Clinical trial stop reasons
This model is a fine-tuned version of bert-base-uncased on the task of classification of why a clinical trial has stopped early.
The dataset containing 3,747 manually curated reasons used for fine-tuning is available in the Hub.
More details on the model training are available in the GitHub project (link) and in the associated publication (DOI: 10.1038/s41588-024-01854-z).
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy Thresh |
|---|---|---|---|---|
| No log | 1.0 | 106 | 0.1824 | 0.9475 |
| No log | 2.0 | 212 | 0.1339 | 0.9630 |
| No log | 3.0 | 318 | 0.1109 | 0.9689 |
| No log | 4.0 | 424 | 0.0988 | 0.9741 |
| 0.1439 | 5.0 | 530 | 0.0943 | 0.9743 |
| 0.1439 | 6.0 | 636 | 0.0891 | 0.9763 |
| 0.1439 | 7.0 | 742 | 0.0899 | 0.9760 |
Framework versions
- Transformers 4.26.0
- Pytorch 1.12.1+cu102
- Datasets 2.9.0
- Tokenizers 0.13.2
- Downloads last month
- 37
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for opentargets/clinical_trial_stop_reasons
Base model
google-bert/bert-base-uncased