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--- |
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base_model: demdecuong/vihealthbert-base-word |
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tags: |
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- generated_from_trainer |
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datasets: |
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- tmnam20/ViNLI |
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metrics: |
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- accuracy |
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model-index: |
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- name: vihealthbert-w_dual-ViNLI |
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results: |
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- task: |
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name: Masked Language Modeling |
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type: fill-mask |
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dataset: |
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name: tmnam20/ViNLI |
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type: tmnam20/ViNLI |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.5919165580182529 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# vihealthbert-w_dual-ViNLI |
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This model is a fine-tuned version of [demdecuong/vihealthbert-base-word](https://huggingface.co/demdecuong/vihealthbert-base-word) on the tmnam20/ViNLI dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.6042 |
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- Accuracy: 0.5919 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- training_steps: 30000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-------:|:-----:|:---------------:|:--------:| |
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| 5.8126 | 15.625 | 1000 | 3.5461 | 0.4450 | |
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| 2.605 | 31.25 | 2000 | 2.7789 | 0.5404 | |
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| 1.5924 | 46.875 | 3000 | 2.5432 | 0.5809 | |
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| 1.2233 | 62.5 | 4000 | 2.6662 | 0.5567 | |
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| 0.9236 | 78.125 | 5000 | 2.4691 | 0.5927 | |
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| 0.7193 | 93.75 | 6000 | 2.4053 | 0.6027 | |
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| 0.6259 | 109.375 | 7000 | 2.5938 | 0.5782 | |
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| 0.5082 | 125.0 | 8000 | 2.4809 | 0.6137 | |
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| 0.4438 | 140.625 | 9000 | 2.7056 | 0.5819 | |
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| 0.4075 | 156.25 | 10000 | 2.6501 | 0.5946 | |
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| 0.3571 | 171.875 | 11000 | 2.5337 | 0.6082 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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