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NaeemCSECUET18
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banglabert-MLTC-BB1

Text Classification
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electra
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  1. README.md +18 -18
  2. model.safetensors +1 -1
README.md CHANGED
@@ -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: 0.3577
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- - F1: 0.8555
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- - F1 Weighted: 0.8534
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- - Roc Auc: 0.8534
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- - Accuracy: 0.5733
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- - Hamming Loss: 0.1465
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- - Jaccard Score: 0.7475
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- - Zero One Loss: 0.4267
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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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- | 0.5579 | 1.0 | 49 | 0.5012 | 0.7993 | 0.7864 | 0.7832 | 0.4267 | 0.2166 | 0.6657 | 0.5733 |
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- | 0.4039 | 2.0 | 98 | 0.4338 | 0.8318 | 0.8287 | 0.8218 | 0.5347 | 0.1780 | 0.7121 | 0.4653 |
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- | 0.3704 | 3.0 | 147 | 0.3728 | 0.8600 | 0.8604 | 0.8560 | 0.5861 | 0.1440 | 0.7544 | 0.4139 |
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- | 0.3117 | 4.0 | 196 | 0.3615 | 0.8568 | 0.8553 | 0.8528 | 0.5733 | 0.1472 | 0.7495 | 0.4267 |
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- | 0.2723 | 5.0 | 245 | 0.3514 | 0.8548 | 0.8537 | 0.8528 | 0.5784 | 0.1472 | 0.7464 | 0.4216 |
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- | 0.2709 | 6.0 | 294 | 0.3640 | 0.8469 | 0.8434 | 0.8438 | 0.5476 | 0.1562 | 0.7344 | 0.4524 |
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- | 0.224 | 7.0 | 343 | 0.3581 | 0.8488 | 0.8461 | 0.8477 | 0.5578 | 0.1523 | 0.7373 | 0.4422 |
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- | 0.2335 | 8.0 | 392 | 0.3622 | 0.8532 | 0.8510 | 0.8502 | 0.5656 | 0.1497 | 0.7440 | 0.4344 |
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- | 0.2453 | 9.0 | 441 | 0.3552 | 0.8573 | 0.8560 | 0.8554 | 0.5758 | 0.1446 | 0.7503 | 0.4242 |
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- | 0.2194 | 10.0 | 490 | 0.3577 | 0.8555 | 0.8534 | 0.8534 | 0.5733 | 0.1465 | 0.7475 | 0.4267 |
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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: 0.3620
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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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  | 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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