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
- f1
tags:
- generated_from_trainer
model-index:
- name: bert-base-multilingual-cased-qqp-1
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tmnam20/VieGLUE/QQP
      type: tmnam20/VieGLUE
      config: qqp
      split: validation
      args: qqp
    metrics:
    - type: accuracy
      value: 0.8912441256492704
      name: Accuracy
    - type: f1
      value: 0.8515680383485805
      name: F1
---

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# bert-base-multilingual-cased-qqp-1

This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the tmnam20/VieGLUE/QQP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2978
- Accuracy: 0.8912
- F1: 0.8516
- Combined Score: 0.8714

## 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 | F1     | Combined Score |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:--------------:|
| 0.3241        | 0.44  | 5000  | 0.3155          | 0.8585   | 0.8090 | 0.8337         |
| 0.3239        | 0.88  | 10000 | 0.2986          | 0.8655   | 0.8091 | 0.8373         |
| 0.2479        | 1.32  | 15000 | 0.2984          | 0.8762   | 0.8301 | 0.8532         |
| 0.2461        | 1.76  | 20000 | 0.2838          | 0.8818   | 0.8387 | 0.8603         |
| 0.1919        | 2.2   | 25000 | 0.2947          | 0.8887   | 0.8491 | 0.8689         |
| 0.1965        | 2.64  | 30000 | 0.2967          | 0.8896   | 0.8489 | 0.8692         |


### Framework versions

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