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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/deberta-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- f1 |
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- accuracy |
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model-index: |
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- name: CS221-deberta-base-finetuned-semeval-aug |
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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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# CS221-deberta-base-finetuned-semeval-aug |
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This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3475 |
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- F1: 0.8781 |
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- Roc Auc: 0.9070 |
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- Accuracy: 0.7651 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
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| 0.4197 | 1.0 | 277 | 0.3731 | 0.6624 | 0.7504 | 0.4435 | |
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| 0.2694 | 2.0 | 554 | 0.3172 | 0.7734 | 0.8309 | 0.5411 | |
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| 0.1796 | 3.0 | 831 | 0.2815 | 0.7769 | 0.8256 | 0.5890 | |
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| 0.1281 | 4.0 | 1108 | 0.2802 | 0.8120 | 0.8543 | 0.6305 | |
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| 0.0754 | 5.0 | 1385 | 0.2998 | 0.8177 | 0.8565 | 0.6495 | |
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| 0.067 | 6.0 | 1662 | 0.2926 | 0.8367 | 0.8755 | 0.6838 | |
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| 0.0303 | 7.0 | 1939 | 0.2977 | 0.8409 | 0.8750 | 0.7010 | |
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| 0.009 | 8.0 | 2216 | 0.3252 | 0.8474 | 0.8777 | 0.7091 | |
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| 0.0114 | 9.0 | 2493 | 0.3181 | 0.8539 | 0.8899 | 0.7281 | |
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| 0.006 | 10.0 | 2770 | 0.3390 | 0.8581 | 0.8890 | 0.7344 | |
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| 0.0023 | 11.0 | 3047 | 0.3407 | 0.8646 | 0.8934 | 0.7353 | |
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| 0.0022 | 12.0 | 3324 | 0.3453 | 0.8674 | 0.8991 | 0.7525 | |
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| 0.0031 | 13.0 | 3601 | 0.3488 | 0.8708 | 0.9021 | 0.7507 | |
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| 0.0013 | 14.0 | 3878 | 0.3440 | 0.8736 | 0.9044 | 0.7579 | |
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| 0.0009 | 15.0 | 4155 | 0.3475 | 0.8781 | 0.9070 | 0.7651 | |
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| 0.0026 | 16.0 | 4432 | 0.3455 | 0.8767 | 0.9057 | 0.7651 | |
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| 0.0008 | 17.0 | 4709 | 0.3504 | 0.8755 | 0.9053 | 0.7615 | |
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| 0.0009 | 18.0 | 4986 | 0.3549 | 0.8742 | 0.9043 | 0.7588 | |
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### Framework versions |
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- Transformers 4.47.1 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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