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pubmedqa-bert
nikhilteja30/pubmedqa-bert
pubmedqa-bert is a text classification model from nikhilteja30. 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 7, 2026
- Updated
- Apr 7, 2026
- SHA
- 23c84b21
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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. -->
pubmedqa-bert
This model is a fine-tuned version of microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1777
- Accuracy: 0.57
- F1 Macro: 0.5147
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: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 34
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|---|---|---|---|---|---|
| 0.7552 | 1.0 | 57 | 1.0784 | 0.58 | 0.4976 |
| 0.4710 | 2.0 | 114 | 1.2157 | 0.51 | 0.4021 |
| 0.4729 | 3.0 | 171 | 1.1777 | 0.57 | 0.5147 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Apr 7, 2026
- Updated
- Apr 7, 2026