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pubmedbert-finetuned-ner
jialinselenasong/pubmedbert-finetuned-ner
pubmedbert-finetuned-ner is a token classification model from jialinselenasong. 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
- May 8, 2024
- Updated
- May 8, 2024
- SHA
- c4ea8096
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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. -->
pubmedbert-finetuned-ner
This model is a fine-tuned version of neuml/pubmedbert-base-embeddings on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4100
- Precision: 0.6458
- Recall: 0.6891
- F1: 0.6668
- Accuracy: 0.8570
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 | 305 | 0.4216 | 0.6399 | 0.6691 | 0.6542 | 0.8532 |
| 0.4885 | 2.0 | 610 | 0.4065 | 0.6374 | 0.6901 | 0.6627 | 0.8546 |
| 0.4885 | 3.0 | 915 | 0.4100 | 0.6458 | 0.6891 | 0.6668 | 0.8570 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- May 8, 2024
- Updated
- May 8, 2024