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-sst2-100
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tmnam20/VieGLUE/SST2
      type: tmnam20/VieGLUE
      config: sst2
      split: validation
      args: sst2
    metrics:
    - type: accuracy
      value: 0.8818807339449541
      name: Accuracy
---

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# bert-base-multilingual-cased-sst2-100

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the tmnam20/VieGLUE/SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4327
- Accuracy: 0.8819

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3948        | 0.24  | 500  | 0.4225          | 0.8257   |
| 0.3381        | 0.48  | 1000 | 0.3634          | 0.8567   |
| 0.2941        | 0.71  | 1500 | 0.3880          | 0.8475   |
| 0.2828        | 0.95  | 2000 | 0.3587          | 0.8589   |
| 0.2157        | 1.19  | 2500 | 0.3510          | 0.8761   |
| 0.2367        | 1.43  | 3000 | 0.4677          | 0.8440   |
| 0.2072        | 1.66  | 3500 | 0.3567          | 0.8773   |
| 0.1904        | 1.9   | 4000 | 0.4433          | 0.8624   |
| 0.1401        | 2.14  | 4500 | 0.4476          | 0.875    |
| 0.1452        | 2.38  | 5000 | 0.4352          | 0.8624   |
| 0.1531        | 2.61  | 5500 | 0.4456          | 0.8647   |
| 0.1359        | 2.85  | 6000 | 0.4398          | 0.8761   |


### Framework versions

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