transformers safetensors bert text-classification generated_from_trainer en dataset:tmnam20/VieGLUE base_model:google-bert/bert-base-multilingual-cased base_model:finetune:google-bert/bert-base-multilingual-cased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-multilingual-cased
datasets:
- tmnam20/VieGLUE
language:
- en
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert-base-multilingual-cased-mnli-1
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tmnam20/VieGLUE/MNLI
      type: tmnam20/VieGLUE
      config: mnli
      split: validation_matched
      args: mnli
    metrics:
    - type: accuracy
      value: 0.8031936533767291
      name: Accuracy
---

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# 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
