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NaeemCSECUET18
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xlm-roberta-base-MLTC-rob

Text Classification
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Safetensors
xlm-roberta
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  1. README.md +20 -19
  2. model.safetensors +1 -1
README.md CHANGED
@@ -18,13 +18,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3235
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- - F1: 0.8774
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- - Roc Auc: 0.8753
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- - Accuracy: 0.6195
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- - Hamming Loss: 0.1247
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- - Jaccard Score: 0.7815
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- - Zero One Loss: 0.3805
 
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  ## Model description
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@@ -53,18 +54,18 @@ The following hyperparameters were used during training:
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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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- | 0.5771 | 1.0 | 73 | 0.5508 | 0.7244 | 0.6906 | 0.3522 | 0.3091 | 0.5678 | 0.6478 |
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- | 0.4874 | 2.0 | 146 | 0.4105 | 0.8320 | 0.8297 | 0.5476 | 0.1703 | 0.7123 | 0.4524 |
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- | 0.3872 | 3.0 | 219 | 0.3960 | 0.8505 | 0.8418 | 0.5656 | 0.1581 | 0.7400 | 0.4344 |
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- | 0.3274 | 4.0 | 292 | 0.3367 | 0.8625 | 0.8585 | 0.5835 | 0.1414 | 0.7582 | 0.4165 |
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- | 0.2935 | 5.0 | 365 | 0.3384 | 0.8668 | 0.8630 | 0.6067 | 0.1369 | 0.7649 | 0.3933 |
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- | 0.3295 | 6.0 | 438 | 0.3267 | 0.8686 | 0.8669 | 0.6093 | 0.1330 | 0.7677 | 0.3907 |
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- | 0.263 | 7.0 | 511 | 0.3238 | 0.8779 | 0.8766 | 0.6324 | 0.1234 | 0.7823 | 0.3676 |
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- | 0.253 | 8.0 | 584 | 0.3306 | 0.8737 | 0.8727 | 0.6195 | 0.1272 | 0.7758 | 0.3805 |
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- | 0.2806 | 9.0 | 657 | 0.3243 | 0.8792 | 0.8772 | 0.6247 | 0.1228 | 0.7844 | 0.3753 |
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- | 0.2459 | 10.0 | 730 | 0.3235 | 0.8774 | 0.8753 | 0.6195 | 0.1247 | 0.7815 | 0.3805 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3590
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+ - F1: 0.8582
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+ - F1 Weighted: 0.8575
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+ - Roc Auc: 0.8540
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+ - Accuracy: 0.5861
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+ - Hamming Loss: 0.1459
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+ - Jaccard Score: 0.7516
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+ - Zero One Loss: 0.4139
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  ## Model description
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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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+ | 0.5819 | 1.0 | 73 | 0.5865 | 0.7125 | 0.6535 | 0.6745 | 0.3239 | 0.3252 | 0.5534 | 0.6761 |
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+ | 0.5155 | 2.0 | 146 | 0.4923 | 0.7685 | 0.7096 | 0.7602 | 0.3959 | 0.2397 | 0.6240 | 0.6041 |
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+ | 0.4172 | 3.0 | 219 | 0.4423 | 0.8240 | 0.8193 | 0.8076 | 0.4884 | 0.1922 | 0.7007 | 0.5116 |
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+ | 0.3246 | 4.0 | 292 | 0.3742 | 0.8472 | 0.8469 | 0.8425 | 0.5681 | 0.1575 | 0.7348 | 0.4319 |
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+ | 0.3171 | 5.0 | 365 | 0.3589 | 0.8521 | 0.8515 | 0.8489 | 0.5861 | 0.1510 | 0.7423 | 0.4139 |
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+ | 0.3444 | 6.0 | 438 | 0.3621 | 0.8534 | 0.8522 | 0.8502 | 0.5758 | 0.1497 | 0.7442 | 0.4242 |
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+ | 0.2437 | 7.0 | 511 | 0.3559 | 0.8589 | 0.8583 | 0.8560 | 0.5913 | 0.1440 | 0.7528 | 0.4087 |
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+ | 0.249 | 8.0 | 584 | 0.3624 | 0.8545 | 0.8540 | 0.8508 | 0.5810 | 0.1491 | 0.7459 | 0.4190 |
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+ | 0.2847 | 9.0 | 657 | 0.3552 | 0.8577 | 0.8574 | 0.8541 | 0.5887 | 0.1459 | 0.7508 | 0.4113 |
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+ | 0.2463 | 10.0 | 730 | 0.3590 | 0.8582 | 0.8575 | 0.8540 | 0.5861 | 0.1459 | 0.7516 | 0.4139 |
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  ### Framework versions
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