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stulcrad
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CNEC_extended_xlm-roberta-large

Token Classification
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Czech
xlm-roberta
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  1. README.md +17 -17
  2. runs/Feb26_20-03-36_n29/events.out.tfevents.1708974220.n29.36067.0 +2 -2
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: 0.8526912181303116
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  - name: Recall
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  type: recall
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- value: 0.8962779156327544
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  - name: F1
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  type: f1
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- value: 0.8739414468908783
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  - name: Accuracy
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  type: accuracy
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- value: 0.9765807962529274
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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 [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1428
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- - Precision: 0.8527
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- - Recall: 0.8963
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- - F1: 0.8739
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- - Accuracy: 0.9766
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  ## Model description
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@@ -79,14 +79,14 @@ 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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- | 0.2508 | 1.12 | 500 | 0.1431 | 0.7569 | 0.8481 | 0.7999 | 0.9672 |
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- | 0.1103 | 2.24 | 1000 | 0.1169 | 0.7717 | 0.8541 | 0.8108 | 0.9704 |
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- | 0.0731 | 3.36 | 1500 | 0.1134 | 0.8066 | 0.8715 | 0.8378 | 0.9749 |
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- | 0.0527 | 4.47 | 2000 | 0.1137 | 0.8360 | 0.8928 | 0.8635 | 0.9767 |
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- | 0.039 | 5.59 | 2500 | 0.1248 | 0.8364 | 0.8854 | 0.8602 | 0.9755 |
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- | 0.0265 | 6.71 | 3000 | 0.1252 | 0.8427 | 0.8878 | 0.8647 | 0.9769 |
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- | 0.0206 | 7.83 | 3500 | 0.1424 | 0.8473 | 0.8953 | 0.8707 | 0.9757 |
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- | 0.0148 | 8.95 | 4000 | 0.1428 | 0.8527 | 0.8963 | 0.8739 | 0.9766 |
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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: 0.8556554661618552
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  - name: Recall
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  type: recall
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+ value: 0.8972704714640198
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  - name: F1
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  type: f1
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+ value: 0.8759689922480619
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9759953161592506
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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 [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the cnec dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1541
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+ - Precision: 0.8557
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+ - Recall: 0.8973
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+ - F1: 0.8760
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+ - Accuracy: 0.9760
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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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+ | 0.2518 | 1.12 | 500 | 0.1312 | 0.7219 | 0.8427 | 0.7777 | 0.9649 |
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+ | 0.0996 | 2.24 | 1000 | 0.1222 | 0.8003 | 0.8511 | 0.8249 | 0.9677 |
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+ | 0.0652 | 3.36 | 1500 | 0.1259 | 0.8137 | 0.8734 | 0.8425 | 0.9730 |
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+ | 0.0421 | 4.47 | 2000 | 0.1293 | 0.8306 | 0.8859 | 0.8573 | 0.9739 |
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+ | 0.0277 | 5.59 | 2500 | 0.1519 | 0.8320 | 0.8799 | 0.8553 | 0.9742 |
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+ | 0.0169 | 6.71 | 3000 | 0.1342 | 0.8516 | 0.8968 | 0.8736 | 0.9756 |
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+ | 0.0116 | 7.83 | 3500 | 0.1496 | 0.8540 | 0.8973 | 0.8751 | 0.9760 |
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+ | 0.0065 | 8.95 | 4000 | 0.1541 | 0.8557 | 0.8973 | 0.8760 | 0.9760 |
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  ### Framework versions
runs/Feb26_20-03-36_n29/events.out.tfevents.1708974220.n29.36067.0 CHANGED
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