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--- |
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license: mit |
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base_model: sagorsarker/bangla-bert-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: bangla-bert-base-MLTC-BB2 |
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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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# bangla-bert-base-MLTC-BB2 |
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This model is a fine-tuned version of [sagorsarker/bangla-bert-base](https://huggingface.co/sagorsarker/bangla-bert-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4005 |
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- F1: 0.8514 |
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- F1 Weighted: 0.8504 |
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- Roc Auc: 0.8470 |
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- Accuracy: 0.5424 |
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- Hamming Loss: 0.1530 |
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- Jaccard Score: 0.7413 |
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- Zero One Loss: 0.4576 |
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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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| 0.4015 | 1.0 | 73 | 0.4027 | 0.8246 | 0.8201 | 0.8206 | 0.5039 | 0.1793 | 0.7016 | 0.4961 | |
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| 0.3579 | 2.0 | 146 | 0.3583 | 0.8541 | 0.8548 | 0.8522 | 0.5553 | 0.1478 | 0.7453 | 0.4447 | |
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| 0.2831 | 3.0 | 219 | 0.3768 | 0.8429 | 0.8402 | 0.8360 | 0.5398 | 0.1639 | 0.7284 | 0.4602 | |
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| 0.1799 | 4.0 | 292 | 0.3565 | 0.8534 | 0.8534 | 0.8489 | 0.5604 | 0.1510 | 0.7443 | 0.4396 | |
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| 0.1671 | 5.0 | 365 | 0.3724 | 0.8552 | 0.8547 | 0.8508 | 0.5681 | 0.1491 | 0.7470 | 0.4319 | |
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| 0.1451 | 6.0 | 438 | 0.3773 | 0.8493 | 0.8484 | 0.8477 | 0.5450 | 0.1523 | 0.7381 | 0.4550 | |
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| 0.0899 | 7.0 | 511 | 0.3986 | 0.8502 | 0.8494 | 0.8457 | 0.5398 | 0.1542 | 0.7394 | 0.4602 | |
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| 0.0744 | 8.0 | 584 | 0.4005 | 0.8514 | 0.8504 | 0.8470 | 0.5424 | 0.1530 | 0.7413 | 0.4576 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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