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--- |
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base_model: csebuetnlp/banglabert |
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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: banglabert-MLTC-BB |
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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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# banglabert-MLTC-BB |
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This model is a fine-tuned version of [csebuetnlp/banglabert](https://huggingface.co/csebuetnlp/banglabert) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3626 |
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- F1: 0.8666 |
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- Roc Auc: 0.8624 |
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- Accuracy: 0.5861 |
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- Hamming Loss: 0.1375 |
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- Jaccard Score: 0.7646 |
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- Zero One Loss: 0.4139 |
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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 | Roc Auc | Accuracy | Hamming Loss | Jaccard Score | Zero One Loss | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|:------------:|:-------------:|:-------------:| |
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| 0.4962 | 1.0 | 73 | 0.4868 | 0.8020 | 0.7962 | 0.4730 | 0.2037 | 0.6694 | 0.5270 | |
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| 0.3992 | 2.0 | 146 | 0.3993 | 0.8420 | 0.8386 | 0.5656 | 0.1613 | 0.7272 | 0.4344 | |
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| 0.3163 | 3.0 | 219 | 0.3647 | 0.8616 | 0.8586 | 0.5810 | 0.1414 | 0.7569 | 0.4190 | |
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| 0.2545 | 4.0 | 292 | 0.3626 | 0.8666 | 0.8624 | 0.5861 | 0.1375 | 0.7646 | 0.4139 | |
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| 0.2464 | 5.0 | 365 | 0.3537 | 0.8626 | 0.8612 | 0.5835 | 0.1388 | 0.7584 | 0.4165 | |
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| 0.2534 | 6.0 | 438 | 0.3591 | 0.8600 | 0.8566 | 0.5707 | 0.1433 | 0.7544 | 0.4293 | |
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| 0.194 | 7.0 | 511 | 0.3525 | 0.8644 | 0.8624 | 0.5938 | 0.1375 | 0.7612 | 0.4062 | |
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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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