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BantuBERTa-vmw-finetuned-vmw-noaug

Text Classification
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roberta
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: cc-by-4.0
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+ base_model: Kuongan/BantuBERTa-vmw-finetuned
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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: BantuBERTa-vmw-finetuned-vmw-noaug
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+ results: []
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+ ---
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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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+
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+ # BantuBERTa-vmw-finetuned-vmw-noaug
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+
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+ This model is a fine-tuned version of [Kuongan/BantuBERTa-vmw-finetuned](https://huggingface.co/Kuongan/BantuBERTa-vmw-finetuned) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.4720
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+ - F1: 0.1990
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+ - Roc Auc: 0.5622
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+ - Accuracy: 0.4690
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
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+ | 0.1056 | 1.0 | 49 | 0.3296 | 0.1466 | 0.5417 | 0.4729 |
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+ | 0.1022 | 2.0 | 98 | 0.3384 | 0.1506 | 0.5425 | 0.4806 |
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+ | 0.0889 | 3.0 | 147 | 0.3528 | 0.1372 | 0.5439 | 0.4651 |
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+ | 0.071 | 4.0 | 196 | 0.3808 | 0.1874 | 0.5581 | 0.4264 |
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+ | 0.0659 | 5.0 | 245 | 0.3800 | 0.1664 | 0.5478 | 0.4690 |
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+ | 0.0534 | 6.0 | 294 | 0.3999 | 0.1873 | 0.5564 | 0.4651 |
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+ | 0.0407 | 7.0 | 343 | 0.4108 | 0.1439 | 0.5378 | 0.4535 |
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+ | 0.0368 | 8.0 | 392 | 0.4235 | 0.1970 | 0.5590 | 0.4612 |
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+ | 0.0247 | 9.0 | 441 | 0.4248 | 0.1724 | 0.5497 | 0.4729 |
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+ | 0.0213 | 10.0 | 490 | 0.4313 | 0.2049 | 0.5648 | 0.4690 |
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+ | 0.0164 | 11.0 | 539 | 0.4384 | 0.1918 | 0.5592 | 0.4690 |
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+ | 0.0144 | 12.0 | 588 | 0.4576 | 0.2116 | 0.5694 | 0.4574 |
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+ | 0.0138 | 13.0 | 637 | 0.4575 | 0.2075 | 0.5665 | 0.4729 |
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+ | 0.0117 | 14.0 | 686 | 0.4694 | 0.2008 | 0.5638 | 0.4574 |
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+ | 0.0114 | 15.0 | 735 | 0.4673 | 0.2169 | 0.5714 | 0.4651 |
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+ | 0.0098 | 16.0 | 784 | 0.4703 | 0.2034 | 0.5646 | 0.4690 |
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+ | 0.0096 | 17.0 | 833 | 0.4729 | 0.1943 | 0.5600 | 0.4574 |
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+ | 0.0102 | 18.0 | 882 | 0.4707 | 0.1989 | 0.5619 | 0.4690 |
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+ | 0.0101 | 19.0 | 931 | 0.4720 | 0.1990 | 0.5622 | 0.4690 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.47.0
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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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