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--- |
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library_name: transformers |
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license: mit |
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base_model: xlnet-large-cased |
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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-xlnet-large-cased-finetuned-semeval |
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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-xlnet-large-cased-finetuned-semeval |
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This model is a fine-tuned version of [xlnet-large-cased](https://huggingface.co/xlnet-large-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6440 |
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- F1: 0.7846 |
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- Roc Auc: 0.8368 |
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- Accuracy: 0.4892 |
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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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| 0.577 | 1.0 | 139 | 0.5832 | 0.4593 | 0.6262 | 0.1516 | |
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| 0.5274 | 2.0 | 278 | 0.5171 | 0.4943 | 0.6506 | 0.1805 | |
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| 0.429 | 3.0 | 417 | 0.3725 | 0.7256 | 0.7927 | 0.4188 | |
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| 0.3146 | 4.0 | 556 | 0.3735 | 0.7468 | 0.8089 | 0.4567 | |
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| 0.2192 | 5.0 | 695 | 0.3846 | 0.7625 | 0.8216 | 0.4729 | |
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| 0.167 | 6.0 | 834 | 0.4137 | 0.7541 | 0.8126 | 0.4585 | |
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| 0.0954 | 7.0 | 973 | 0.4414 | 0.7672 | 0.8222 | 0.4783 | |
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| 0.0668 | 8.0 | 1112 | 0.5105 | 0.7696 | 0.8271 | 0.4747 | |
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| 0.0299 | 9.0 | 1251 | 0.5573 | 0.7688 | 0.8263 | 0.4531 | |
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| 0.0258 | 10.0 | 1390 | 0.5910 | 0.7793 | 0.8366 | 0.4783 | |
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| 0.0132 | 11.0 | 1529 | 0.6008 | 0.7741 | 0.8298 | 0.4801 | |
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| 0.0082 | 12.0 | 1668 | 0.6108 | 0.7780 | 0.8340 | 0.4711 | |
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| 0.0054 | 13.0 | 1807 | 0.6386 | 0.7806 | 0.8356 | 0.4711 | |
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| 0.0033 | 14.0 | 1946 | 0.6429 | 0.7775 | 0.8325 | 0.4747 | |
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| 0.0025 | 15.0 | 2085 | 0.6464 | 0.7763 | 0.8314 | 0.4675 | |
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| 0.0028 | 16.0 | 2224 | 0.6440 | 0.7846 | 0.8368 | 0.4892 | |
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| 0.0029 | 17.0 | 2363 | 0.6455 | 0.7816 | 0.8344 | 0.4856 | |
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| 0.0029 | 18.0 | 2502 | 0.6496 | 0.7777 | 0.8316 | 0.4765 | |
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| 0.0023 | 19.0 | 2641 | 0.6500 | 0.7812 | 0.8347 | 0.4819 | |
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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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