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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: training1 |
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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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# training1 |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1739 |
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- Accuracy: 0.9431 |
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- F1: 0.8115 |
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- Precision: 0.8659 |
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- Recall: 0.7636 |
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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: 9.946303722432942e-06 |
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- train_batch_size: 16 |
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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_steps: 500 |
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- num_epochs: 10 |
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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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| 0.6954 | 1.0 | 61 | 0.6728 | 0.6127 | 0.1189 | 0.0936 | 0.1629 | |
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| 0.5462 | 2.0 | 122 | 0.3988 | 0.8683 | 0.4352 | 0.6972 | 0.3163 | |
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| 0.3711 | 3.0 | 183 | 0.3401 | 0.8765 | 0.4703 | 0.7535 | 0.3419 | |
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| 0.3269 | 4.0 | 244 | 0.3175 | 0.8883 | 0.4785 | 0.9524 | 0.3195 | |
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| 0.2899 | 5.0 | 305 | 0.2781 | 0.9042 | 0.5961 | 0.92 | 0.4409 | |
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| 0.2568 | 6.0 | 366 | 0.2576 | 0.9144 | 0.6745 | 0.865 | 0.5527 | |
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| 0.2176 | 7.0 | 427 | 0.2305 | 0.9242 | 0.7376 | 0.8287 | 0.6645 | |
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| 0.1879 | 8.0 | 488 | 0.2014 | 0.9329 | 0.7579 | 0.8991 | 0.6550 | |
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| 0.1541 | 9.0 | 549 | 0.2002 | 0.9329 | 0.7842 | 0.8095 | 0.7604 | |
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| 0.1275 | 10.0 | 610 | 0.1739 | 0.9431 | 0.8115 | 0.8659 | 0.7636 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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