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bert-finetuned-ner

Token Classification
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albert
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  ---
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- license: mit
 
 
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  tags:
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  - generated_from_trainer
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- datasets:
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- - ner-tr
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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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- - task:
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- name: Token Classification
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- type: token-classification
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- dataset:
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- name: ner-tr
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- type: ner-tr
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- config: NERTR
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- split: train
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- args: NERTR
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- metrics:
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- - name: Precision
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- type: precision
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- value: 1.0
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- - name: Recall
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- type: recall
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- value: 1.0
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- - name: F1
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- type: f1
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- value: 1.0
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- - name: Accuracy
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- type: accuracy
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- value: 1.0
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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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  # bert-finetuned-ner
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- This model is a fine-tuned version of [dbmdz/bert-base-turkish-cased](https://huggingface.co/dbmdz/bert-base-turkish-cased) on the ner-tr dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0002
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- - Precision: 1.0
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- - Recall: 1.0
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- - F1: 1.0
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- - Accuracy: 1.0
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  ## Model description
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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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- | 0.2603 | 1.0 | 529 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.002 | 2.0 | 1058 | 0.0003 | 1.0 | 1.0 | 1.0 | 1.0 |
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- | 0.001 | 3.0 | 1587 | 0.0002 | 1.0 | 1.0 | 1.0 | 1.0 |
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  ### Framework versions
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- - Transformers 4.22.1
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- - Pytorch 1.12.1+cu113
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- - Datasets 2.4.0
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- - Tokenizers 0.12.1
 
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  ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: bert-base-cased
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  tags:
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  - generated_from_trainer
 
 
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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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  ---
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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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  # bert-finetuned-ner
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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: 0.0854
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+ - Precision: 0.9707
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+ - Recall: 0.9773
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+ - F1: 0.9740
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+ - Accuracy: 0.9845
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  ## Model description
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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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+ | 0.0627 | 1.0 | 5285 | 0.1139 | 0.9563 | 0.9675 | 0.9619 | 0.9785 |
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+ | 0.0502 | 2.0 | 10570 | 0.0914 | 0.9675 | 0.9744 | 0.9709 | 0.9829 |
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+ | 0.0686 | 3.0 | 15855 | 0.0854 | 0.9707 | 0.9773 | 0.9740 | 0.9845 |
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  ### Framework versions
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.1+cu121
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+ - Datasets 3.0.1
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+ - Tokenizers 0.19.1