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PubMedBERT-base-abstract-LitCovid-v1.0
sofia-todeschini/PubMedBERT-base-abstract-LitCovid-v1.0
PubMedBERT-base-abstract-LitCovid-v1.0 is a text classification model from sofia-todeschini. 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
- Jun 6, 2023
- Updated
- Jun 6, 2023
- SHA
- de2548ba
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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. -->
PubMedBERT-base-abstract-LitCovid-v1.0
This model is a fine-tuned version of microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1046
- F1: 0.9006
- Roc Auc: 0.9352
- Accuracy: 0.7996
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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.1105 | 1.0 | 3120 | 0.1046 | 0.9006 | 0.9352 | 0.7996 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Jun 6, 2023
- Updated
- Jun 6, 2023