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bert-finetuned-ner
sergak0/bert-finetuned-ner
bert-finetuned-ner is a token classification model from sergak0. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Oct 11, 2025
- Updated
- Oct 11, 2025
- SHA
- c98c44f0
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Base models
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 BAAI/bge-small-en-v1.5 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0846
- Precision: 0.8800
- Recall: 0.9143
- F1: 0.8968
- Accuracy: 0.9793
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 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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.3294 | 1.0 | 1250 | 0.1350 | 0.8106 | 0.8686 | 0.8386 | 0.9704 |
| 0.1143 | 2.0 | 2500 | 0.1004 | 0.8690 | 0.9057 | 0.8869 | 0.9775 |
| 0.0746 | 3.0 | 3750 | 0.0846 | 0.8800 | 0.9143 | 0.8968 | 0.9793 |
Framework versions
- Transformers 4.50.0
- Pytorch 2.4.1+cu121
- Datasets 4.2.0
- Tokenizers 0.21.4
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Oct 11, 2025
- Updated
- Oct 11, 2025