---
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-1
  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: 0.885227896760022
---

<!-- 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-1

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: 0.3278
- Accuracy: 0.8852

## 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: 1
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3938        | 0.15  | 500  | 0.3494          | 0.8495   |
| 0.3712        | 0.31  | 1000 | 0.3266          | 0.8570   |
| 0.3837        | 0.46  | 1500 | 0.3174          | 0.8655   |
| 0.3466        | 0.61  | 2000 | 0.2957          | 0.8785   |
| 0.3084        | 0.76  | 2500 | 0.3093          | 0.8715   |
| 0.322         | 0.92  | 3000 | 0.2950          | 0.8731   |
| 0.273         | 1.07  | 3500 | 0.2872          | 0.8834   |
| 0.2628        | 1.22  | 4000 | 0.3110          | 0.8794   |
| 0.2732        | 1.37  | 4500 | 0.2910          | 0.8797   |
| 0.2592        | 1.53  | 5000 | 0.2855          | 0.8849   |
| 0.241         | 1.68  | 5500 | 0.2974          | 0.8861   |
| 0.2256        | 1.83  | 6000 | 0.2914          | 0.8850   |
| 0.2402        | 1.99  | 6500 | 0.2759          | 0.8883   |
| 0.1958        | 2.14  | 7000 | 0.3080          | 0.8880   |
| 0.1684        | 2.29  | 7500 | 0.3190          | 0.8847   |
| 0.1472        | 2.44  | 8000 | 0.3305          | 0.8871   |
| 0.1601        | 2.6   | 8500 | 0.3298          | 0.8836   |
| 0.1857        | 2.75  | 9000 | 0.3274          | 0.8847   |
| 0.1667        | 2.9   | 9500 | 0.3256          | 0.8841   |


### Framework versions

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