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--- |
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library_name: transformers |
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license: mit |
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base_model: xlnet/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-eng-finetuned-20-epochs-tapt |
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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-eng-finetuned-20-epochs-tapt |
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This model is a fine-tuned version of [xlnet/xlnet-large-cased](https://huggingface.co/xlnet/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: 0.5355 |
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- F1: 0.7594 |
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- Roc Auc: 0.8228 |
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- Accuracy: 0.4506 |
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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: 32 |
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- eval_batch_size: 32 |
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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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| 0.6009 | 1.0 | 73 | 0.5947 | 0.436 | 0.6134 | 0.1369 | |
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| 0.5678 | 2.0 | 146 | 0.5712 | 0.4273 | 0.6124 | 0.1456 | |
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| 0.5116 | 3.0 | 219 | 0.4825 | 0.6053 | 0.7128 | 0.2877 | |
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| 0.4375 | 4.0 | 292 | 0.4319 | 0.6749 | 0.7600 | 0.3501 | |
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| 0.3723 | 5.0 | 365 | 0.3959 | 0.7178 | 0.7919 | 0.4073 | |
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| 0.2906 | 6.0 | 438 | 0.3896 | 0.7329 | 0.8001 | 0.4385 | |
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| 0.2457 | 7.0 | 511 | 0.4272 | 0.7291 | 0.8009 | 0.4125 | |
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| 0.2035 | 8.0 | 584 | 0.4255 | 0.7516 | 0.8198 | 0.4385 | |
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| 0.1546 | 9.0 | 657 | 0.4514 | 0.7462 | 0.8126 | 0.4402 | |
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| 0.1238 | 10.0 | 730 | 0.4732 | 0.7518 | 0.8155 | 0.4419 | |
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| 0.0896 | 11.0 | 803 | 0.5441 | 0.7437 | 0.8104 | 0.4506 | |
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| 0.0659 | 12.0 | 876 | 0.5355 | 0.7594 | 0.8228 | 0.4506 | |
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| 0.0459 | 13.0 | 949 | 0.5450 | 0.7582 | 0.8200 | 0.4714 | |
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| 0.0436 | 14.0 | 1022 | 0.5919 | 0.7525 | 0.8164 | 0.4558 | |
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| 0.0366 | 15.0 | 1095 | 0.6016 | 0.7588 | 0.8226 | 0.4541 | |
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### Framework versions |
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- Transformers 4.48.0 |
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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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