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--- |
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license: mit |
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datasets: |
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- tweet_eval |
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- bookcorpus |
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- wikipedia |
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- cc_news |
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language: |
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- en |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- medical |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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Pretrained model on English language for text classification. Model trained from [tweet_emotion_eval](https://huggingface.co/elozano/tweet_emotion_eval) ([roberta-base](https://huggingface.co/roberta-base) fine-tuned on emotion task of [tweet_eval](https://huggingface.co/datasets/tweet_eval) dataset) on psychotherapy text transcripts. |
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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. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** Margot Wagner, Jasleen Jagayat, Anchan Kumar, Amir Shirazi, Nazanin Alavi, Mohsen Omrani |
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- **Funded by:** Queen's University |
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- **Model type:** RoBERTa |
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- **Language(s) (NLP):** English |
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- **License:** MIT |
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- **Finetuned from model:** [elonzano/tweet_emotion_eval](https://huggingface.co/elozano/tweet_emotion_eval) |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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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. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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from transformers import pipeline |
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classifier = pipeline(task="text-classification", model="margotwagner/roberta-psychotherapy-eval") |
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sentences = ["I am not having a great day"] |
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model_outputs = classifier(sentences) |
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print(model_outputs[0]) |
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# produces a list of dicts for each of the labels |
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``` |
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## Training Details |
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### Training Data |
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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. |
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### Training Procedure |
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Weighted cross-entropy loss function was employed to address class imbalance. Model accuracy in the form of F1 was used for model selection. |
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### Metrics |
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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. |
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