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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: PRE-xlnet-large-cased-finetuned-augmentation |
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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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# PRE-xlnet-large-cased-finetuned-augmentation |
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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: 0.3312 |
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- F1: 0.7328 |
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- Roc Auc: 0.8488 |
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- Accuracy: 0.7773 |
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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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| 0.34 | 1.0 | 389 | 0.3108 | 0.1424 | 0.5799 | 0.5386 | |
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| 0.2957 | 2.0 | 778 | 0.2733 | 0.3070 | 0.6420 | 0.5637 | |
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| 0.2697 | 3.0 | 1167 | 0.2241 | 0.4578 | 0.6995 | 0.6525 | |
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| 0.2248 | 4.0 | 1556 | 0.1944 | 0.5903 | 0.7595 | 0.7239 | |
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| 0.1689 | 5.0 | 1945 | 0.1793 | 0.6729 | 0.8127 | 0.7561 | |
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| 0.1052 | 6.0 | 2334 | 0.1961 | 0.6682 | 0.7962 | 0.7600 | |
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| 0.0964 | 7.0 | 2723 | 0.2035 | 0.6728 | 0.7989 | 0.7613 | |
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| 0.0885 | 8.0 | 3112 | 0.2315 | 0.7185 | 0.8404 | 0.7593 | |
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| 0.0497 | 9.0 | 3501 | 0.2608 | 0.7264 | 0.8476 | 0.7593 | |
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| 0.0411 | 10.0 | 3890 | 0.2688 | 0.7212 | 0.8363 | 0.7831 | |
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| 0.0182 | 11.0 | 4279 | 0.3081 | 0.7300 | 0.8558 | 0.7709 | |
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| 0.0186 | 12.0 | 4668 | 0.3179 | 0.7216 | 0.8452 | 0.7754 | |
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| 0.0131 | 13.0 | 5057 | 0.3312 | 0.7328 | 0.8488 | 0.7773 | |
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| 0.0069 | 14.0 | 5446 | 0.3464 | 0.7272 | 0.8472 | 0.7716 | |
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| 0.0069 | 15.0 | 5835 | 0.3522 | 0.7316 | 0.8481 | 0.7793 | |
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| 0.0027 | 16.0 | 6224 | 0.3555 | 0.7303 | 0.8500 | 0.7773 | |
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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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