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Bio_ClinicalBERT_fold_10_binary_v1
elopezlopez/Bio_ClinicalBERT_fold_10_binary_v1
Bio_ClinicalBERT_fold_10_binary_v1 is a text classification model from elopezlopez. 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
- Aug 3, 2022
- Updated
- Aug 4, 2022
- SHA
- c22d5f19
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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. -->
Bio_ClinicalBERT_fold_10_binary_v1
This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5504
- F1: 0.8243
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: 25
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| No log | 1.0 | 288 | 0.3803 | 0.8103 |
| 0.4005 | 2.0 | 576 | 0.4769 | 0.8070 |
| 0.4005 | 3.0 | 864 | 0.5258 | 0.7955 |
| 0.1889 | 4.0 | 1152 | 0.7423 | 0.8153 |
| 0.1889 | 5.0 | 1440 | 1.1246 | 0.8012 |
| 0.0703 | 6.0 | 1728 | 1.1325 | 0.8039 |
| 0.0246 | 7.0 | 2016 | 1.2192 | 0.8196 |
| 0.0246 | 8.0 | 2304 | 1.3645 | 0.8050 |
| 0.0192 | 9.0 | 2592 | 1.4029 | 0.8087 |
| 0.0192 | 10.0 | 2880 | 1.3714 | 0.8117 |
| 0.0107 | 11.0 | 3168 | 1.4673 | 0.8092 |
| 0.0107 | 12.0 | 3456 | 1.3941 | 0.8199 |
| 0.0084 | 13.0 | 3744 | 1.4350 | 0.8126 |
| 0.0083 | 14.0 | 4032 | 1.4428 | 0.8162 |
| 0.0083 | 15.0 | 4320 | 1.2892 | 0.8263 |
| 0.0119 | 16.0 | 4608 | 1.4238 | 0.8222 |
| 0.0119 | 17.0 | 4896 | 1.4961 | 0.8174 |
| 0.0046 | 18.0 | 5184 | 1.5010 | 0.8107 |
| 0.0046 | 19.0 | 5472 | 1.4876 | 0.8215 |
| 0.0036 | 20.0 | 5760 | 1.5080 | 0.8180 |
| 0.0031 | 21.0 | 6048 | 1.5317 | 0.8261 |
| 0.0031 | 22.0 | 6336 | 1.5103 | 0.8215 |
| 0.0005 | 23.0 | 6624 | 1.5255 | 0.8197 |
| 0.0005 | 24.0 | 6912 | 1.5578 | 0.8257 |
| 0.0001 | 25.0 | 7200 | 1.5504 | 0.8243 |
Framework versions
- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Aug 3, 2022
- Updated
- Aug 4, 2022