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bert-finetuned-ner-v4.001
terzimert/bert-finetuned-ner-v4.001
bert-finetuned-ner-v4.001 is a token classification model from terzimert. 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
- Mar 31, 2023
- Updated
- Mar 31, 2023
- SHA
- 51698ffc
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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-v4.001
This model is a fine-tuned version of bert-base-multilingual-cased on the caner dataset. It achieves the following results on the evaluation set:
- Loss: 0.4995
- Precision: 0.8814
- Recall: 0.8208
- F1: 0.8500
- Accuracy: 0.9327
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 |
|---|---|---|---|---|---|---|---|
| 0.2518 | 1.0 | 4842 | 0.5403 | 0.8354 | 0.7712 | 0.8020 | 0.9178 |
| 0.1364 | 2.0 | 9684 | 0.4746 | 0.8728 | 0.8016 | 0.8357 | 0.9287 |
| 0.0915 | 3.0 | 14526 | 0.4995 | 0.8814 | 0.8208 | 0.8500 | 0.9327 |
Framework versions
- Transformers 4.27.4
- Pytorch 1.13.1+cu116
- Datasets 2.11.0
- Tokenizers 0.13.2
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Mar 31, 2023
- Updated
- Mar 31, 2023