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-qnli-100
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tmnam20/VieGLUE/QNLI
      type: tmnam20/VieGLUE
      config: qnli
      split: validation
      args: qnli
    metrics:
    - type: accuracy
      value: 0.8885227896760022
      name: Accuracy
---

<!-- 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-100

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: 0.3284
- Accuracy: 0.8885

## 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: 100
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4041        | 0.15  | 500  | 0.3611          | 0.8488   |
| 0.3784        | 0.31  | 1000 | 0.3232          | 0.8603   |
| 0.364         | 0.46  | 1500 | 0.3128          | 0.8642   |
| 0.364         | 0.61  | 2000 | 0.3020          | 0.8702   |
| 0.3236        | 0.76  | 2500 | 0.2960          | 0.8768   |
| 0.3475        | 0.92  | 3000 | 0.2895          | 0.8816   |
| 0.252         | 1.07  | 3500 | 0.3019          | 0.8812   |
| 0.261         | 1.22  | 4000 | 0.2783          | 0.8893   |
| 0.2718        | 1.37  | 4500 | 0.2880          | 0.8832   |
| 0.2407        | 1.53  | 5000 | 0.3017          | 0.8812   |
| 0.254         | 1.68  | 5500 | 0.2775          | 0.8827   |
| 0.2611        | 1.83  | 6000 | 0.2837          | 0.8812   |
| 0.257         | 1.99  | 6500 | 0.2816          | 0.8852   |
| 0.1645        | 2.14  | 7000 | 0.3323          | 0.8845   |
| 0.1679        | 2.29  | 7500 | 0.3568          | 0.8825   |
| 0.1643        | 2.44  | 8000 | 0.3203          | 0.8889   |
| 0.1662        | 2.6   | 8500 | 0.3240          | 0.8878   |
| 0.1558        | 2.75  | 9000 | 0.3302          | 0.8856   |
| 0.1614        | 2.9   | 9500 | 0.3299          | 0.8872   |


### Framework versions

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