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NaeemCSECUET18
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banglabert-MLTC-BB1

Text Classification
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Safetensors
electra
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@@ -17,14 +17,14 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.3620
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- - F1: 0.8590
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- - F1 Weighted: 0.8576
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- - Roc Auc: 0.8573
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- - Accuracy: 0.5810
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- - Hamming Loss: 0.1427
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- - Jaccard Score: 0.7528
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- - Zero One Loss: 0.4190
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  ## Model description
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@@ -55,16 +55,16 @@ The following hyperparameters were used during training:
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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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- | 0.5572 | 1.0 | 49 | 0.5051 | 0.8005 | 0.7870 | 0.7871 | 0.4216 | 0.2127 | 0.6673 | 0.5784 |
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- | 0.3995 | 2.0 | 98 | 0.4366 | 0.8273 | 0.8258 | 0.8160 | 0.5167 | 0.1838 | 0.7055 | 0.4833 |
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- | 0.3671 | 3.0 | 147 | 0.3832 | 0.8493 | 0.8493 | 0.8451 | 0.5630 | 0.1549 | 0.7380 | 0.4370 |
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- | 0.3239 | 4.0 | 196 | 0.3671 | 0.8600 | 0.8595 | 0.8573 | 0.5810 | 0.1427 | 0.7544 | 0.4190 |
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- | 0.2786 | 5.0 | 245 | 0.3593 | 0.8573 | 0.8557 | 0.8541 | 0.5784 | 0.1459 | 0.7503 | 0.4216 |
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- | 0.2783 | 6.0 | 294 | 0.3608 | 0.8530 | 0.8509 | 0.8502 | 0.5733 | 0.1497 | 0.7437 | 0.4267 |
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- | 0.2247 | 7.0 | 343 | 0.3576 | 0.8579 | 0.8564 | 0.8573 | 0.5758 | 0.1427 | 0.7511 | 0.4242 |
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- | 0.2354 | 8.0 | 392 | 0.3631 | 0.8591 | 0.8579 | 0.8560 | 0.5861 | 0.1440 | 0.7530 | 0.4139 |
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- | 0.2517 | 9.0 | 441 | 0.3630 | 0.8553 | 0.8541 | 0.8534 | 0.5758 | 0.1465 | 0.7472 | 0.4242 |
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- | 0.223 | 10.0 | 490 | 0.3620 | 0.8590 | 0.8576 | 0.8573 | 0.5810 | 0.1427 | 0.7528 | 0.4190 |
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  ### Framework versions
 
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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: 0.3599
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+ - F1: 0.8582
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+ - F1 Weighted: 0.8565
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+ - Roc Auc: 0.8547
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+ - Accuracy: 0.5835
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+ - Hamming Loss: 0.1452
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+ - Jaccard Score: 0.7516
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+ - Zero One Loss: 0.4165
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  ## Model description
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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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+ | 0.5692 | 1.0 | 49 | 0.5109 | 0.7781 | 0.7194 | 0.7685 | 0.4216 | 0.2314 | 0.6367 | 0.5784 |
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+ | 0.4149 | 2.0 | 98 | 0.4230 | 0.8469 | 0.8467 | 0.8405 | 0.5604 | 0.1594 | 0.7345 | 0.4396 |
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+ | 0.3732 | 3.0 | 147 | 0.3856 | 0.8479 | 0.8474 | 0.8425 | 0.5527 | 0.1575 | 0.7360 | 0.4473 |
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+ | 0.3321 | 4.0 | 196 | 0.3750 | 0.8542 | 0.8522 | 0.8476 | 0.5578 | 0.1523 | 0.7454 | 0.4422 |
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+ | 0.2817 | 5.0 | 245 | 0.3721 | 0.8545 | 0.8514 | 0.8482 | 0.5630 | 0.1517 | 0.7460 | 0.4370 |
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+ | 0.2781 | 6.0 | 294 | 0.3553 | 0.8561 | 0.8547 | 0.8528 | 0.5656 | 0.1472 | 0.7484 | 0.4344 |
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+ | 0.2264 | 7.0 | 343 | 0.3576 | 0.8566 | 0.8550 | 0.8534 | 0.5733 | 0.1465 | 0.7492 | 0.4267 |
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+ | 0.2441 | 8.0 | 392 | 0.3595 | 0.8575 | 0.8560 | 0.8534 | 0.5733 | 0.1465 | 0.7505 | 0.4267 |
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+ | 0.2547 | 9.0 | 441 | 0.3608 | 0.8561 | 0.8548 | 0.8528 | 0.5784 | 0.1472 | 0.7484 | 0.4216 |
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+ | 0.2211 | 10.0 | 490 | 0.3599 | 0.8582 | 0.8565 | 0.8547 | 0.5835 | 0.1452 | 0.7516 | 0.4165 |
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  ### Framework versions