metadata

language:
  - en
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
  - generated_from_trainer
datasets:
  - tmnam20/VieGLUE
metrics:
  - accuracy
model-index:
  - name: mdeberta-v3-base-qnli-10
    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: 0.8984074684239429

mdeberta-v3-base-qnli-10

This model is a fine-tuned version of microsoft/mdeberta-v3-base on the tmnam20/VieGLUE/QNLI dataset. It achieves the following results on the evaluation set:

Loss: 0.2859
Accuracy: 0.8984

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
0.3968	0.15	500	0.3264	0.8623
0.3826	0.31	1000	0.2996	0.8774
0.3478	0.46	1500	0.2894	0.8845
0.2959	0.61	2000	0.2745	0.8883
0.3228	0.76	2500	0.2640	0.8905
0.2899	0.92	3000	0.2723	0.8925
0.2269	1.07	3500	0.2850	0.8935
0.2614	1.22	4000	0.2607	0.8984
0.2508	1.37	4500	0.2831	0.8878
0.2563	1.53	5000	0.2556	0.8960
0.2485	1.68	5500	0.2618	0.9019
0.2373	1.83	6000	0.2600	0.8953
0.2361	1.99	6500	0.2545	0.9023
0.162	2.14	7000	0.3093	0.8997
0.2115	2.29	7500	0.2685	0.9010
0.176	2.44	8000	0.2966	0.8982
0.2047	2.6	8500	0.2767	0.8982
0.1831	2.75	9000	0.2918	0.8968
0.1818	2.9	9500	0.2818	0.8979

Framework versions

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