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bert-finetuned-ner
princend/bert-finetuned-ner
bert-finetuned-ner is a token classification model from princend. 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
- Dec 25, 2024
- Updated
- Dec 26, 2024
- SHA
- e939d63f
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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-finetuned-ner
This model is a fine-tuned version of bert-base-cased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0589
- Precision: 0.9321
- Recall: 0.9495
- F1: 0.9407
- Accuracy: 0.9865
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0769 | 1.0 | 1756 | 0.0643 | 0.8979 | 0.9337 | 0.9154 | 0.9821 |
| 0.0344 | 2.0 | 3512 | 0.0637 | 0.9298 | 0.9446 | 0.9371 | 0.9854 |
| 0.0235 | 3.0 | 5268 | 0.0589 | 0.9321 | 0.9495 | 0.9407 | 0.9865 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cpu
- Datasets 3.2.0
- Tokenizers 0.21.0
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Dec 25, 2024
- Updated
- Dec 26, 2024