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bert-finetuned-ner-best
Monishhh24/bert-finetuned-ner-best
bert-finetuned-ner-best is a token classification model from Monishhh24. 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
- Nov 11, 2024
- Updated
- Nov 11, 2024
- SHA
- e0e9fce3
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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-best
This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1873
- Precision: 0.8679
- Recall: 0.8971
- F1: 0.8822
- Accuracy: 0.9550
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: 5e-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 |
|---|---|---|---|---|---|---|---|
| 0.1542 | 1.0 | 249 | 0.1875 | 0.8427 | 0.8761 | 0.8591 | 0.9476 |
| 0.058 | 2.0 | 498 | 0.1873 | 0.8679 | 0.8971 | 0.8822 | 0.9550 |
| 0.035 | 3.0 | 747 | 0.2050 | 0.8655 | 0.8985 | 0.8817 | 0.9547 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Nov 11, 2024
- Updated
- Nov 11, 2024