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phibert-finetuned-ner
girinlp-i2i/phibert-finetuned-ner
phibert-finetuned-ner is a token classification model from girinlp-i2i. Use it when you need labels on individual words, such as names. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Jan 11, 2023
- Updated
- Feb 8, 2023
- SHA
- 05c27b34
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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. -->
phibert-finetuned-ner
This model is a fine-tuned version of dmis-lab/biobert-v1.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0293
- Precision: 0.9238
- Recall: 0.9213
- F1: 0.9226
- Accuracy: 0.9950
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 |
|---|---|---|---|---|---|---|---|
| 0.0309 | 1.0 | 5728 | 0.0305 | 0.8977 | 0.9042 | 0.9009 | 0.9939 |
| 0.0131 | 2.0 | 11456 | 0.0308 | 0.9089 | 0.9114 | 0.9102 | 0.9939 |
| 0.008 | 3.0 | 17184 | 0.0293 | 0.9238 | 0.9213 | 0.9226 | 0.9950 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- Created
- Jan 11, 2023
- Updated
- Feb 8, 2023