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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- conll2003
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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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- 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: conll2003
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type: conll2003
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config: conll2003
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split: train
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args: conll2003
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metrics:
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- name: Precision
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type: precision
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value: 0.9292912605319676
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- name: Recall
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type: recall
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value: 0.946650959272972
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- name: F1
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type: f1
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value: 0.9378907878282617
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- name: Accuracy
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type: accuracy
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value: 0.9855330546888798
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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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should probably proofread and complete it, then remove this comment. -->
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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 the conll2003 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0643
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- Precision: 0.9293
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- Recall: 0.9467
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- F1: 0.9379
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- Accuracy: 0.9855
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- num_epochs: 3
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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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| 0.0866 | 1.0 | 1756 | 0.0722 | 0.9116 | 0.9290 | 0.9202 | 0.9816 |
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| 0.034 | 2.0 | 3512 | 0.0676 | 0.9273 | 0.9461 | 0.9366 | 0.9852 |
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| 0.0189 | 3.0 | 5268 | 0.0643 | 0.9293 | 0.9467 | 0.9379 | 0.9855 |
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### Framework versions
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- Transformers 4.25.1
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- Pytorch 1.13.0+cu116
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- Datasets 2.8.0
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- Tokenizers 0.13.2
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