Update README.md

README.md

@@ -37,78 +37,39 @@ Given a sentence, this model provides a binary classification as either symptoma
 ## 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. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
+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.
+```python
+from transformers import pipeline
+
+classifier = pipeline(task="text-classification", model="margotwagner/roberta-psychotherapy-eval")
 
-[More Information Needed]
+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 should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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-[More Information Needed]
+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 
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
+Weighted cross-entropy loss function was employed to address class imbalance. Model accuracy in the form of F1 was used for model selection.
 
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
+### Testing Data & Metrics
 
 #### Testing Data
 
-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
+The testing data used was clinical data from a board-reviewed and ethically-compliant online psychotherapy clinical trial conducted at Queen’s University between 2020 and 2021. The study underwent a thorough review process by the Queen’s University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board to ensure adherence to ethical standards (File #: 6020045).
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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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.