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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: xlnet-large-cased-finetuned-augmentation-LUNAR |
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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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# xlnet-large-cased-finetuned-augmentation-LUNAR |
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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: 0.5861 |
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- F1: 0.7939 |
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- Roc Auc: 0.8377 |
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- Accuracy: 0.5610 |
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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: 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_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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| 0.5702 | 1.0 | 179 | 0.5158 | 0.2278 | 0.5480 | 0.2006 | |
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| 0.4202 | 2.0 | 358 | 0.3750 | 0.6839 | 0.7559 | 0.4376 | |
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| 0.336 | 3.0 | 537 | 0.3422 | 0.7237 | 0.7859 | 0.4642 | |
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| 0.2321 | 4.0 | 716 | 0.3447 | 0.7519 | 0.8097 | 0.5245 | |
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| 0.1487 | 5.0 | 895 | 0.3742 | 0.7534 | 0.8108 | 0.5316 | |
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| 0.0974 | 6.0 | 1074 | 0.4043 | 0.7727 | 0.8325 | 0.5442 | |
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| 0.0674 | 7.0 | 1253 | 0.4612 | 0.7701 | 0.8214 | 0.5175 | |
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| 0.0485 | 8.0 | 1432 | 0.4862 | 0.7771 | 0.8227 | 0.5330 | |
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| 0.0281 | 9.0 | 1611 | 0.5346 | 0.7712 | 0.8249 | 0.5456 | |
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| 0.0178 | 10.0 | 1790 | 0.5535 | 0.7709 | 0.8213 | 0.5372 | |
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| 0.0137 | 11.0 | 1969 | 0.5715 | 0.7908 | 0.8450 | 0.5484 | |
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| 0.0144 | 12.0 | 2148 | 0.5597 | 0.7866 | 0.8343 | 0.5694 | |
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| 0.005 | 13.0 | 2327 | 0.5850 | 0.7844 | 0.8333 | 0.5596 | |
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| 0.0034 | 14.0 | 2506 | 0.5807 | 0.7881 | 0.8344 | 0.5596 | |
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| 0.0034 | 15.0 | 2685 | 0.5856 | 0.7924 | 0.8370 | 0.5750 | |
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| 0.0025 | 16.0 | 2864 | 0.5861 | 0.7939 | 0.8377 | 0.5610 | |
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| 0.0056 | 17.0 | 3043 | 0.5916 | 0.7920 | 0.8374 | 0.5596 | |
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| 0.0043 | 18.0 | 3222 | 0.5900 | 0.7909 | 0.8374 | 0.5708 | |
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| 0.0036 | 19.0 | 3401 | 0.5902 | 0.7882 | 0.8352 | 0.5652 | |
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| 0.0016 | 20.0 | 3580 | 0.5903 | 0.7890 | 0.8359 | 0.5666 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.0 |
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- Datasets 3.0.1 |
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- Tokenizers 0.20.0 |
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