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Text Classification
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PyTorch
electra
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  1. README.md +35 -44
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -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 [ai-forever/ruElectra-medium](https://huggingface.co/ai-forever/ruElectra-medium) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.6537
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- - Accuracy: 0.6901
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- - Recall: 0.6451
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- - Precision: 0.6599
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- - F1: 0.6390
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  ## Model description
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@@ -43,7 +43,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -55,44 +55,35 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | No log | 1.0 | 100 | 1.3590 | 0.5643 | 0.3617 | 0.3821 | 0.3270 |
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- | No log | 2.0 | 200 | 0.9903 | 0.6637 | 0.5263 | 0.5238 | 0.5058 |
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- | No log | 3.0 | 300 | 0.9370 | 0.6842 | 0.5254 | 0.5367 | 0.5185 |
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- | No log | 4.0 | 400 | 0.9366 | 0.7047 | 0.5982 | 0.5655 | 0.5675 |
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- | 0.9611 | 5.0 | 500 | 1.0894 | 0.6901 | 0.5707 | 0.5656 | 0.5529 |
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- | 0.9611 | 6.0 | 600 | 1.1565 | 0.7018 | 0.5834 | 0.5569 | 0.5649 |
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- | 0.9611 | 7.0 | 700 | 1.1471 | 0.7076 | 0.5887 | 0.5565 | 0.5687 |
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- | 0.9611 | 8.0 | 800 | 1.2477 | 0.7281 | 0.6326 | 0.7122 | 0.6341 |
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- | 0.9611 | 9.0 | 900 | 1.3606 | 0.7310 | 0.6556 | 0.7163 | 0.6484 |
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- | 0.1529 | 10.0 | 1000 | 1.7044 | 0.6725 | 0.6059 | 0.6230 | 0.5964 |
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- | 0.1529 | 11.0 | 1100 | 1.5851 | 0.7193 | 0.6600 | 0.6571 | 0.6548 |
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- | 0.1529 | 12.0 | 1200 | 1.7624 | 0.6959 | 0.6463 | 0.6714 | 0.6457 |
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- | 0.1529 | 13.0 | 1300 | 1.9156 | 0.6988 | 0.6312 | 0.6636 | 0.6360 |
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- | 0.1529 | 14.0 | 1400 | 1.8304 | 0.7251 | 0.6525 | 0.6899 | 0.6586 |
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- | 0.0417 | 15.0 | 1500 | 1.9549 | 0.7164 | 0.6442 | 0.6758 | 0.6485 |
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- | 0.0417 | 16.0 | 1600 | 1.9306 | 0.7398 | 0.6569 | 0.7047 | 0.6639 |
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- | 0.0417 | 17.0 | 1700 | 2.1130 | 0.6959 | 0.6591 | 0.6904 | 0.6556 |
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- | 0.0417 | 18.0 | 1800 | 1.9658 | 0.7368 | 0.6312 | 0.7479 | 0.6545 |
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- | 0.0417 | 19.0 | 1900 | 2.0108 | 0.7281 | 0.6497 | 0.7180 | 0.6605 |
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- | 0.0149 | 20.0 | 2000 | 2.0183 | 0.7368 | 0.6757 | 0.7038 | 0.6832 |
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- | 0.0149 | 21.0 | 2100 | 2.1543 | 0.7222 | 0.7085 | 0.6745 | 0.6824 |
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- | 0.0149 | 22.0 | 2200 | 1.9347 | 0.7485 | 0.6518 | 0.7867 | 0.6722 |
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- | 0.0149 | 23.0 | 2300 | 1.8752 | 0.7690 | 0.6852 | 0.7686 | 0.7024 |
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- | 0.0149 | 24.0 | 2400 | 2.0048 | 0.7544 | 0.6834 | 0.7379 | 0.6966 |
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- | 0.0111 | 25.0 | 2500 | 2.0534 | 0.7515 | 0.6635 | 0.7640 | 0.6841 |
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- | 0.0111 | 26.0 | 2600 | 2.0457 | 0.7368 | 0.6503 | 0.6918 | 0.6586 |
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- | 0.0111 | 27.0 | 2700 | 2.1561 | 0.7368 | 0.6657 | 0.6990 | 0.6678 |
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- | 0.0111 | 28.0 | 2800 | 2.1431 | 0.7398 | 0.6590 | 0.6734 | 0.6604 |
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- | 0.0111 | 29.0 | 2900 | 2.3783 | 0.7135 | 0.6544 | 0.6643 | 0.6509 |
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- | 0.0103 | 30.0 | 3000 | 2.3847 | 0.7251 | 0.6368 | 0.7351 | 0.6597 |
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- | 0.0103 | 31.0 | 3100 | 2.2030 | 0.7427 | 0.7017 | 0.7082 | 0.7023 |
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- | 0.0103 | 32.0 | 3200 | 2.4123 | 0.7368 | 0.6679 | 0.6974 | 0.6697 |
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- | 0.0103 | 33.0 | 3300 | 2.2644 | 0.7398 | 0.6760 | 0.7428 | 0.6902 |
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- | 0.0103 | 34.0 | 3400 | 2.3744 | 0.7339 | 0.6847 | 0.7080 | 0.6800 |
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- | 0.0135 | 35.0 | 3500 | 2.1573 | 0.7485 | 0.6933 | 0.6932 | 0.6867 |
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- | 0.0135 | 36.0 | 3600 | 2.1728 | 0.7515 | 0.6649 | 0.7606 | 0.6802 |
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- | 0.0135 | 37.0 | 3700 | 2.0993 | 0.7719 | 0.6859 | 0.7705 | 0.6972 |
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- | 0.0135 | 38.0 | 3800 | 2.6537 | 0.6901 | 0.6451 | 0.6599 | 0.6390 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [ai-forever/ruElectra-medium](https://huggingface.co/ai-forever/ruElectra-medium) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 2.6856
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+ - Accuracy: 0.7135
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+ - Recall: 0.6688
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+ - Precision: 0.7321
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+ - F1: 0.6855
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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: 0.0001
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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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | No log | 1.0 | 200 | 1.0503 | 0.6462 | 0.5404 | 0.5573 | 0.5309 |
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+ | No log | 2.0 | 400 | 0.9312 | 0.6842 | 0.6358 | 0.6068 | 0.5981 |
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+ | 0.9761 | 3.0 | 600 | 0.9141 | 0.7193 | 0.6410 | 0.6629 | 0.6447 |
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+ | 0.9761 | 4.0 | 800 | 1.1036 | 0.7193 | 0.6453 | 0.6843 | 0.6516 |
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+ | 0.3389 | 5.0 | 1000 | 1.3396 | 0.7135 | 0.6512 | 0.7203 | 0.6576 |
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+ | 0.3389 | 6.0 | 1200 | 1.4660 | 0.7251 | 0.6688 | 0.7587 | 0.6759 |
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+ | 0.3389 | 7.0 | 1400 | 1.4835 | 0.7135 | 0.6656 | 0.6910 | 0.6640 |
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+ | 0.1627 | 8.0 | 1600 | 1.8635 | 0.7135 | 0.6535 | 0.7441 | 0.6673 |
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+ | 0.1627 | 9.0 | 1800 | 1.5689 | 0.7368 | 0.7140 | 0.7412 | 0.7192 |
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+ | 0.0893 | 10.0 | 2000 | 1.9628 | 0.7047 | 0.6885 | 0.7050 | 0.6842 |
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+ | 0.0893 | 11.0 | 2200 | 1.9155 | 0.7339 | 0.6814 | 0.7328 | 0.6995 |
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+ | 0.0893 | 12.0 | 2400 | 2.0020 | 0.7398 | 0.7086 | 0.7351 | 0.7064 |
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+ | 0.0781 | 13.0 | 2600 | 2.0432 | 0.7193 | 0.7005 | 0.7265 | 0.6876 |
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+ | 0.0781 | 14.0 | 2800 | 1.8877 | 0.7544 | 0.7385 | 0.7634 | 0.7415 |
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+ | 0.0435 | 15.0 | 3000 | 2.2208 | 0.7281 | 0.6876 | 0.7271 | 0.6871 |
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+ | 0.0435 | 16.0 | 3200 | 1.9514 | 0.7485 | 0.7071 | 0.7438 | 0.7169 |
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+ | 0.0435 | 17.0 | 3400 | 2.0358 | 0.7368 | 0.7551 | 0.7406 | 0.7402 |
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+ | 0.0405 | 18.0 | 3600 | 2.2364 | 0.7310 | 0.6250 | 0.6655 | 0.6307 |
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+ | 0.0405 | 19.0 | 3800 | 2.3225 | 0.7164 | 0.6779 | 0.7234 | 0.6868 |
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+ | 0.0511 | 20.0 | 4000 | 2.1369 | 0.7310 | 0.6826 | 0.7670 | 0.7089 |
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+ | 0.0511 | 21.0 | 4200 | 2.2229 | 0.7427 | 0.6981 | 0.7783 | 0.7145 |
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+ | 0.0511 | 22.0 | 4400 | 2.2711 | 0.7222 | 0.6650 | 0.7214 | 0.6671 |
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+ | 0.0382 | 23.0 | 4600 | 2.4241 | 0.7222 | 0.6556 | 0.7826 | 0.6834 |
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+ | 0.0382 | 24.0 | 4800 | 2.0575 | 0.7368 | 0.6767 | 0.7238 | 0.6804 |
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+ | 0.0413 | 25.0 | 5000 | 2.5485 | 0.7076 | 0.6681 | 0.6842 | 0.6682 |
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+ | 0.0413 | 26.0 | 5200 | 2.2235 | 0.7222 | 0.6474 | 0.6889 | 0.6536 |
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+ | 0.0413 | 27.0 | 5400 | 2.5252 | 0.7105 | 0.6835 | 0.7028 | 0.6793 |
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+ | 0.035 | 28.0 | 5600 | 2.5843 | 0.7164 | 0.6438 | 0.7341 | 0.6654 |
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+ | 0.035 | 29.0 | 5800 | 2.6856 | 0.7135 | 0.6688 | 0.7321 | 0.6855 |
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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