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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-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
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