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Token Classification
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roberta
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  1. README.md +12 -12
  2. model.safetensors +1 -1
  3. runs/Jun18_13-47-33_Darius_PC/events.out.tfevents.1718689654.Darius_PC.18056.0 +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: 0.9456827121183742
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  - name: Recall
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  type: recall
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- value: 0.9537211938043068
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  - name: F1
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  type: f1
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- value: 0.9496849431016647
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  - name: Accuracy
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  type: accuracy
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- value: 0.9877113313491455
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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 [distilroberta-base](https://huggingface.co/distilroberta-base) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0587
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- - Precision: 0.9457
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- - Recall: 0.9537
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- - F1: 0.9497
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- - Accuracy: 0.9877
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  ## Model description
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@@ -79,9 +79,9 @@ 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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- | 0.0796 | 1.0 | 1756 | 0.0731 | 0.9235 | 0.9362 | 0.9298 | 0.9829 |
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- | 0.0378 | 2.0 | 3512 | 0.0647 | 0.9450 | 0.9500 | 0.9475 | 0.9868 |
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- | 0.0189 | 3.0 | 5268 | 0.0587 | 0.9457 | 0.9537 | 0.9497 | 0.9877 |
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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: 0.9508227550540668
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  - name: Recall
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  type: recall
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+ value: 0.955043445409898
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  - name: F1
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  type: f1
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+ value: 0.9529284267068746
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9880181645239483
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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 [distilroberta-base](https://huggingface.co/distilroberta-base) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0590
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+ - Precision: 0.9508
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+ - Recall: 0.9550
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+ - F1: 0.9529
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+ - Accuracy: 0.9880
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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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+ | 0.0803 | 1.0 | 1756 | 0.0725 | 0.9236 | 0.9313 | 0.9274 | 0.9820 |
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+ | 0.0373 | 2.0 | 3512 | 0.0627 | 0.9453 | 0.9487 | 0.9470 | 0.9868 |
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+ | 0.0213 | 3.0 | 5268 | 0.0590 | 0.9508 | 0.9550 | 0.9529 | 0.9880 |
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  ### Framework versions
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