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--- |
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license: mit |
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base_model: FacebookAI/xlm-roberta-base |
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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: xlm-roberta-base-MLTC-rob |
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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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# xlm-roberta-base-MLTC-rob |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3645 |
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- F1: 0.8629 |
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- F1 Weighted: 0.8632 |
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- Roc Auc: 0.8598 |
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- Accuracy: 0.6067 |
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- Hamming Loss: 0.1401 |
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- Jaccard Score: 0.7588 |
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- Zero One Loss: 0.3933 |
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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: linear |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | F1 Weighted | Roc Auc | Accuracy | Hamming Loss | Jaccard Score | Zero One Loss | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:-------:|:--------:|:------------:|:-------------:|:-------------:| |
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| 0.5856 | 1.0 | 73 | 0.5884 | 0.7201 | 0.6619 | 0.6835 | 0.3393 | 0.3162 | 0.5627 | 0.6607 | |
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| 0.5053 | 2.0 | 146 | 0.4688 | 0.7718 | 0.7159 | 0.7712 | 0.4139 | 0.2288 | 0.6284 | 0.5861 | |
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| 0.3929 | 3.0 | 219 | 0.4002 | 0.8410 | 0.8413 | 0.8334 | 0.5347 | 0.1665 | 0.7256 | 0.4653 | |
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| 0.3293 | 4.0 | 292 | 0.3816 | 0.8471 | 0.8453 | 0.8399 | 0.5527 | 0.1600 | 0.7348 | 0.4473 | |
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| 0.3242 | 5.0 | 365 | 0.3607 | 0.8550 | 0.8538 | 0.8515 | 0.5784 | 0.1485 | 0.7467 | 0.4216 | |
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| 0.3228 | 6.0 | 438 | 0.3776 | 0.8495 | 0.8462 | 0.8437 | 0.5707 | 0.1562 | 0.7384 | 0.4293 | |
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| 0.2713 | 7.0 | 511 | 0.4086 | 0.8453 | 0.8412 | 0.8373 | 0.5630 | 0.1626 | 0.7320 | 0.4370 | |
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| 0.2519 | 8.0 | 584 | 0.3711 | 0.8534 | 0.8531 | 0.8489 | 0.5861 | 0.1510 | 0.7443 | 0.4139 | |
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| 0.2724 | 9.0 | 657 | 0.3645 | 0.8629 | 0.8632 | 0.8598 | 0.6067 | 0.1401 | 0.7588 | 0.3933 | |
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| 0.2484 | 10.0 | 730 | 0.3669 | 0.8586 | 0.8585 | 0.8553 | 0.5964 | 0.1446 | 0.7522 | 0.4036 | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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