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Bio_ClinicalBERT_fold_1_ternary_v1
elopezlopez/Bio_ClinicalBERT_fold_1_ternary_v1
Bio_ClinicalBERT_fold_1_ternary_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 4, 2022
- Updated
- Aug 9, 2022
- SHA
- 99b03a6a
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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_1_ternary_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: 1.9306
- F1: 0.7943
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 | 290 | 0.5314 | 0.7931 |
| 0.556 | 2.0 | 580 | 0.5572 | 0.8042 |
| 0.556 | 3.0 | 870 | 0.6381 | 0.7893 |
| 0.2427 | 4.0 | 1160 | 0.9330 | 0.7807 |
| 0.2427 | 5.0 | 1450 | 1.1033 | 0.7918 |
| 0.0987 | 6.0 | 1740 | 1.2582 | 0.7856 |
| 0.0381 | 7.0 | 2030 | 1.3816 | 0.7807 |
| 0.0381 | 8.0 | 2320 | 1.5874 | 0.7807 |
| 0.0199 | 9.0 | 2610 | 1.5712 | 0.7856 |
| 0.0199 | 10.0 | 2900 | 1.5860 | 0.7856 |
| 0.0139 | 11.0 | 3190 | 1.7511 | 0.7757 |
| 0.0139 | 12.0 | 3480 | 1.7635 | 0.7807 |
| 0.0056 | 13.0 | 3770 | 1.7726 | 0.7893 |
| 0.0077 | 14.0 | 4060 | 1.7565 | 0.7881 |
| 0.0077 | 15.0 | 4350 | 1.8406 | 0.7844 |
| 0.005 | 16.0 | 4640 | 1.8595 | 0.7893 |
| 0.005 | 17.0 | 4930 | 1.8831 | 0.7770 |
| 0.0036 | 18.0 | 5220 | 1.9561 | 0.7881 |
| 0.0017 | 19.0 | 5510 | 2.0062 | 0.7856 |
| 0.0017 | 20.0 | 5800 | 1.9275 | 0.7980 |
| 0.0004 | 21.0 | 6090 | 1.8752 | 0.7993 |
| 0.0004 | 22.0 | 6380 | 1.9238 | 0.7918 |
| 0.0022 | 23.0 | 6670 | 1.9552 | 0.7881 |
| 0.0022 | 24.0 | 6960 | 1.9274 | 0.7955 |
| 0.0009 | 25.0 | 7250 | 1.9306 | 0.7943 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Aug 4, 2022
- Updated
- Aug 9, 2022