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bert-large-200-ner

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End of training

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  1. README.md +12 -12
  2. model.safetensors +1 -1
  3. runs/Apr17_12-30-18_DESKTOP-IMESE8D/events.out.tfevents.1744864220.DESKTOP-IMESE8D.465474.0 +3 -0
  4. runs/Apr17_12-30-23_DESKTOP-IMESE8D/events.out.tfevents.1744864223.DESKTOP-IMESE8D.465550.0 +3 -0
  5. training_args.bin +1 -1
README.md CHANGED
@@ -26,16 +26,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8042925278219396
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  - name: Recall
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  type: recall
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- value: 0.8513968360821272
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  - name: F1
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  type: f1
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- value: 0.8271746239372139
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  - name: Accuracy
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  type: accuracy
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- value: 0.9720999961060707
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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
@@ -45,11 +45,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1335
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- - Precision: 0.8043
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- - Recall: 0.8514
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- - F1: 0.8272
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- - Accuracy: 0.9721
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  ## Model description
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@@ -80,9 +80,9 @@ 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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- | No log | 1.0 | 100 | 0.2986 | 0.5786 | 0.6313 | 0.6038 | 0.9344 |
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- | No log | 2.0 | 200 | 0.1594 | 0.7696 | 0.8213 | 0.7946 | 0.9681 |
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- | No log | 3.0 | 300 | 0.1335 | 0.8043 | 0.8514 | 0.8272 | 0.9721 |
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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: 0.06935851514164768
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  - name: Recall
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  type: recall
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+ value: 0.035846516324469876
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  - name: F1
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  type: f1
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+ value: 0.04726506157772107
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  - name: Accuracy
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  type: accuracy
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+ value: 0.7959775709668626
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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 [bert-large-cased](https://huggingface.co/bert-large-cased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2990
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+ - Precision: 0.0694
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+ - Recall: 0.0358
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+ - F1: 0.0473
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+ - Accuracy: 0.7960
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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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+ | No log | 1.0 | 3 | 1.6411 | 0.0368 | 0.0577 | 0.0450 | 0.7000 |
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+ | No log | 2.0 | 6 | 1.3858 | 0.0587 | 0.0370 | 0.0454 | 0.7865 |
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+ | No log | 3.0 | 9 | 1.2990 | 0.0694 | 0.0358 | 0.0473 | 0.7960 |
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  ### Framework versions
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