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--- |
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license: mit |
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base_model: microsoft/deberta-v3-large |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: taskA-DeBERTa-large-1.0.0 |
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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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# taskA-DeBERTa-large-1.0.0 |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1001 |
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- Accuracy: 0.7924 |
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- Precision: 0.5855 |
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- Recall: 0.5821 |
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- F1: 0.5838 |
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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: 4e-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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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: 4 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.5231 | 0.39 | 500 | 0.5201 | 0.7967 | 0.7519 | 0.2795 | 0.4076 | |
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| 0.4515 | 0.78 | 1000 | 0.5449 | 0.8032 | 0.7937 | 0.2882 | 0.4228 | |
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| 0.4128 | 1.16 | 1500 | 0.6890 | 0.8190 | 0.6805 | 0.5216 | 0.5905 | |
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| 0.4025 | 1.55 | 2000 | 0.9337 | 0.7787 | 0.5481 | 0.6571 | 0.5976 | |
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| 0.4251 | 1.94 | 2500 | 0.8829 | 0.7981 | 0.6070 | 0.5476 | 0.5758 | |
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| 0.2864 | 2.33 | 3000 | 1.1001 | 0.7924 | 0.5855 | 0.5821 | 0.5838 | |
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| 0.3186 | 2.71 | 3500 | 1.1268 | 0.7794 | 0.5504 | 0.6455 | 0.5942 | |
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| 0.2679 | 3.1 | 4000 | 1.0378 | 0.8039 | 0.6093 | 0.6023 | 0.6058 | |
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| 0.1893 | 3.49 | 4500 | 1.1135 | 0.7996 | 0.6062 | 0.5677 | 0.5863 | |
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| 0.2037 | 3.88 | 5000 | 1.1569 | 0.7945 | 0.5886 | 0.5937 | 0.5911 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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