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bert-ner
fahmiaziz/bert-ner
bert-ner is a token classification model from fahmiaziz. 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
- Sep 29, 2023
- Updated
- Sep 29, 2023
- SHA
- d1ae4bae
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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
This model is a fine-tuned version of bert-base-uncased on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.0648
- Precision: 0.9420
- Recall: 0.9513
- F1: 0.9466
- Accuracy: 0.9864
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.2234 | 1.0 | 878 | 0.0648 | 0.9110 | 0.9327 | 0.9217 | 0.9821 |
| 0.0443 | 2.0 | 1756 | 0.0552 | 0.9345 | 0.9432 | 0.9388 | 0.9854 |
| 0.0258 | 3.0 | 2634 | 0.0571 | 0.9385 | 0.9451 | 0.9418 | 0.9856 |
| 0.0139 | 4.0 | 3512 | 0.0623 | 0.9413 | 0.9500 | 0.9456 | 0.9863 |
| 0.0098 | 5.0 | 4390 | 0.0648 | 0.9420 | 0.9513 | 0.9466 | 0.9864 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Sep 29, 2023
- Updated
- Sep 29, 2023