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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-TAPT-DAIR |
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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-TAPT-DAIR |
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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: 0.3389 |
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- F1: 0.7981 |
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- Roc Auc: 0.8621 |
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- Accuracy: 0.6672 |
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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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| 0.5069 | 1.0 | 627 | 0.5087 | 0.1392 | 0.5511 | 0.1141 | |
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| 0.4852 | 2.0 | 1254 | 0.5375 | 0.1380 | 0.5503 | 0.1141 | |
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| 0.483 | 3.0 | 1881 | 0.4748 | 0.2573 | 0.5929 | 0.2402 | |
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| 0.4274 | 4.0 | 2508 | 0.4217 | 0.3887 | 0.6509 | 0.4134 | |
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| 0.4097 | 5.0 | 3135 | 0.3956 | 0.4075 | 0.6626 | 0.4505 | |
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| 0.3566 | 6.0 | 3762 | 0.3691 | 0.4944 | 0.7070 | 0.4916 | |
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| 0.344 | 7.0 | 4389 | 0.3530 | 0.5637 | 0.7385 | 0.5243 | |
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| 0.3145 | 8.0 | 5016 | 0.3265 | 0.6867 | 0.7866 | 0.5874 | |
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| 0.2944 | 9.0 | 5643 | 0.3415 | 0.6197 | 0.7661 | 0.5607 | |
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| 0.2168 | 10.0 | 6270 | 0.3160 | 0.7367 | 0.8176 | 0.6373 | |
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| 0.1664 | 11.0 | 6897 | 0.3014 | 0.7569 | 0.8345 | 0.6333 | |
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| 0.1604 | 12.0 | 7524 | 0.3070 | 0.7606 | 0.8411 | 0.6453 | |
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| 0.1616 | 13.0 | 8151 | 0.3060 | 0.7700 | 0.8411 | 0.6592 | |
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| 0.1155 | 14.0 | 8778 | 0.3160 | 0.7831 | 0.8532 | 0.6536 | |
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| 0.1226 | 15.0 | 9405 | 0.3307 | 0.7886 | 0.8556 | 0.6600 | |
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| 0.0968 | 16.0 | 10032 | 0.3346 | 0.7919 | 0.8594 | 0.6604 | |
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| 0.0939 | 17.0 | 10659 | 0.3389 | 0.7981 | 0.8621 | 0.6672 | |
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| 0.0632 | 18.0 | 11286 | 0.3417 | 0.7970 | 0.8634 | 0.6648 | |
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| 0.0816 | 19.0 | 11913 | 0.3438 | 0.7970 | 0.8625 | 0.6676 | |
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| 0.0759 | 20.0 | 12540 | 0.3446 | 0.7969 | 0.8626 | 0.6676 | |
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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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