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stulcrad
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CNEC_2_0_ext_robeczech-base

Token Classification
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roberta
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  1. README.md +25 -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: 0.8721215090641842
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  - name: Recall
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  type: recall
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- value: 0.8833746898263027
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  - name: F1
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  type: f1
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- value: 0.8777120315581853
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  - name: Accuracy
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  type: accuracy
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- value: 0.9689010132501948
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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: 0.1565
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- - Precision: 0.8721
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- - Recall: 0.8834
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- - F1: 0.8777
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- - Accuracy: 0.9689
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  ## Model description
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@@ -68,22 +68,28 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 64
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- - eval_batch_size: 64
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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: 25
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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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- | 0.4321 | 4.46 | 500 | 0.1753 | 0.8613 | 0.8352 | 0.8481 | 0.9643 |
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- | 0.0909 | 8.93 | 1000 | 0.1457 | 0.8640 | 0.8888 | 0.8762 | 0.9695 |
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- | 0.0484 | 13.39 | 1500 | 0.1493 | 0.8662 | 0.8834 | 0.8747 | 0.9681 |
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- | 0.0328 | 17.86 | 2000 | 0.1515 | 0.8710 | 0.8844 | 0.8776 | 0.9691 |
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- | 0.0255 | 22.32 | 2500 | 0.1565 | 0.8721 | 0.8834 | 0.8777 | 0.9689 |
 
 
 
 
 
 
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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: 0.8633093525179856
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  - name: Recall
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  type: recall
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+ value: 0.8933002481389578
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  - name: F1
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  type: f1
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+ value: 0.8780487804878048
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9703429462197973
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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: 0.1663
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+ - Precision: 0.8633
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+ - Recall: 0.8933
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+ - F1: 0.8780
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+ - Accuracy: 0.9703
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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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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+ | 0.2593 | 4.46 | 1000 | 0.1653 | 0.8195 | 0.8223 | 0.8209 | 0.9593 |
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+ | 0.1209 | 8.93 | 2000 | 0.1355 | 0.8441 | 0.8789 | 0.8612 | 0.9679 |
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+ | 0.0763 | 13.39 | 3000 | 0.1310 | 0.8591 | 0.8893 | 0.8739 | 0.9709 |
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+ | 0.0539 | 17.86 | 4000 | 0.1383 | 0.8656 | 0.8953 | 0.8802 | 0.9719 |
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+ | 0.0403 | 22.32 | 5000 | 0.1392 | 0.8626 | 0.8943 | 0.8782 | 0.9710 |
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+ | 0.0316 | 26.79 | 6000 | 0.1539 | 0.8606 | 0.8948 | 0.8774 | 0.9712 |
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+ | 0.0254 | 31.25 | 7000 | 0.1552 | 0.8660 | 0.8913 | 0.8785 | 0.9706 |
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+ | 0.0211 | 35.71 | 8000 | 0.1621 | 0.8658 | 0.8968 | 0.8810 | 0.9701 |
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+ | 0.0183 | 40.18 | 9000 | 0.1593 | 0.8688 | 0.8973 | 0.8828 | 0.9718 |
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+ | 0.0161 | 44.64 | 10000 | 0.1638 | 0.8653 | 0.8993 | 0.8820 | 0.9714 |
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+ | 0.015 | 49.11 | 11000 | 0.1663 | 0.8633 | 0.8933 | 0.8780 | 0.9703 |
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  ### Framework versions
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