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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-mnli-1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tmnam20/VieGLUE/MNLI
type: tmnam20/VieGLUE
config: mnli
split: validation_matched
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8031936533767291
---
<!-- 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-mnli-1
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co./bert-base-multilingual-cased) on the tmnam20/VieGLUE/MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5349
- Accuracy: 0.8032
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8082 | 0.04 | 500 | 0.7958 | 0.6485 |
| 0.7259 | 0.08 | 1000 | 0.7455 | 0.6895 |
| 0.7018 | 0.12 | 1500 | 0.6970 | 0.7118 |
| 0.7026 | 0.16 | 2000 | 0.6827 | 0.7127 |
| 0.6696 | 0.2 | 2500 | 0.6500 | 0.7323 |
| 0.6744 | 0.24 | 3000 | 0.6345 | 0.7380 |
| 0.6136 | 0.29 | 3500 | 0.6294 | 0.7402 |
| 0.632 | 0.33 | 4000 | 0.6269 | 0.7472 |
| 0.6735 | 0.37 | 4500 | 0.6195 | 0.7489 |
| 0.6202 | 0.41 | 5000 | 0.6336 | 0.7414 |
| 0.6495 | 0.45 | 5500 | 0.6125 | 0.7517 |
| 0.6235 | 0.49 | 6000 | 0.6097 | 0.7515 |
| 0.5852 | 0.53 | 6500 | 0.6068 | 0.7581 |
| 0.6395 | 0.57 | 7000 | 0.6039 | 0.7493 |
| 0.6009 | 0.61 | 7500 | 0.5878 | 0.7553 |
| 0.6059 | 0.65 | 8000 | 0.5876 | 0.7638 |
| 0.6019 | 0.69 | 8500 | 0.5829 | 0.7651 |
| 0.5989 | 0.73 | 9000 | 0.5922 | 0.7612 |
| 0.6195 | 0.77 | 9500 | 0.5868 | 0.7615 |
| 0.6028 | 0.81 | 10000 | 0.5724 | 0.7709 |
| 0.5741 | 0.86 | 10500 | 0.5670 | 0.7717 |
| 0.582 | 0.9 | 11000 | 0.5702 | 0.7732 |
| 0.5706 | 0.94 | 11500 | 0.5597 | 0.7755 |
| 0.5676 | 0.98 | 12000 | 0.5655 | 0.7735 |
| 0.5235 | 1.02 | 12500 | 0.5849 | 0.7662 |
| 0.521 | 1.06 | 13000 | 0.5646 | 0.7788 |
| 0.5122 | 1.1 | 13500 | 0.5717 | 0.7738 |
| 0.5102 | 1.14 | 14000 | 0.5667 | 0.7765 |
| 0.5152 | 1.18 | 14500 | 0.5598 | 0.7780 |
| 0.4904 | 1.22 | 15000 | 0.5693 | 0.7746 |
| 0.507 | 1.26 | 15500 | 0.5584 | 0.7804 |
| 0.5163 | 1.3 | 16000 | 0.5570 | 0.7787 |
| 0.4921 | 1.34 | 16500 | 0.5727 | 0.7798 |
| 0.5249 | 1.39 | 17000 | 0.5653 | 0.7789 |
| 0.4994 | 1.43 | 17500 | 0.5726 | 0.7783 |
| 0.5335 | 1.47 | 18000 | 0.5547 | 0.7848 |
| 0.543 | 1.51 | 18500 | 0.5541 | 0.7785 |
| 0.5138 | 1.55 | 19000 | 0.5569 | 0.7842 |
| 0.4626 | 1.59 | 19500 | 0.5625 | 0.7860 |
| 0.4828 | 1.63 | 20000 | 0.5434 | 0.7858 |
| 0.5121 | 1.67 | 20500 | 0.5495 | 0.7806 |
| 0.5012 | 1.71 | 21000 | 0.5318 | 0.7900 |
| 0.4609 | 1.75 | 21500 | 0.5485 | 0.7878 |
| 0.4928 | 1.79 | 22000 | 0.5462 | 0.7868 |
| 0.4922 | 1.83 | 22500 | 0.5305 | 0.7920 |
| 0.4913 | 1.87 | 23000 | 0.5396 | 0.7891 |
| 0.4992 | 1.91 | 23500 | 0.5341 | 0.7952 |
| 0.4732 | 1.96 | 24000 | 0.5277 | 0.7952 |
| 0.4925 | 2.0 | 24500 | 0.5339 | 0.7943 |
| 0.4098 | 2.04 | 25000 | 0.5643 | 0.7911 |
| 0.4168 | 2.08 | 25500 | 0.5534 | 0.7929 |
| 0.4099 | 2.12 | 26000 | 0.5674 | 0.7925 |
| 0.4142 | 2.16 | 26500 | 0.5652 | 0.7918 |
| 0.398 | 2.2 | 27000 | 0.5875 | 0.7899 |
| 0.3899 | 2.24 | 27500 | 0.5726 | 0.7975 |
| 0.403 | 2.28 | 28000 | 0.5596 | 0.7968 |
| 0.399 | 2.32 | 28500 | 0.5716 | 0.7885 |
| 0.4176 | 2.36 | 29000 | 0.5570 | 0.7941 |
| 0.3871 | 2.4 | 29500 | 0.5689 | 0.7926 |
| 0.4156 | 2.44 | 30000 | 0.5648 | 0.7918 |
| 0.386 | 2.49 | 30500 | 0.5650 | 0.7931 |
| 0.4131 | 2.53 | 31000 | 0.5525 | 0.7948 |
| 0.4202 | 2.57 | 31500 | 0.5585 | 0.7914 |
| 0.4129 | 2.61 | 32000 | 0.5495 | 0.7963 |
| 0.4215 | 2.65 | 32500 | 0.5524 | 0.7978 |
| 0.413 | 2.69 | 33000 | 0.5578 | 0.7954 |
| 0.4296 | 2.73 | 33500 | 0.5509 | 0.7966 |
| 0.3602 | 2.77 | 34000 | 0.5581 | 0.7974 |
| 0.3901 | 2.81 | 34500 | 0.5561 | 0.7985 |
| 0.4163 | 2.85 | 35000 | 0.5502 | 0.7955 |
| 0.3787 | 2.89 | 35500 | 0.5573 | 0.7951 |
| 0.4285 | 2.93 | 36000 | 0.5535 | 0.7958 |
| 0.3578 | 2.97 | 36500 | 0.5563 | 0.7964 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.2.0.dev20231203+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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