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BERT-finetuned-ner-conll2003
ViktorDo/BERT-finetuned-ner-conll2003
BERT-finetuned-ner-conll2003 is a token classification model from ViktorDo. 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
- May 18, 2023
- Updated
- May 19, 2023
- SHA
- c47f3f69
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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-conll2003
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: 0.0607
- Precision: 0.9317
- Recall: 0.9478
- F1: 0.9397
- Accuracy: 0.9862
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0855 | 1.0 | 1756 | 0.0637 | 0.9180 | 0.9369 | 0.9274 | 0.9832 |
| 0.0354 | 2.0 | 3512 | 0.0639 | 0.9301 | 0.9473 | 0.9386 | 0.9859 |
| 0.018 | 3.0 | 5268 | 0.0607 | 0.9317 | 0.9478 | 0.9397 | 0.9862 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- May 18, 2023
- Updated
- May 19, 2023