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Swaraj66
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bert-finetuned-ner

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  1. README.md +13 -13
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@@ -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: 0.9372926343729263
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  - name: Recall
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  type: recall
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- value: 0.9508582968697409
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  - name: F1
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  type: f1
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- value: 0.9440267335004177
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  - name: Accuracy
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  type: accuracy
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- value: 0.9864602342968152
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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-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0617
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- - Precision: 0.9373
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- - Recall: 0.9509
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- - F1: 0.9440
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- - Accuracy: 0.9865
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  ## Model description
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@@ -80,14 +80,14 @@ 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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- | 0.0754 | 1.0 | 1756 | 0.0655 | 0.9068 | 0.9349 | 0.9206 | 0.9824 |
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- | 0.0339 | 2.0 | 3512 | 0.0662 | 0.9314 | 0.9458 | 0.9385 | 0.9853 |
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- | 0.0212 | 3.0 | 5268 | 0.0617 | 0.9373 | 0.9509 | 0.9440 | 0.9865 |
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  ### Framework versions
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  - Transformers 4.50.0
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  - Pytorch 2.6.0+cu124
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- - Datasets 3.4.1
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  - Tokenizers 0.21.1
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9320885657633841
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  - name: Recall
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  type: recall
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+ value: 0.9493436553349041
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  - name: F1
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  type: f1
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+ value: 0.9406369851592463
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9860481544710661
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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 the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0639
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+ - Precision: 0.9321
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+ - Recall: 0.9493
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+ - F1: 0.9406
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+ - Accuracy: 0.9860
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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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+ | 0.0772 | 1.0 | 1756 | 0.0676 | 0.9020 | 0.9359 | 0.9186 | 0.9816 |
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+ | 0.0356 | 2.0 | 3512 | 0.0684 | 0.9288 | 0.9440 | 0.9363 | 0.9845 |
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+ | 0.0228 | 3.0 | 5268 | 0.0639 | 0.9321 | 0.9493 | 0.9406 | 0.9860 |
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  ### Framework versions
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  - Transformers 4.50.0
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  - Pytorch 2.6.0+cu124
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+ - Datasets 3.5.0
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  - Tokenizers 0.21.1