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bert-NER-500
wwwtwwwt/bert-NER-500
bert-NER-500 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
- a9700e50
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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-500
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: 0.2171
- Precision: 0.6480
- Recall: 0.7115
- F1: 0.6783
- Accuracy: 0.9480
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 | 65 | 0.4473 | 0.4267 | 0.2955 | 0.3492 | 0.8825 |
| No log | 2.0 | 130 | 0.2540 | 0.6038 | 0.6557 | 0.6287 | 0.9371 |
| No log | 3.0 | 195 | 0.2171 | 0.6480 | 0.7115 | 0.6783 | 0.9480 |
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