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bert-finetuned-ner
asebaq/bert-finetuned-ner
bert-finetuned-ner is a machine learning model from asebaq. Use it for the machine learning task on the model card, and read the license before you ship it in a product. 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
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Apr 19, 2025
- Updated
- Apr 19, 2025
- SHA
- f815981d
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: 0.0618
- Precision: 0.9358
- Recall: 0.9500
- F1: 0.9429
- 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: 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 |
|---|---|---|---|---|---|---|---|
| 0.0739 | 1.0 | 1756 | 0.0668 | 0.9048 | 0.9320 | 0.9182 | 0.9811 |
| 0.0379 | 2.0 | 3512 | 0.0688 | 0.9346 | 0.9451 | 0.9398 | 0.9852 |
| 0.0205 | 3.0 | 5268 | 0.0618 | 0.9358 | 0.9500 | 0.9429 | 0.9865 |
Framework versions
- Transformers 4.38.0
- Pytorch 2.5.1+cu121
- Datasets 3.5.0
- Tokenizers 0.15.2
- Type
- bert
- License
- apache-2.0
- Created
- Apr 19, 2025
- Updated
- Apr 19, 2025