roberta-psychotherapy-eval

Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

Pretrained model on English language for text classification. Model trained from tweet_emotion_eval (roberta-base fine-tuned on emotion task of tweet_eval dataset) on psychotherapy text transcripts.

Given a sentence, this model provides a binary classification as either symptomatic or non-symptomatic where symptomatic means the sentence displays signs of anxiety and/or depression.

Model Details

Model Description

<!-- Provide a longer summary of what this model is. -->

• Developed by: Margot Wagner, Jasleen Jagayat, Anchan Kumar, Amir Shirazi, Nazanin Alavi, Mohsen Omrani
• Funded by: Queen's University
• Model type: RoBERTa
• Language(s) (NLP): English
• License: MIT
• Finetuned from model: elonzano/tweet_emotion_eval

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

This model is intended to be used to assess the mental health status using sentence-level text data. Specifically, it looks for symptoms related to anxiety and depression.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import pipeline

classifier = pipeline(task="text-classification", model="margotwagner/roberta-psychotherapy-eval")

sentences = ["I am not having a great day"]

model_outputs = classifier(sentences)
print(model_outputs[0])
# produces a list of dicts for each of the labels

Training Details

Training Data

This model was fine-tuned using English sentence-level data in a supervised manner where symptomatic labels were obtained from expert clinicians. Sentences were required to be independent in nature. Back-translation was utilized to increase the size of the training dataset.

Training Procedure

Weighted cross-entropy loss function was employed to address class imbalance. Model accuracy in the form of F1 was used for model selection.

Metrics

F1 score was used as the model accuracy metric, as it maintains a balance between precision and recall with particular importance given to positive examples.