---
license: mit
datasets:
- tweet_eval
- bookcorpus
- wikipedia
- cc_news
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- medical
---
# 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](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.

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

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- **Developed by:** [More Information Needed]
- **Funded by [optional]:** Queen's University
- **Model type:** RoBERTa
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** [elonzano/tweet_emotion_eval](https://huggingface.co/elozano/tweet_emotion_eval)

## Uses

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### Direct Use

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[More Information Needed]

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary