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bert-finetuned-ner
illiadagil/bert-finetuned-ner
bert-finetuned-ner is a token classification model from illiadagil. 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
- distilbert
- Access
- Public
- Created
- Dec 12, 2022
- Updated
- Dec 19, 2022
- SHA
- ffb46601
Try a prompt
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 distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0474
- Precision: 0.8185
- Recall: 0.8754
- F1: 0.8460
- Accuracy: 0.9837
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 |
|---|---|---|---|---|---|---|---|
| 0.0548 | 1.0 | 2112 | 0.0526 | 0.7847 | 0.8358 | 0.8094 | 0.9822 |
| 0.0413 | 2.0 | 4224 | 0.0477 | 0.8172 | 0.8713 | 0.8434 | 0.9837 |
| 0.0355 | 3.0 | 6336 | 0.0474 | 0.8185 | 0.8754 | 0.8460 | 0.9837 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2
- Task
- Token Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- Dec 12, 2022
- Updated
- Dec 19, 2022