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bert-finetuned-ner-balancedData
terzimert/bert-finetuned-ner-balancedData
bert-finetuned-ner-balancedData 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 24, 2023
- Updated
- Mar 24, 2023
- SHA
- 67f79c16
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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-balancedData
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: 0.6584
- Precision: 0.7292
- Recall: 0.7543
- F1: 0.7415
- Accuracy: 0.8972
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 |
|---|---|---|---|---|---|---|---|
| 0.3967 | 1.0 | 2396 | 0.6536 | 0.6556 | 0.7356 | 0.6933 | 0.8696 |
| 0.2112 | 2.0 | 4792 | 0.6049 | 0.7372 | 0.7658 | 0.7512 | 0.8958 |
| 0.1353 | 3.0 | 7188 | 0.6584 | 0.7292 | 0.7543 | 0.7415 | 0.8972 |
Framework versions
- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Mar 24, 2023
- Updated
- Mar 24, 2023