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BioLinkBERT-LitCovid-v1.1
sofia-todeschini/BioLinkBERT-LitCovid-v1.1
BioLinkBERT-LitCovid-v1.1 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 apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- bert
- Access
- Public
- Created
- Jun 15, 2023
- Updated
- Jun 15, 2023
- SHA
- 34060af6
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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. -->
BioLinkBERT-LitCovid-v1.1
This model is a fine-tuned version of michiyasunaga/BioLinkBERT-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1070
- F1: 0.9009
- Roc Auc: 0.9439
- Accuracy: 0.7915
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|
| 0.119 | 1.0 | 1560 | 0.1121 | 0.8949 | 0.9366 | 0.7857 |
| 0.0994 | 2.0 | 3120 | 0.1050 | 0.8999 | 0.9335 | 0.7934 |
| 0.0745 | 3.0 | 4680 | 0.1070 | 0.9009 | 0.9439 | 0.7915 |
| 0.0584 | 4.0 | 6240 | 0.1132 | 0.8986 | 0.9367 | 0.7900 |
| 0.0445 | 5.0 | 7800 | 0.1183 | 0.8993 | 0.9385 | 0.7886 |
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- apache-2.0
- Created
- Jun 15, 2023
- Updated
- Jun 15, 2023