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stulcrad
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CNEC_2_0_Supertypes_slavicbert

Token Classification
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Safetensors
bert
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Eval Results
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  1. README.md +27 -18
  2. model.safetensors +1 -1
README.md CHANGED
@@ -24,16 +24,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8512165935380933
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  - name: Recall
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  type: recall
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- value: 0.8814539446509707
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  - name: F1
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  type: f1
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- value: 0.8660714285714285
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  - name: Accuracy
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  type: accuracy
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- value: 0.9612966749981064
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -43,11 +43,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the cnec dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2522
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- - Precision: 0.8512
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- - Recall: 0.8815
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- - F1: 0.8661
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- - Accuracy: 0.9613
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  ## Model description
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@@ -78,15 +78,24 @@ 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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- | 0.1496 | 2.22 | 1000 | 0.1549 | 0.8135 | 0.8538 | 0.8331 | 0.9564 |
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- | 0.083 | 4.44 | 2000 | 0.1590 | 0.8302 | 0.8765 | 0.8527 | 0.9616 |
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- | 0.0526 | 6.67 | 3000 | 0.1680 | 0.8340 | 0.8864 | 0.8594 | 0.9620 |
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- | 0.0341 | 8.89 | 4000 | 0.1855 | 0.8405 | 0.8794 | 0.8595 | 0.9618 |
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- | 0.0242 | 11.11 | 5000 | 0.2032 | 0.8493 | 0.8798 | 0.8643 | 0.9621 |
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- | 0.0154 | 13.33 | 6000 | 0.2241 | 0.8527 | 0.8823 | 0.8672 | 0.9631 |
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- | 0.0128 | 15.56 | 7000 | 0.2522 | 0.8476 | 0.8777 | 0.8624 | 0.9611 |
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- | 0.0109 | 17.78 | 8000 | 0.2484 | 0.8510 | 0.8798 | 0.8652 | 0.9619 |
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- | 0.0083 | 20.0 | 9000 | 0.2522 | 0.8512 | 0.8815 | 0.8661 | 0.9613 |
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.8392434988179669
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  - name: Recall
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  type: recall
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+ value: 0.8798017348203222
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  - name: F1
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  type: f1
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+ value: 0.8590441621294617
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9608422328258729
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [DeepPavlov/bert-base-bg-cs-pl-ru-cased](https://huggingface.co/DeepPavlov/bert-base-bg-cs-pl-ru-cased) on the cnec dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1943
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+ - Precision: 0.8392
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+ - Recall: 0.8798
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+ - F1: 0.8590
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+ - Accuracy: 0.9608
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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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+ | 0.5752 | 1.11 | 500 | 0.2395 | 0.6751 | 0.7381 | 0.7052 | 0.9324 |
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+ | 0.2671 | 2.22 | 1000 | 0.1882 | 0.7554 | 0.8253 | 0.7888 | 0.9476 |
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+ | 0.2057 | 3.33 | 1500 | 0.1645 | 0.7985 | 0.8563 | 0.8264 | 0.9533 |
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+ | 0.1713 | 4.44 | 2000 | 0.1852 | 0.7936 | 0.8542 | 0.8228 | 0.9531 |
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+ | 0.1562 | 5.56 | 2500 | 0.1724 | 0.8051 | 0.8583 | 0.8309 | 0.9550 |
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+ | 0.1289 | 6.67 | 3000 | 0.1639 | 0.8203 | 0.8711 | 0.8450 | 0.9598 |
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+ | 0.1186 | 7.78 | 3500 | 0.1763 | 0.8216 | 0.8691 | 0.8446 | 0.9585 |
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+ | 0.1031 | 8.89 | 4000 | 0.1764 | 0.8267 | 0.8707 | 0.8481 | 0.9584 |
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+ | 0.0973 | 10.0 | 4500 | 0.1868 | 0.8330 | 0.8798 | 0.8558 | 0.9597 |
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+ | 0.0877 | 11.11 | 5000 | 0.1818 | 0.8304 | 0.8835 | 0.8561 | 0.9594 |
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+ | 0.0811 | 12.22 | 5500 | 0.1842 | 0.8374 | 0.8872 | 0.8616 | 0.9613 |
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+ | 0.0735 | 13.33 | 6000 | 0.1858 | 0.8393 | 0.8777 | 0.8581 | 0.9616 |
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+ | 0.07 | 14.44 | 6500 | 0.1933 | 0.8349 | 0.8794 | 0.8566 | 0.9602 |
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+ | 0.0674 | 15.56 | 7000 | 0.1913 | 0.8384 | 0.8852 | 0.8612 | 0.9613 |
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+ | 0.0671 | 16.67 | 7500 | 0.1927 | 0.8340 | 0.8823 | 0.8575 | 0.9606 |
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+ | 0.0606 | 17.78 | 8000 | 0.1963 | 0.8398 | 0.8815 | 0.8601 | 0.9607 |
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+ | 0.0601 | 18.89 | 8500 | 0.1925 | 0.8395 | 0.8794 | 0.8590 | 0.9615 |
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+ | 0.0559 | 20.0 | 9000 | 0.1943 | 0.8392 | 0.8798 | 0.8590 | 0.9608 |
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  ### Framework versions
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