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--- |
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license: mit |
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base_model: microsoft/deberta-v3-small |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: DeBERTaV3_model_V3 |
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results: [] |
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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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# DeBERTaV3_model_V3 |
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This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1015 |
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- Accuracy: 0.9693 |
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- F1: 0.8766 |
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- Precision: 0.8803 |
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- Recall: 0.8729 |
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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: 5 |
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- eval_batch_size: 5 |
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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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- num_epochs: 12 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 100 | 0.3499 | 0.875 | 0.0 | 0.0 | 0.0 | |
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| No log | 2.0 | 200 | 0.2517 | 0.9068 | 0.4211 | 0.9412 | 0.2712 | |
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| No log | 3.0 | 300 | 0.1835 | 0.9396 | 0.7077 | 0.8961 | 0.5847 | |
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| No log | 4.0 | 400 | 0.1338 | 0.9587 | 0.8219 | 0.8911 | 0.7627 | |
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| 0.2507 | 5.0 | 500 | 0.1043 | 0.9640 | 0.8522 | 0.875 | 0.8305 | |
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| 0.2507 | 6.0 | 600 | 0.1076 | 0.9629 | 0.8472 | 0.8739 | 0.8220 | |
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| 0.2507 | 7.0 | 700 | 0.1061 | 0.9619 | 0.8475 | 0.8475 | 0.8475 | |
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| 0.2507 | 8.0 | 800 | 0.1015 | 0.9693 | 0.8766 | 0.8803 | 0.8729 | |
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| 0.2507 | 9.0 | 900 | 0.1099 | 0.9650 | 0.8596 | 0.8632 | 0.8559 | |
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| 0.0434 | 10.0 | 1000 | 0.1101 | 0.9661 | 0.8632 | 0.8707 | 0.8559 | |
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| 0.0434 | 11.0 | 1100 | 0.1054 | 0.9693 | 0.8766 | 0.8803 | 0.8729 | |
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| 0.0434 | 12.0 | 1200 | 0.1066 | 0.9682 | 0.8729 | 0.8729 | 0.8729 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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