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longformer-ner-finetuned-pii
hewonty/longformer-ner-finetuned-pii
longformer-ner-finetuned-pii is a token classification model from hewonty. 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
- longformer
- Access
- Public
- Created
- Feb 15, 2024
- Updated
- Feb 16, 2024
- SHA
- 882dd9b1
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Base models
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. -->
longformer-ner-finetuned-pii
This model is a fine-tuned version of allenai/longformer-base-4096 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0034
- Precision: 0.9772
- Recall: 0.9879
- F1: 0.9825
- Accuracy: 0.9993
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 |
|---|---|---|---|---|---|---|---|
| 0.0063 | 1.0 | 1324 | 0.0050 | 0.9613 | 0.9842 | 0.9726 | 0.9990 |
| 0.0037 | 2.0 | 2648 | 0.0038 | 0.9735 | 0.9873 | 0.9803 | 0.9992 |
| 0.002 | 3.0 | 3972 | 0.0034 | 0.9772 | 0.9879 | 0.9825 | 0.9993 |
Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
- Task
- Token Classification
- Library
- transformers
- Type
- longformer
- License
- apache-2.0
- Created
- Feb 15, 2024
- Updated
- Feb 16, 2024