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Lilya
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distilbert-base-uncased-ner-invoiceSenderRecipient-all-inv-26-12

Token Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Inference Endpoints
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update model card README.md

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@@ -18,11 +18,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0240
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- - Precision: 0.8517
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- - Recall: 0.9006
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- - F1: 0.8755
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- - Accuracy: 0.9918
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  ## Model description
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@@ -53,41 +53,41 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.0302 | 0.06 | 500 | 0.0312 | 0.8199 | 0.8274 | 0.8236 | 0.9892 |
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- | 0.028 | 0.11 | 1000 | 0.0308 | 0.8108 | 0.8591 | 0.8343 | 0.9894 |
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- | 0.0255 | 0.17 | 1500 | 0.0319 | 0.8278 | 0.8226 | 0.8252 | 0.9890 |
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- | 0.0253 | 0.23 | 2000 | 0.0314 | 0.8046 | 0.8674 | 0.8348 | 0.9893 |
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- | 0.0253 | 0.28 | 2500 | 0.0329 | 0.7914 | 0.8783 | 0.8326 | 0.9887 |
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- | 0.0239 | 0.34 | 3000 | 0.0309 | 0.7907 | 0.8871 | 0.8361 | 0.9893 |
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- | 0.0238 | 0.4 | 3500 | 0.0313 | 0.8109 | 0.8822 | 0.8450 | 0.9898 |
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- | 0.0242 | 0.46 | 4000 | 0.0292 | 0.8290 | 0.8646 | 0.8464 | 0.9902 |
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- | 0.0239 | 0.51 | 4500 | 0.0302 | 0.7938 | 0.8859 | 0.8373 | 0.9895 |
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- | 0.0237 | 0.57 | 5000 | 0.0291 | 0.8246 | 0.8795 | 0.8512 | 0.9903 |
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- | 0.0254 | 0.63 | 5500 | 0.0296 | 0.8160 | 0.8884 | 0.8507 | 0.9901 |
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- | 0.0248 | 0.68 | 6000 | 0.0270 | 0.8269 | 0.8845 | 0.8547 | 0.9906 |
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- | 0.029 | 0.74 | 6500 | 0.0271 | 0.8283 | 0.8874 | 0.8568 | 0.9906 |
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- | 0.0277 | 0.8 | 7000 | 0.0259 | 0.8374 | 0.8823 | 0.8593 | 0.9909 |
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- | 0.0276 | 0.85 | 7500 | 0.0264 | 0.8317 | 0.8930 | 0.8612 | 0.9909 |
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- | 0.0263 | 0.91 | 8000 | 0.0252 | 0.8412 | 0.8901 | 0.8650 | 0.9911 |
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- | 0.0271 | 0.97 | 8500 | 0.0247 | 0.8531 | 0.8756 | 0.8642 | 0.9913 |
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- | 0.0242 | 1.02 | 9000 | 0.0256 | 0.8459 | 0.8909 | 0.8678 | 0.9913 |
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- | 0.022 | 1.08 | 9500 | 0.0262 | 0.8310 | 0.9000 | 0.8641 | 0.9910 |
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- | 0.0212 | 1.14 | 10000 | 0.0251 | 0.8581 | 0.8780 | 0.8679 | 0.9914 |
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- | 0.0215 | 1.19 | 10500 | 0.0255 | 0.8441 | 0.8952 | 0.8689 | 0.9914 |
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- | 0.0209 | 1.25 | 11000 | 0.0253 | 0.8378 | 0.8982 | 0.8669 | 0.9913 |
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- | 0.021 | 1.31 | 11500 | 0.0253 | 0.8358 | 0.9049 | 0.8690 | 0.9913 |
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- | 0.0211 | 1.37 | 12000 | 0.0252 | 0.8437 | 0.8989 | 0.8704 | 0.9915 |
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- | 0.0205 | 1.42 | 12500 | 0.0249 | 0.8464 | 0.8980 | 0.8714 | 0.9916 |
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- | 0.0206 | 1.48 | 13000 | 0.0247 | 0.8440 | 0.8973 | 0.8698 | 0.9916 |
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- | 0.02 | 1.54 | 13500 | 0.0246 | 0.8528 | 0.8939 | 0.8729 | 0.9916 |
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- | 0.0208 | 1.59 | 14000 | 0.0249 | 0.8397 | 0.9063 | 0.8718 | 0.9915 |
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- | 0.0205 | 1.65 | 14500 | 0.0241 | 0.8549 | 0.8932 | 0.8736 | 0.9917 |
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- | 0.0204 | 1.71 | 15000 | 0.0241 | 0.8534 | 0.8976 | 0.8749 | 0.9918 |
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- | 0.0196 | 1.76 | 15500 | 0.0246 | 0.8464 | 0.9038 | 0.8741 | 0.9917 |
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- | 0.0202 | 1.82 | 16000 | 0.0239 | 0.8514 | 0.8990 | 0.8746 | 0.9918 |
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- | 0.0197 | 1.88 | 16500 | 0.0242 | 0.8494 | 0.9008 | 0.8744 | 0.9917 |
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- | 0.0198 | 1.93 | 17000 | 0.0240 | 0.8514 | 0.9005 | 0.8752 | 0.9918 |
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- | 0.0202 | 1.99 | 17500 | 0.0240 | 0.8517 | 0.9007 | 0.8755 | 0.9918 |
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  ### Framework versions
 
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  This model was trained from scratch on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0245
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+ - Precision: 0.8602
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+ - Recall: 0.9015
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+ - F1: 0.8804
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+ - Accuracy: 0.9921
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0157 | 0.06 | 500 | 0.0286 | 0.8569 | 0.8605 | 0.8587 | 0.9908 |
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+ | 0.0156 | 0.11 | 1000 | 0.0287 | 0.8340 | 0.8940 | 0.8630 | 0.9910 |
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+ | 0.0141 | 0.17 | 1500 | 0.0296 | 0.8368 | 0.8853 | 0.8604 | 0.9908 |
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+ | 0.014 | 0.23 | 2000 | 0.0296 | 0.8356 | 0.8915 | 0.8627 | 0.9910 |
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+ | 0.0144 | 0.28 | 2500 | 0.0306 | 0.8310 | 0.8896 | 0.8593 | 0.9906 |
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+ | 0.0136 | 0.34 | 3000 | 0.0290 | 0.8384 | 0.8842 | 0.8607 | 0.9910 |
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+ | 0.0134 | 0.4 | 3500 | 0.0306 | 0.8514 | 0.8779 | 0.8645 | 0.9912 |
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+ | 0.0138 | 0.46 | 4000 | 0.0307 | 0.8475 | 0.8790 | 0.8630 | 0.9910 |
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+ | 0.0139 | 0.51 | 4500 | 0.0301 | 0.8208 | 0.9002 | 0.8587 | 0.9908 |
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+ | 0.014 | 0.57 | 5000 | 0.0320 | 0.8307 | 0.8981 | 0.8631 | 0.9909 |
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+ | 0.0155 | 0.63 | 5500 | 0.0307 | 0.8329 | 0.8992 | 0.8648 | 0.9909 |
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+ | 0.0154 | 0.68 | 6000 | 0.0268 | 0.8403 | 0.8971 | 0.8677 | 0.9913 |
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+ | 0.0178 | 0.74 | 6500 | 0.0269 | 0.8548 | 0.8869 | 0.8705 | 0.9916 |
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+ | 0.0177 | 0.8 | 7000 | 0.0268 | 0.8552 | 0.8904 | 0.8725 | 0.9917 |
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+ | 0.0178 | 0.85 | 7500 | 0.0267 | 0.8498 | 0.8972 | 0.8729 | 0.9917 |
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+ | 0.017 | 0.91 | 8000 | 0.0259 | 0.8517 | 0.8969 | 0.8737 | 0.9917 |
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+ | 0.0176 | 0.97 | 8500 | 0.0249 | 0.8523 | 0.8921 | 0.8717 | 0.9916 |
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+ | 0.0157 | 1.02 | 9000 | 0.0274 | 0.8535 | 0.8990 | 0.8757 | 0.9918 |
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+ | 0.0134 | 1.08 | 9500 | 0.0293 | 0.8375 | 0.9060 | 0.8704 | 0.9913 |
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+ | 0.0132 | 1.14 | 10000 | 0.0278 | 0.8648 | 0.8864 | 0.8755 | 0.9919 |
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+ | 0.0135 | 1.19 | 10500 | 0.0273 | 0.8540 | 0.8958 | 0.8744 | 0.9917 |
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+ | 0.0133 | 1.25 | 11000 | 0.0277 | 0.8442 | 0.9034 | 0.8728 | 0.9917 |
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+ | 0.0138 | 1.31 | 11500 | 0.0276 | 0.8484 | 0.9035 | 0.8751 | 0.9917 |
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+ | 0.0141 | 1.37 | 12000 | 0.0274 | 0.8501 | 0.9016 | 0.8751 | 0.9918 |
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+ | 0.0137 | 1.42 | 12500 | 0.0274 | 0.8529 | 0.9010 | 0.8763 | 0.9918 |
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+ | 0.014 | 1.48 | 13000 | 0.0269 | 0.8509 | 0.9022 | 0.8758 | 0.9919 |
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+ | 0.0141 | 1.54 | 13500 | 0.0260 | 0.8653 | 0.8926 | 0.8787 | 0.9920 |
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+ | 0.0149 | 1.59 | 14000 | 0.0258 | 0.8521 | 0.9048 | 0.8777 | 0.9919 |
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+ | 0.0149 | 1.65 | 14500 | 0.0257 | 0.8607 | 0.8980 | 0.8790 | 0.9921 |
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+ | 0.0152 | 1.71 | 15000 | 0.0257 | 0.8596 | 0.9001 | 0.8794 | 0.9920 |
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+ | 0.015 | 1.76 | 15500 | 0.0257 | 0.8556 | 0.9032 | 0.8788 | 0.9920 |
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+ | 0.0157 | 1.82 | 16000 | 0.0248 | 0.8620 | 0.8993 | 0.8802 | 0.9922 |
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+ | 0.0158 | 1.88 | 16500 | 0.0251 | 0.8573 | 0.9036 | 0.8798 | 0.9921 |
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+ | 0.0163 | 1.93 | 17000 | 0.0248 | 0.8579 | 0.9034 | 0.8800 | 0.9921 |
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+ | 0.017 | 1.99 | 17500 | 0.0245 | 0.8602 | 0.9015 | 0.8804 | 0.9921 |
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  ### Framework versions