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IceBERT-finetuned-ner
bergurth/IceBERT-finetuned-ner
IceBERT-finetuned-ner is a token classification model from bergurth. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as gpl-3.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- License
- gpl-3.0
- Model type
- roberta
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Oct 5, 2021
- SHA
- a9d63442
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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. -->
IceBERT-finetuned-ner
This model is a fine-tuned version of vesteinn/IceBERT on the mim_gold_ner dataset. It achieves the following results on the evaluation set:
- Loss: 0.0783
- Precision: 0.8873
- Recall: 0.8627
- F1: 0.8748
- Accuracy: 0.9848
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: 16
- eval_batch_size: 16
- 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 |
|---|---|---|---|---|---|---|---|
| 0.0539 | 1.0 | 2904 | 0.0768 | 0.8732 | 0.8453 | 0.8590 | 0.9833 |
| 0.0281 | 2.0 | 5808 | 0.0737 | 0.8781 | 0.8492 | 0.8634 | 0.9838 |
| 0.0166 | 3.0 | 8712 | 0.0783 | 0.8873 | 0.8627 | 0.8748 | 0.9848 |
Framework versions
- Transformers 4.11.2
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
- Task
- Token Classification
- Library
- transformers
- Type
- roberta
- License
- gpl-3.0
- Created
- Mar 2, 2022
- Updated
- Oct 5, 2021