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BioMedical_NER-maccrobat-distilbert

Token Classification
Transformers
PyTorch
Safetensors
distilbert
Generated from Trainer
Inference Endpoints
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Training complete

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  1. README.md +76 -56
  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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3622
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- - Precision: 0.8525
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- - Recall: 0.9225
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- - F1: 0.8861
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- - Accuracy: 0.9419
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  ## Model description
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@@ -49,62 +49,82 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 50
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  ### Training results
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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 | 45 | 1.7742 | 0.6341 | 0.0055 | 0.0109 | 0.6272 |
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- | No log | 2.0 | 90 | 1.4622 | 0.3671 | 0.1659 | 0.2286 | 0.6546 |
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- | No log | 3.0 | 135 | 1.2643 | 0.3409 | 0.2941 | 0.3158 | 0.6742 |
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- | No log | 4.0 | 180 | 1.1501 | 0.4039 | 0.4283 | 0.4157 | 0.7028 |
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- | No log | 5.0 | 225 | 1.0938 | 0.4145 | 0.5229 | 0.4624 | 0.7098 |
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- | No log | 6.0 | 270 | 1.0439 | 0.4371 | 0.5780 | 0.4978 | 0.7257 |
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- | No log | 7.0 | 315 | 0.9442 | 0.4997 | 0.6147 | 0.5513 | 0.7583 |
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- | No log | 8.0 | 360 | 0.9376 | 0.5126 | 0.6591 | 0.5767 | 0.7629 |
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- | No log | 9.0 | 405 | 0.8024 | 0.5512 | 0.6753 | 0.6070 | 0.7921 |
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- | No log | 10.0 | 450 | 0.7367 | 0.5949 | 0.6842 | 0.6364 | 0.8121 |
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- | No log | 11.0 | 495 | 0.7276 | 0.5959 | 0.7209 | 0.6525 | 0.8222 |
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- | 1.0374 | 12.0 | 540 | 0.6606 | 0.6329 | 0.7289 | 0.6775 | 0.8369 |
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- | 1.0374 | 13.0 | 585 | 0.6466 | 0.6335 | 0.7530 | 0.6881 | 0.8423 |
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- | 1.0374 | 14.0 | 630 | 0.6825 | 0.6200 | 0.7716 | 0.6875 | 0.8397 |
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- | 1.0374 | 15.0 | 675 | 0.5721 | 0.6767 | 0.7777 | 0.7237 | 0.8657 |
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- | 1.0374 | 16.0 | 720 | 0.5446 | 0.6965 | 0.7876 | 0.7393 | 0.8771 |
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- | 1.0374 | 17.0 | 765 | 0.5136 | 0.7475 | 0.7881 | 0.7672 | 0.8872 |
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- | 1.0374 | 18.0 | 810 | 0.5248 | 0.7185 | 0.8218 | 0.7667 | 0.8866 |
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- | 1.0374 | 19.0 | 855 | 0.4944 | 0.7494 | 0.8284 | 0.7869 | 0.8961 |
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- | 1.0374 | 20.0 | 900 | 0.5092 | 0.7299 | 0.8391 | 0.7807 | 0.8920 |
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- | 1.0374 | 21.0 | 945 | 0.4491 | 0.7775 | 0.8393 | 0.8072 | 0.9083 |
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- | 1.0374 | 22.0 | 990 | 0.4400 | 0.7744 | 0.8537 | 0.8121 | 0.9104 |
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- | 0.3072 | 23.0 | 1035 | 0.4593 | 0.7689 | 0.8619 | 0.8128 | 0.9091 |
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- | 0.3072 | 24.0 | 1080 | 0.4547 | 0.7726 | 0.8670 | 0.8171 | 0.9094 |
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- | 0.3072 | 25.0 | 1125 | 0.4425 | 0.7825 | 0.8689 | 0.8234 | 0.9141 |
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- | 0.3072 | 26.0 | 1170 | 0.4229 | 0.7949 | 0.8712 | 0.8313 | 0.9184 |
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- | 0.3072 | 27.0 | 1215 | 0.4015 | 0.8192 | 0.8731 | 0.8453 | 0.9241 |
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- | 0.3072 | 28.0 | 1260 | 0.4222 | 0.7995 | 0.8771 | 0.8365 | 0.9197 |
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- | 0.3072 | 29.0 | 1305 | 0.4119 | 0.8017 | 0.8849 | 0.8413 | 0.9217 |
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- | 0.3072 | 30.0 | 1350 | 0.3960 | 0.8217 | 0.8864 | 0.8528 | 0.9276 |
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- | 0.3072 | 31.0 | 1395 | 0.3965 | 0.8204 | 0.8919 | 0.8547 | 0.9278 |
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- | 0.3072 | 32.0 | 1440 | 0.3936 | 0.8222 | 0.8972 | 0.8581 | 0.9282 |
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- | 0.3072 | 33.0 | 1485 | 0.3979 | 0.8263 | 0.8991 | 0.8612 | 0.9299 |
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- | 0.1369 | 34.0 | 1530 | 0.3799 | 0.8352 | 0.8989 | 0.8659 | 0.9333 |
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- | 0.1369 | 35.0 | 1575 | 0.3712 | 0.8407 | 0.9054 | 0.8718 | 0.9356 |
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- | 0.1369 | 36.0 | 1620 | 0.3648 | 0.8443 | 0.9046 | 0.8734 | 0.9368 |
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- | 0.1369 | 37.0 | 1665 | 0.3640 | 0.8414 | 0.9048 | 0.8719 | 0.9368 |
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- | 0.1369 | 38.0 | 1710 | 0.3632 | 0.8473 | 0.9088 | 0.8770 | 0.9385 |
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- | 0.1369 | 39.0 | 1755 | 0.3765 | 0.8369 | 0.9118 | 0.8727 | 0.9363 |
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- | 0.1369 | 40.0 | 1800 | 0.3686 | 0.8465 | 0.9107 | 0.8775 | 0.9382 |
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- | 0.1369 | 41.0 | 1845 | 0.3644 | 0.8461 | 0.9158 | 0.8796 | 0.9389 |
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- | 0.1369 | 42.0 | 1890 | 0.3676 | 0.8446 | 0.9156 | 0.8786 | 0.9390 |
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- | 0.1369 | 43.0 | 1935 | 0.3667 | 0.8451 | 0.9177 | 0.8799 | 0.9397 |
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- | 0.1369 | 44.0 | 1980 | 0.3622 | 0.8502 | 0.9189 | 0.8832 | 0.9407 |
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- | 0.0844 | 45.0 | 2025 | 0.3628 | 0.8535 | 0.9187 | 0.8849 | 0.9410 |
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- | 0.0844 | 46.0 | 2070 | 0.3677 | 0.8510 | 0.9198 | 0.8840 | 0.9406 |
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- | 0.0844 | 47.0 | 2115 | 0.3670 | 0.8521 | 0.9229 | 0.8861 | 0.9410 |
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- | 0.0844 | 48.0 | 2160 | 0.3627 | 0.8532 | 0.9227 | 0.8866 | 0.9417 |
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- | 0.0844 | 49.0 | 2205 | 0.3640 | 0.8511 | 0.9232 | 0.8857 | 0.9417 |
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- | 0.0844 | 50.0 | 2250 | 0.3622 | 0.8525 | 0.9225 | 0.8861 | 0.9419 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
20
 
21
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
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+ - Loss: 0.3418
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+ - Precision: 0.8858
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+ - Recall: 0.9578
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+ - F1: 0.9204
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+ - Accuracy: 0.9541
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 70
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  ### Training results
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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 | 45 | 1.8297 | 0.0 | 0.0 | 0.0 | 0.6197 |
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+ | No log | 2.0 | 90 | 1.5738 | 0.2713 | 0.0490 | 0.0830 | 0.6324 |
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+ | No log | 3.0 | 135 | 1.3283 | 0.3165 | 0.2269 | 0.2644 | 0.6654 |
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+ | No log | 4.0 | 180 | 1.1738 | 0.3634 | 0.3538 | 0.3585 | 0.6915 |
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+ | No log | 5.0 | 225 | 1.1003 | 0.4080 | 0.5041 | 0.4510 | 0.7074 |
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+ | No log | 6.0 | 270 | 1.0484 | 0.4339 | 0.5727 | 0.4937 | 0.7193 |
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+ | No log | 7.0 | 315 | 0.9841 | 0.4685 | 0.6209 | 0.5340 | 0.7434 |
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+ | No log | 8.0 | 360 | 0.8765 | 0.5286 | 0.6369 | 0.5777 | 0.7712 |
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+ | No log | 9.0 | 405 | 0.8037 | 0.5638 | 0.6635 | 0.6096 | 0.7922 |
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+ | No log | 10.0 | 450 | 0.7924 | 0.5572 | 0.7013 | 0.6210 | 0.8008 |
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+ | No log | 11.0 | 495 | 0.7403 | 0.5732 | 0.7228 | 0.6394 | 0.8143 |
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+ | 1.0716 | 12.0 | 540 | 0.6235 | 0.6636 | 0.7083 | 0.6852 | 0.8457 |
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+ | 1.0716 | 13.0 | 585 | 0.6182 | 0.6418 | 0.7448 | 0.6895 | 0.8487 |
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+ | 1.0716 | 14.0 | 630 | 0.6498 | 0.6312 | 0.7724 | 0.6947 | 0.8456 |
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+ | 1.0716 | 15.0 | 675 | 0.5830 | 0.6638 | 0.7874 | 0.7204 | 0.8650 |
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+ | 1.0716 | 16.0 | 720 | 0.5199 | 0.6992 | 0.7954 | 0.7442 | 0.8804 |
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+ | 1.0716 | 17.0 | 765 | 0.5470 | 0.7129 | 0.8119 | 0.7592 | 0.8836 |
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+ | 1.0716 | 18.0 | 810 | 0.5065 | 0.7269 | 0.8318 | 0.7758 | 0.8920 |
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+ | 1.0716 | 19.0 | 855 | 0.4645 | 0.7521 | 0.8353 | 0.7916 | 0.9018 |
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+ | 1.0716 | 20.0 | 900 | 0.5204 | 0.7240 | 0.8501 | 0.7820 | 0.8915 |
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+ | 1.0716 | 21.0 | 945 | 0.4383 | 0.7660 | 0.8495 | 0.8056 | 0.9078 |
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+ | 1.0716 | 22.0 | 990 | 0.4345 | 0.7659 | 0.8662 | 0.8130 | 0.9127 |
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+ | 0.2987 | 23.0 | 1035 | 0.4492 | 0.7675 | 0.8733 | 0.8170 | 0.9118 |
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+ | 0.2987 | 24.0 | 1080 | 0.4654 | 0.7691 | 0.8805 | 0.8211 | 0.9101 |
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+ | 0.2987 | 25.0 | 1125 | 0.4186 | 0.7995 | 0.8778 | 0.8368 | 0.9216 |
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+ | 0.2987 | 26.0 | 1170 | 0.3898 | 0.8131 | 0.8871 | 0.8485 | 0.9269 |
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+ | 0.2987 | 27.0 | 1215 | 0.4057 | 0.8041 | 0.8928 | 0.8461 | 0.9256 |
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+ | 0.2987 | 28.0 | 1260 | 0.3916 | 0.8156 | 0.8938 | 0.8529 | 0.9290 |
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+ | 0.2987 | 29.0 | 1305 | 0.3771 | 0.8250 | 0.8989 | 0.8604 | 0.9317 |
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+ | 0.2987 | 30.0 | 1350 | 0.3690 | 0.8253 | 0.8997 | 0.8609 | 0.9337 |
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+ | 0.2987 | 31.0 | 1395 | 0.3716 | 0.8320 | 0.9084 | 0.8685 | 0.9357 |
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+ | 0.2987 | 32.0 | 1440 | 0.3764 | 0.8278 | 0.9115 | 0.8677 | 0.9349 |
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+ | 0.2987 | 33.0 | 1485 | 0.3549 | 0.8389 | 0.9113 | 0.8736 | 0.9376 |
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+ | 0.1133 | 34.0 | 1530 | 0.3715 | 0.8368 | 0.9160 | 0.8746 | 0.9372 |
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+ | 0.1133 | 35.0 | 1575 | 0.3621 | 0.8452 | 0.9208 | 0.8814 | 0.9401 |
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+ | 0.1133 | 36.0 | 1620 | 0.3533 | 0.8489 | 0.9248 | 0.8852 | 0.9420 |
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+ | 0.1133 | 37.0 | 1665 | 0.3471 | 0.8540 | 0.9259 | 0.8885 | 0.9427 |
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+ | 0.1133 | 38.0 | 1710 | 0.3492 | 0.8504 | 0.9263 | 0.8867 | 0.9423 |
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+ | 0.1133 | 39.0 | 1755 | 0.3570 | 0.8572 | 0.9327 | 0.8933 | 0.9441 |
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+ | 0.1133 | 40.0 | 1800 | 0.3647 | 0.8535 | 0.9348 | 0.8923 | 0.9436 |
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+ | 0.1133 | 41.0 | 1845 | 0.3500 | 0.8656 | 0.9381 | 0.9004 | 0.9466 |
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+ | 0.1133 | 42.0 | 1890 | 0.3570 | 0.8594 | 0.9405 | 0.8981 | 0.9452 |
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+ | 0.1133 | 43.0 | 1935 | 0.3545 | 0.8695 | 0.9436 | 0.9050 | 0.9480 |
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+ | 0.1133 | 44.0 | 1980 | 0.3578 | 0.8660 | 0.9415 | 0.9022 | 0.9467 |
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+ | 0.0575 | 45.0 | 2025 | 0.3384 | 0.8723 | 0.9419 | 0.9058 | 0.9498 |
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+ | 0.0575 | 46.0 | 2070 | 0.3450 | 0.8755 | 0.9472 | 0.9100 | 0.9502 |
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+ | 0.0575 | 47.0 | 2115 | 0.3468 | 0.8736 | 0.9495 | 0.9100 | 0.9500 |
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+ | 0.0575 | 48.0 | 2160 | 0.3488 | 0.8706 | 0.9502 | 0.9087 | 0.9505 |
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+ | 0.0575 | 49.0 | 2205 | 0.3480 | 0.8738 | 0.9517 | 0.9111 | 0.9506 |
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+ | 0.0575 | 50.0 | 2250 | 0.3474 | 0.8725 | 0.9504 | 0.9098 | 0.9501 |
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+ | 0.0575 | 51.0 | 2295 | 0.3463 | 0.8711 | 0.9498 | 0.9087 | 0.9499 |
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+ | 0.0575 | 52.0 | 2340 | 0.3328 | 0.8782 | 0.9525 | 0.9138 | 0.9518 |
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+ | 0.0575 | 53.0 | 2385 | 0.3550 | 0.8738 | 0.9527 | 0.9115 | 0.9508 |
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+ | 0.0575 | 54.0 | 2430 | 0.3351 | 0.8777 | 0.9525 | 0.9135 | 0.9526 |
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+ | 0.0575 | 55.0 | 2475 | 0.3438 | 0.8781 | 0.9548 | 0.9148 | 0.9521 |
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+ | 0.0364 | 56.0 | 2520 | 0.3452 | 0.8797 | 0.9540 | 0.9153 | 0.9521 |
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+ | 0.0364 | 57.0 | 2565 | 0.3496 | 0.8810 | 0.9561 | 0.9170 | 0.9523 |
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+ | 0.0364 | 58.0 | 2610 | 0.3472 | 0.8802 | 0.9557 | 0.9164 | 0.9525 |
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+ | 0.0364 | 59.0 | 2655 | 0.3476 | 0.8813 | 0.9559 | 0.9171 | 0.9530 |
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+ | 0.0364 | 60.0 | 2700 | 0.3413 | 0.8839 | 0.9563 | 0.9187 | 0.9536 |
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+ | 0.0364 | 61.0 | 2745 | 0.3395 | 0.8839 | 0.9563 | 0.9187 | 0.9538 |
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+ | 0.0364 | 62.0 | 2790 | 0.3417 | 0.8843 | 0.9580 | 0.9196 | 0.9537 |
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+ | 0.0364 | 63.0 | 2835 | 0.3397 | 0.8846 | 0.9563 | 0.9191 | 0.9536 |
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+ | 0.0364 | 64.0 | 2880 | 0.3428 | 0.8839 | 0.9576 | 0.9192 | 0.9534 |
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+ | 0.0364 | 65.0 | 2925 | 0.3411 | 0.8847 | 0.9576 | 0.9197 | 0.9539 |
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+ | 0.0364 | 66.0 | 2970 | 0.3442 | 0.8849 | 0.9574 | 0.9197 | 0.9538 |
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+ | 0.028 | 67.0 | 3015 | 0.3444 | 0.8844 | 0.9578 | 0.9196 | 0.9538 |
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+ | 0.028 | 68.0 | 3060 | 0.3437 | 0.8857 | 0.9584 | 0.9206 | 0.9541 |
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+ | 0.028 | 69.0 | 3105 | 0.3411 | 0.8857 | 0.9582 | 0.9205 | 0.9540 |
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+ | 0.028 | 70.0 | 3150 | 0.3418 | 0.8858 | 0.9578 | 0.9204 | 0.9541 |
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  ### Framework versions
pytorch_model.bin CHANGED
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