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NHS-BiomedNLP-BiomedBERT-hypop
NIHNCATS/NHS-BiomedNLP-BiomedBERT-hypop
NHS-BiomedNLP-BiomedBERT-hypop is a text classification model from NIHNCATS. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Apr 25, 2024
- Updated
- Apr 26, 2024
- SHA
- 5c46f0f8
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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. -->
NHS-BiomedNLP-BiomedBERT-hypop
This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4277
- Accuracy: 0.8293
- Precision: 0.8301
- Recall: 0.8375
- F1: 0.8285
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: 3e-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: 6
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.0264 | 1.0 | 397 | 0.4689 | 0.7950 | 0.7974 | 0.8017 | 0.7946 |
| 0.5258 | 2.0 | 794 | 0.5543 | 0.7779 | 0.7745 | 0.7743 | 0.7744 |
| 3.0689 | 3.0 | 1191 | 0.6701 | 0.8050 | 0.8068 | 0.7957 | 0.7990 |
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Apr 25, 2024
- Updated
- Apr 26, 2024