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README.md
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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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datasets:
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- conll2003
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metrics:
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- f1
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- precision
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- recall
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model-index:
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- name: bert-base-cased-conll2003-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: validation
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args: conll2003
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metrics:
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- name: F1
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type: f1
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value: 0.911552663970459
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- name: Precision
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type: precision
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value: 0.9053440447083478
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- name: Recall
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type: recall
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value: 0.9178470254957507
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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-base-cased-conll2003-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: 0.1194
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- F1: 0.9116
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- Precision: 0.9053
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- Recall: 0.9178
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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: 16
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- eval_batch_size: 16
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- seed: 42
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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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 | F1 | Precision | Recall |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|
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| 0.1659 | 1.0 | 878 | 0.0414 | 0.9342 | 0.9314 | 0.9371 |
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| 0.0279 | 2.0 | 1756 | 0.0383 | 0.9480 | 0.9463 | 0.9497 |
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| 0.0145 | 3.0 | 2634 | 0.0374 | 0.9518 | 0.9497 | 0.9539 |
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### Framework versions
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- Transformers 5.0.0
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- Pytorch 2.10.0+cu128
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- Datasets 2.21.0
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- Tokenizers 0.22.2
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