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DistilBERT-smaller-BioRED
zonghaoyang/DistilBERT-smaller-BioRED
DistilBERT-smaller-BioRED is a text classification model from zonghaoyang. 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
- distilbert
- Access
- Public
- Created
- May 17, 2023
- Updated
- May 17, 2023
- SHA
- 85a3ee42
Try a prompt
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. -->
DistilBERT-smaller-BioRED
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3354
- Accuracy: 0.8900
- F1: 0.5850
- Precision: 0.6149
- Recall: 0.5579
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: 5e-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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.3401 | 1.0 | 813 | 0.2766 | 0.8777 | 0.5822 | 0.5487 | 0.6199 |
| 0.2345 | 2.0 | 1626 | 0.3227 | 0.8977 | 0.5358 | 0.7113 | 0.4298 |
| 0.1928 | 3.0 | 2439 | 0.3499 | 0.8946 | 0.5912 | 0.6329 | 0.5546 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- distilbert
- License
- apache-2.0
- Created
- May 17, 2023
- Updated
- May 17, 2023