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IceBERT-finetuned-ner
thorduragust/IceBERT-finetuned-ner
IceBERT-finetuned-ner is a token classification model from thorduragust. 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
- 2b5c72ce
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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: 0.0787
- Precision: 0.8948
- Recall: 0.8622
- F1: 0.8782
- Accuracy: 0.9852
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 |
|---|---|---|---|---|---|---|---|
| 0.0526 | 1.0 | 2904 | 0.0746 | 0.8802 | 0.8539 | 0.8668 | 0.9836 |
| 0.0264 | 2.0 | 5808 | 0.0711 | 0.8777 | 0.8594 | 0.8684 | 0.9843 |
| 0.0161 | 3.0 | 8712 | 0.0787 | 0.8948 | 0.8622 | 0.8782 | 0.9852 |
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