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bert-finetuned-ner
Norika/bert-finetuned-ner
bert-finetuned-ner is a token classification model from Norika. 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
- Oct 30, 2023
- Updated
- Nov 1, 2023
- SHA
- e1ec5444
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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 wnut_17 dataset. It achieves the following results on the evaluation set:
- Loss: 0.2964
- Precision: 0.5422
- Recall: 0.3809
- F1: 0.4475
- Accuracy: 0.9476
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 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 425 | 0.2617 | 0.5380 | 0.3086 | 0.3922 | 0.9427 |
| 0.1895 | 2.0 | 850 | 0.2944 | 0.5930 | 0.3160 | 0.4123 | 0.9443 |
| 0.0702 | 3.0 | 1275 | 0.2964 | 0.5422 | 0.3809 | 0.4475 | 0.9476 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Oct 30, 2023
- Updated
- Nov 1, 2023