Update README.md

README.md

@@ -18,34 +18,14 @@ library_name: transformers
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-# stop_reasons_classificator_multilabel
+# clinical_trial_stop_reasons
 
-This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the task of classification of why a clinical trial has stopped early in 17 different classes. The datased used for fine tuning was manually curated by a group of experts in Open Targets and is also available for download at the [Hub](https://huggingface.co/datasets/opentargets/clinical_trial_reason_to_stop).
+This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/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 at [Hub](https://huggingface.co/datasets/opentargets/clinical_trial_reason_to_stop).
 
-It achieves the following results on the evaluation set:
-- Loss: 0.0899
-- Accuracy Thresh: 0.9760
+More details on the model training are available in the github project ([link](https://github.com/opentargets/stopReasons)) and in the associated publication (TBC).
 
-## Model description
-
-This research has been done by Olesya Razuvayevskaya (@LesyaR).
-
-We fine-tuned BERT model for the task of predicting the stop reasons on the training set of 3,571
-human-annotated stopped clinical trials (Devlin et al., 2018). We used a BERT uncased pre-trained
-model with a one-layer feed-forward classifier. The fine-tuning was performed by using the
-Hugging Face transformer library (Wolf et al., 2019). The classifier uses 50 hidden units and the
-ReLu activation function.
-
-## Intended uses & limitations
-
-This model is intended to be used by the whole scientific community. It is Apache 2.0 licensed.
-
-## Training and evaluation data
-
-An expert-curated data set of >5000 reasons why a clinical trials have stopped. These data have been extracted from clinicaltrials.gov.
-
-A set of experts from the Open Targets Consortium assigned these free text labels to a set of 17 different classes after receiving training.
 
 ## Training procedure