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0xtimi
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bert-finetuned-ner

Token Classification
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Safetensors
bert
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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: 0.9253879168042258
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  - name: Recall
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  type: recall
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- value: 0.9434533826994278
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  - name: F1
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  type: f1
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- value: 0.9343333333333333
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  - name: Accuracy
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  type: accuracy
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- value: 0.9848119149938188
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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: 0.0585
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- - Precision: 0.9254
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- - Recall: 0.9435
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- - F1: 0.9343
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- - Accuracy: 0.9848
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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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- | No log | 1.0 | 439 | 0.0721 | 0.8881 | 0.9202 | 0.9039 | 0.9795 |
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- | 0.196 | 2.0 | 878 | 0.0600 | 0.9218 | 0.9403 | 0.9309 | 0.9843 |
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- | 0.0482 | 3.0 | 1317 | 0.0585 | 0.9254 | 0.9435 | 0.9343 | 0.9848 |
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  ### Framework versions
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  - Transformers 4.53.3
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- - Pytorch 2.5.1.post302
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  - Datasets 3.6.0
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  - Tokenizers 0.21.4-dev.0
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9226327944572749
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  - name: Recall
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  type: recall
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+ value: 0.941265567149108
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  - name: F1
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  type: f1
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+ value: 0.9318560479840053
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9845617236710426
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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: 0.0592
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+ - Precision: 0.9226
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+ - Recall: 0.9413
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+ - F1: 0.9319
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+ - Accuracy: 0.9846
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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 | 439 | 0.0713 | 0.8850 | 0.9207 | 0.9025 | 0.9798 |
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+ | 0.194 | 2.0 | 878 | 0.0602 | 0.9166 | 0.9392 | 0.9278 | 0.9838 |
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+ | 0.0484 | 3.0 | 1317 | 0.0592 | 0.9226 | 0.9413 | 0.9319 | 0.9846 |
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  ### Framework versions
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  - Transformers 4.53.3
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+ - Pytorch 2.7.1+cu126
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  - Datasets 3.6.0
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  - Tokenizers 0.21.4-dev.0