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stulcrad
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CNEC_2_0_Supertypes_robeczech-base

Token Classification
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roberta
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  1. README.md +15 -21
  2. model.safetensors +1 -1
README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8559556786703602
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  - name: Recall
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  type: recall
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- value: 0.8934324659231723
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  - name: F1
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  type: f1
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- value: 0.8742926434923202
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  - name: Accuracy
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  type: accuracy
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- value: 0.962524121960633
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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
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [ufal/robeczech-base](https://huggingface.co/ufal/robeczech-base) on the cnec dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2234
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- - Precision: 0.8560
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- - Recall: 0.8934
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- - F1: 0.8743
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- - Accuracy: 0.9625
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  ## Model description
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@@ -73,23 +73,17 @@ 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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- | 0.286 | 4.44 | 1000 | 0.2057 | 0.8033 | 0.8199 | 0.8115 | 0.9488 |
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- | 0.1397 | 8.89 | 2000 | 0.1546 | 0.8384 | 0.8831 | 0.8602 | 0.9634 |
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- | 0.0936 | 13.33 | 3000 | 0.1607 | 0.8420 | 0.8848 | 0.8628 | 0.9625 |
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- | 0.0668 | 17.78 | 4000 | 0.1682 | 0.8490 | 0.8872 | 0.8677 | 0.9623 |
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- | 0.0513 | 22.22 | 5000 | 0.1855 | 0.8569 | 0.8905 | 0.8734 | 0.9635 |
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- | 0.0407 | 26.67 | 6000 | 0.1917 | 0.8586 | 0.8951 | 0.8764 | 0.9633 |
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- | 0.0355 | 31.11 | 7000 | 0.1997 | 0.8505 | 0.8881 | 0.8689 | 0.9619 |
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- | 0.0287 | 35.56 | 8000 | 0.2041 | 0.8610 | 0.8955 | 0.8779 | 0.9637 |
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- | 0.0253 | 40.0 | 9000 | 0.2177 | 0.8544 | 0.8897 | 0.8717 | 0.9619 |
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- | 0.0221 | 44.44 | 10000 | 0.2244 | 0.8521 | 0.8947 | 0.8729 | 0.9625 |
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- | 0.0208 | 48.89 | 11000 | 0.2234 | 0.8560 | 0.8934 | 0.8743 | 0.9625 |
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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: 0.8543461237274863
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  - name: Recall
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  type: recall
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+ value: 0.9012804626187526
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  - name: F1
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  type: f1
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+ value: 0.8771859296482412
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9623311462755693
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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 [ufal/robeczech-base](https://huggingface.co/ufal/robeczech-base) on the cnec dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2853
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+ - Precision: 0.8543
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+ - Recall: 0.9013
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+ - F1: 0.8772
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+ - Accuracy: 0.9623
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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: 100
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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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+ | 0.065 | 17.78 | 4000 | 0.1785 | 0.8466 | 0.8893 | 0.8674 | 0.9608 |
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+ | 0.0242 | 35.56 | 8000 | 0.2351 | 0.8534 | 0.8922 | 0.8724 | 0.9616 |
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+ | 0.012 | 53.33 | 12000 | 0.2634 | 0.8537 | 0.8988 | 0.8757 | 0.9615 |
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+ | 0.0075 | 71.11 | 16000 | 0.2730 | 0.8606 | 0.9050 | 0.8822 | 0.9641 |
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+ | 0.0049 | 88.89 | 20000 | 0.2853 | 0.8543 | 0.9013 | 0.8772 | 0.9623 |
 
 
 
 
 
 
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  ### Framework versions
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