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Bio_ClinicalBERT_fold_3_ternary_v1
elopezlopez/Bio_ClinicalBERT_fold_3_ternary_v1
Bio_ClinicalBERT_fold_3_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 9, 2022
- Updated
- Aug 9, 2022
- SHA
- e142ff4a
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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_3_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: 2.0585
- F1: 0.7952
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 | 289 | 0.5738 | 0.7709 |
| 0.5512 | 2.0 | 578 | 0.5828 | 0.7733 |
| 0.5512 | 3.0 | 867 | 0.7217 | 0.7830 |
| 0.2304 | 4.0 | 1156 | 1.0389 | 0.7867 |
| 0.2304 | 5.0 | 1445 | 1.0992 | 0.7915 |
| 0.0951 | 6.0 | 1734 | 1.3528 | 0.7806 |
| 0.0388 | 7.0 | 2023 | 1.4223 | 0.7879 |
| 0.0388 | 8.0 | 2312 | 1.5588 | 0.7830 |
| 0.0172 | 9.0 | 2601 | 1.5913 | 0.7976 |
| 0.0172 | 10.0 | 2890 | 1.7464 | 0.7842 |
| 0.0143 | 11.0 | 3179 | 1.7395 | 0.7927 |
| 0.0143 | 12.0 | 3468 | 1.7523 | 0.7939 |
| 0.0108 | 13.0 | 3757 | 1.8059 | 0.7952 |
| 0.0099 | 14.0 | 4046 | 1.9056 | 0.7855 |
| 0.0099 | 15.0 | 4335 | 1.8550 | 0.7903 |
| 0.0076 | 16.0 | 4624 | 1.8718 | 0.7988 |
| 0.0076 | 17.0 | 4913 | 1.9325 | 0.7976 |
| 0.0033 | 18.0 | 5202 | 1.9504 | 0.7952 |
| 0.0033 | 19.0 | 5491 | 1.9841 | 0.7879 |
| 0.003 | 20.0 | 5780 | 1.9843 | 0.7952 |
| 0.0001 | 21.0 | 6069 | 2.0110 | 0.7927 |
| 0.0001 | 22.0 | 6358 | 2.0049 | 0.7939 |
| 0.0028 | 23.0 | 6647 | 2.0638 | 0.7915 |
| 0.0028 | 24.0 | 6936 | 2.0612 | 0.7903 |
| 0.0011 | 25.0 | 7225 | 2.0585 | 0.7952 |
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 9, 2022
- Updated
- Aug 9, 2022