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stulcrad
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CNEC_1_1_robeczech-base

Token Classification
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roberta
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  1. README.md +20 -19
  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: 0.8354960234407702
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  - name: Recall
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  type: recall
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- value: 0.8812362030905078
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  - name: F1
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  type: f1
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- value: 0.8577567683712936
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  - name: Accuracy
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  type: accuracy
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- value: 0.9450064850843061
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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: 0.2816
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- - Precision: 0.8355
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- - Recall: 0.8812
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- - F1: 0.8578
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- - Accuracy: 0.9450
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  ## Model description
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@@ -73,19 +73,20 @@ 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: 80
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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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- | 0.7852 | 10.2 | 1500 | 0.6287 | 0.3577 | 0.2375 | 0.2855 | 0.8413 |
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- | 0.3806 | 20.41 | 3000 | 0.3455 | 0.7275 | 0.7779 | 0.7519 | 0.9240 |
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- | 0.2384 | 30.61 | 4500 | 0.2764 | 0.8139 | 0.8552 | 0.8340 | 0.9383 |
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- | 0.1722 | 40.82 | 6000 | 0.2640 | 0.8361 | 0.8693 | 0.8524 | 0.9450 |
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- | 0.1357 | 51.02 | 7500 | 0.2666 | 0.8362 | 0.8702 | 0.8529 | 0.9454 |
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- | 0.1115 | 61.22 | 9000 | 0.2697 | 0.8423 | 0.8751 | 0.8584 | 0.9457 |
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- | 0.098 | 71.43 | 10500 | 0.2816 | 0.8355 | 0.8812 | 0.8578 | 0.9450 |
 
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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: 0.8457943925233645
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  - name: Recall
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  type: recall
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+ value: 0.879028697571744
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  - name: F1
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  type: f1
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+ value: 0.8620913617666162
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9463553826199741
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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: 0.2802
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+ - Precision: 0.8458
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+ - Recall: 0.8790
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+ - F1: 0.8621
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+ - Accuracy: 0.9464
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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: 60
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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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+ | 0.7893 | 6.8 | 1000 | 0.5640 | 0.5842 | 0.4671 | 0.5191 | 0.8830 |
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+ | 0.3629 | 13.61 | 2000 | 0.3352 | 0.7879 | 0.8035 | 0.7956 | 0.9299 |
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+ | 0.2227 | 20.41 | 3000 | 0.2793 | 0.8264 | 0.8490 | 0.8375 | 0.9437 |
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+ | 0.1577 | 27.21 | 4000 | 0.2646 | 0.8495 | 0.8649 | 0.8571 | 0.9477 |
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+ | 0.1193 | 34.01 | 5000 | 0.2713 | 0.8491 | 0.8720 | 0.8604 | 0.9478 |
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+ | 0.0947 | 40.82 | 6000 | 0.2670 | 0.8560 | 0.8742 | 0.8650 | 0.9494 |
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+ | 0.0792 | 47.62 | 7000 | 0.2791 | 0.8551 | 0.8804 | 0.8675 | 0.9479 |
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+ | 0.0706 | 54.42 | 8000 | 0.2802 | 0.8458 | 0.8790 | 0.8621 | 0.9464 |
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  ### Framework versions
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