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Librarian Bot: Add base_model information to model (#2)
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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