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license: mit |
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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: deberta-v3-base-finetuned-3d-sentiment |
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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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# deberta-v3-base-finetuned-3d-sentiment |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9369 |
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- Accuracy: 0.8104 |
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- Precision: 0.8132 |
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- Recall: 0.8104 |
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- F1: 0.8111 |
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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: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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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: 12762 |
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- num_epochs: 7 |
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- mixed_precision_training: Native AMP |
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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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| 0.7346 | 1.0 | 3190 | 0.6162 | 0.7666 | 0.7733 | 0.7666 | 0.7676 | |
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| 0.4839 | 2.0 | 6380 | 0.5586 | 0.8013 | 0.8033 | 0.8013 | 0.8016 | |
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| 0.416 | 3.0 | 9570 | 0.5250 | 0.8026 | 0.8044 | 0.8026 | 0.8019 | |
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| 0.3501 | 4.0 | 12760 | 0.5294 | 0.8067 | 0.8068 | 0.8067 | 0.8053 | |
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| 0.2661 | 5.0 | 15950 | 0.6626 | 0.8093 | 0.8127 | 0.8093 | 0.8094 | |
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| 0.173 | 6.0 | 19140 | 0.7242 | 0.8093 | 0.8106 | 0.8093 | 0.8097 | |
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| 0.1146 | 7.0 | 22330 | 0.9369 | 0.8104 | 0.8132 | 0.8104 | 0.8111 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu117 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.3 |
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