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--- |
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license: mit |
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base_model: microsoft/deberta-v2-xxlarge |
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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: deberta-v2-xxl-imdb-v0.1 |
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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-v2-xxl-imdb-v0.1 |
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This model is a fine-tuned version of [microsoft/deberta-v2-xxlarge](https://huggingface.co/microsoft/deberta-v2-xxlarge) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1684 |
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- Accuracy: 0.9708 |
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- F1: 0.9710 |
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- Precision: 0.9669 |
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- Recall: 0.9750 |
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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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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.2 |
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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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| 0.0607 | 1.0 | 6250 | 0.2211 | 0.9616 | 0.9611 | 0.9738 | 0.9487 | |
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| 0.3056 | 2.0 | 12500 | 0.1855 | 0.9662 | 0.9658 | 0.9770 | 0.9548 | |
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| 0.0502 | 3.0 | 18750 | 0.1790 | 0.9696 | 0.9697 | 0.9668 | 0.9726 | |
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| 0.2397 | 4.0 | 25000 | 0.1741 | 0.9705 | 0.9707 | 0.9634 | 0.9782 | |
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| 0.1207 | 5.0 | 31250 | 0.1662 | 0.9708 | 0.9708 | 0.9713 | 0.9702 | |
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| 0.0637 | 6.0 | 37500 | 0.1718 | 0.9707 | 0.9707 | 0.9710 | 0.9703 | |
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| 0.3034 | 7.0 | 43750 | 0.1687 | 0.9706 | 0.9707 | 0.9670 | 0.9745 | |
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| 0.0013 | 8.0 | 50000 | 0.1683 | 0.9708 | 0.9709 | 0.9668 | 0.9751 | |
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| 0.0543 | 9.0 | 56250 | 0.1683 | 0.9707 | 0.9708 | 0.9667 | 0.9750 | |
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| 0.1015 | 10.0 | 62500 | 0.1684 | 0.9708 | 0.9710 | 0.9669 | 0.9750 | |
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### Framework versions |
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- Transformers 4.39.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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