metadata
language:
- en
license: apache-2.0
library_name: transformers
tags:
- generated_from_trainer
- medical
datasets:
- opentargets/clinical_trial_reason_to_stop
metrics:
- accuracy
widget:
- text: Study stopped due to problems to recruit patients
example_title: Enrollment issues
- text: Efficacy endpoint unmet
example_title: Negative reasons
- text: Study stopped due to unexpected adverse effects
example_title: Safety
- text: Study paused due to the pandemic
example_title: COVID-19
base_model: bert-base-uncased
model-index:
- name: stop_reasons_classificator_multilabel
results: []
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