---
language:
- en
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- tmnam20/VieGLUE
metrics:
- accuracy
model-index:
- name: bert-base-multilingual-cased-qnli-10
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tmnam20/VieGLUE/QNLI
      type: tmnam20/VieGLUE
      config: qnli
      split: validation
      args: qnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.891085484166209
---

<!-- 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-qnli-10

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co./bert-base-multilingual-cased) on the tmnam20/VieGLUE/QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3198
- Accuracy: 0.8911

## 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: 10
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4249        | 0.15  | 500  | 0.3656          | 0.8464   |
| 0.3989        | 0.31  | 1000 | 0.3319          | 0.8581   |
| 0.3557        | 0.46  | 1500 | 0.3096          | 0.8688   |
| 0.3257        | 0.61  | 2000 | 0.3055          | 0.8700   |
| 0.3403        | 0.76  | 2500 | 0.2893          | 0.8786   |
| 0.311         | 0.92  | 3000 | 0.2919          | 0.8841   |
| 0.2424        | 1.07  | 3500 | 0.2974          | 0.8838   |
| 0.2663        | 1.22  | 4000 | 0.2966          | 0.8845   |
| 0.2486        | 1.37  | 4500 | 0.2904          | 0.8828   |
| 0.2442        | 1.53  | 5000 | 0.2919          | 0.8810   |
| 0.252         | 1.68  | 5500 | 0.2781          | 0.8880   |
| 0.2514        | 1.83  | 6000 | 0.2754          | 0.8867   |
| 0.254         | 1.99  | 6500 | 0.2692          | 0.8882   |
| 0.1632        | 2.14  | 7000 | 0.3349          | 0.8867   |
| 0.1835        | 2.29  | 7500 | 0.3126          | 0.8902   |
| 0.1725        | 2.44  | 8000 | 0.3145          | 0.8902   |
| 0.1624        | 2.6   | 8500 | 0.3272          | 0.8876   |
| 0.1751        | 2.75  | 9000 | 0.3240          | 0.8882   |
| 0.1653        | 2.9   | 9500 | 0.3235          | 0.8900   |


### Framework versions

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