metadata

language:
  - en
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
  - generated_from_trainer
datasets:
  - tmnam20/VieGLUE
metrics:
  - accuracy
model-index:
  - name: bert-base-multilingual-cased-qnli-100
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: tmnam20/VieGLUE/QNLI
          type: tmnam20/VieGLUE
          config: qnli
          split: validation
          args: qnli
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8885227896760022

bert-base-multilingual-cased-qnli-100

This model is a fine-tuned version of 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