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---
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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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- 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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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0648
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- Precision: 0.9329
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- Recall: 0.9502
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- F1: 0.9415
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- Accuracy: 0.9856
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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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| 0.0732 | 1.0 | 1756 | 0.0685 | 0.9057 | 0.9355 | 0.9204 | 0.9819 |
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| 0.0345 | 2.0 | 3512 | 0.0641 | 0.9349 | 0.9475 | 0.9412 | 0.9857 |
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| 0.0212 | 3.0 | 5268 | 0.0648 | 0.9329 | 0.9502 | 0.9415 | 0.9856 |
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### Framework versions
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- Transformers 4.39.3
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- Pytorch 2.2.2
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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