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bert-NER-200
wwwtwwwt/bert-NER-200
bert-NER-200 is a token classification model from wwwtwwwt. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Apr 16, 2025
- Updated
- Apr 17, 2025
- SHA
- ed31cb3d
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Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
bert-NER-200
This model is a fine-tuned version of bert-large-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4226
- Precision: 0.3886
- Recall: 0.3462
- F1: 0.3662
- Accuracy: 0.8946
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 30 | 0.6392 | 0.6071 | 0.0143 | 0.0280 | 0.8338 |
| No log | 2.0 | 60 | 0.4601 | 0.3525 | 0.2427 | 0.2875 | 0.8770 |
| No log | 3.0 | 90 | 0.4226 | 0.3886 | 0.3462 | 0.3662 | 0.8946 |
Framework versions
- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Apr 16, 2025
- Updated
- Apr 17, 2025