metadata

language:
  - en
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
  - generated_from_trainer
datasets:
  - tmnam20/VieGLUE
metrics:
  - accuracy
  - f1
model-index:
  - name: bert-base-multilingual-cased-qqp-10
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: tmnam20/VieGLUE/QQP
          type: tmnam20/VieGLUE
          config: qqp
          split: validation
          args: qqp
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8885975760573831
          - name: F1
            type: f1
            value: 0.8473737716028464

bert-base-multilingual-cased-qqp-10

This model is a fine-tuned version of bert-base-multilingual-cased on the tmnam20/VieGLUE/QQP dataset. It achieves the following results on the evaluation set:

Loss: 0.3064
Accuracy: 0.8886
F1: 0.8474
Combined Score: 0.8680

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: 10
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
0.3263	0.44	5000	0.3272	0.8557	0.8081	0.8319
0.3084	0.88	10000	0.2968	0.8680	0.8191	0.8436
0.2424	1.32	15000	0.2998	0.8768	0.8324	0.8546
0.2171	1.76	20000	0.2995	0.8847	0.8449	0.8648
0.1796	2.2	25000	0.3124	0.8857	0.8424	0.8640
0.1811	2.64	30000	0.2963	0.8883	0.8477	0.8680

Framework versions

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