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---
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
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