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---
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
<!-- Provide a longer summary of what this model is. -->
- **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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### 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]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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### Results
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#### Summary
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