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bert-finetuned-ner

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  1. README.md +13 -14
README.md CHANGED
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  metrics:
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  - precision
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  - recall
 
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  - accuracy
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  model-index:
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  - name: bert-finetuned-ner
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  results: []
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- datasets:
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- - conll2002
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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
@@ -21,15 +20,15 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1793
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- - Precision: 0.7556
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- - Recall: 0.8015
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- - Overall F1: 0.7779
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- - Accuracy: 0.9669
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  ## Model description
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- This is a model trained on the conll2002 dataset that can be used for Named Entity Recognition. This model uses bert-base-cased as the underlying encoder.
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  ## Intended uses & limitations
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Overall F1 | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:----------:|:--------:|
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- | 0.0221 | 1.0 | 1041 | 0.1840 | 0.7401 | 0.7976 | 0.7678 | 0.9662 |
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- | 0.0362 | 2.0 | 2082 | 0.1571 | 0.7490 | 0.8028 | 0.7750 | 0.9662 |
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- | 0.0187 | 3.0 | 3123 | 0.1793 | 0.7556 | 0.8015 | 0.7779 | 0.9669 |
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  ### Framework versions
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  - Transformers 4.40.2
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  - Pytorch 2.2.1+cu121
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  - Datasets 2.19.1
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- - Tokenizers 0.19.1
 
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  metrics:
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  - precision
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  - recall
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+ - f1
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  - accuracy
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  model-index:
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  - name: bert-finetuned-ner
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  results: []
 
 
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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-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1564
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+ - Precision: 0.75
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+ - Recall: 0.8045
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+ - F1: 0.7763
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+ - Accuracy: 0.9682
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  ## Model description
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+ More information needed
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  ## Intended uses & limitations
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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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+ | 0.0439 | 1.0 | 1041 | 0.1419 | 0.7464 | 0.7918 | 0.7684 | 0.9674 |
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+ | 0.0458 | 2.0 | 2082 | 0.1414 | 0.7493 | 0.8028 | 0.7752 | 0.9677 |
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+ | 0.0261 | 3.0 | 3123 | 0.1564 | 0.75 | 0.8045 | 0.7763 | 0.9682 |
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  ### Framework versions
 
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  - Transformers 4.40.2
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  - Pytorch 2.2.1+cu121
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  - Datasets 2.19.1
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+ - Tokenizers 0.19.1