.2592               | 0.7200   |
| 0.3546        | 12.0  | 408  | 0.7041          | 0.2665               | 0.7239   |
| 0.333         | 13.0  | 442  | 0.6849          | 0.2891               | 0.7229   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.2.1+cu118
- Datasets 2.17.0
- Tokenizers 0.20.3
