README.md

---
language:
- en
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- tmnam20/VieGLUE
metrics:
- matthews_correlation
model-index:
- name: bert-base-multilingual-cased-cola-100
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tmnam20/VieGLUE/COLA
      type: tmnam20/VieGLUE
      config: cola
      split: validation
      args: cola
    metrics:
    - name: Matthews Correlation
      type: matthews_correlation
      value: 0.14126608755137604
---

<!-- 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-multilingual-cased-cola-100

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the tmnam20/VieGLUE/COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6394
- Matthews Correlation: 0.1413

## 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: 16
- seed: 100
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.6           | 1.87  | 500  | 0.6125          | 0.0529               |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.2.0.dev20231203+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0