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update model card README.md

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@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6943
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- - Precision: 0.8467
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- - Recall: 0.8562
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- - F1: 0.8509
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- - Accuracy: 0.8793
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 3e-05
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 257 | 0.5790 | 0.7659 | 0.8225 | 0.7875 | 0.8472 |
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- | 0.7473 | 2.0 | 514 | 0.5007 | 0.8115 | 0.8503 | 0.8264 | 0.8647 |
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- | 0.7473 | 3.0 | 771 | 0.4903 | 0.8007 | 0.8418 | 0.8174 | 0.8594 |
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- | 0.2608 | 4.0 | 1028 | 0.5370 | 0.8249 | 0.8491 | 0.8350 | 0.8657 |
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- | 0.2608 | 5.0 | 1285 | 0.6034 | 0.8424 | 0.8514 | 0.8455 | 0.8803 |
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- | 0.1543 | 6.0 | 1542 | 0.5988 | 0.8396 | 0.8565 | 0.8466 | 0.8788 |
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- | 0.1543 | 7.0 | 1799 | 0.6736 | 0.8486 | 0.8453 | 0.8458 | 0.8769 |
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- | 0.0981 | 8.0 | 2056 | 0.6476 | 0.8400 | 0.8605 | 0.8492 | 0.8788 |
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- | 0.0981 | 9.0 | 2313 | 0.6837 | 0.8443 | 0.8510 | 0.8469 | 0.8788 |
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- | 0.0713 | 10.0 | 2570 | 0.6943 | 0.8467 | 0.8562 | 0.8509 | 0.8793 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6731
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+ - Precision: 0.8400
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+ - Recall: 0.8427
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+ - F1: 0.8407
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+ - Accuracy: 0.8779
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  ## Model description
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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: 32
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  - eval_batch_size: 32
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 257 | 0.6542 | 0.7446 | 0.8052 | 0.7657 | 0.8350 |
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+ | 0.8635 | 2.0 | 514 | 0.5548 | 0.7961 | 0.8277 | 0.8056 | 0.8540 |
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+ | 0.8635 | 3.0 | 771 | 0.4839 | 0.7912 | 0.8427 | 0.8115 | 0.8589 |
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+ | 0.3097 | 4.0 | 1028 | 0.5256 | 0.8148 | 0.8544 | 0.8315 | 0.8667 |
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+ | 0.3097 | 5.0 | 1285 | 0.5657 | 0.8346 | 0.8494 | 0.8413 | 0.8764 |
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+ | 0.1839 | 6.0 | 1542 | 0.6005 | 0.8208 | 0.8430 | 0.8304 | 0.8710 |
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+ | 0.1839 | 7.0 | 1799 | 0.6580 | 0.8319 | 0.8349 | 0.8314 | 0.8706 |
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+ | 0.1254 | 8.0 | 2056 | 0.6348 | 0.8342 | 0.8515 | 0.8423 | 0.8774 |
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+ | 0.1254 | 9.0 | 2313 | 0.6601 | 0.8314 | 0.8394 | 0.8348 | 0.8745 |
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+ | 0.0935 | 10.0 | 2570 | 0.6731 | 0.8400 | 0.8427 | 0.8407 | 0.8779 |
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  ### Framework versions