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 (TBC).
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