brunneis/ilab-erisk-2020
Repository accompanying the CLEF 2020 eRisk Workshop Working Notes for the iLab team (University Of Strathclyde)
This project aims to help researchers and practitioners identify early signs of risk, particularly in mental health contexts. By analyzing textual data, it can help detect subtle indicators of distress or emerging risk factors. The typical user would be a researcher in digital health, psychology, or a related field looking to develop or evaluate risk detection models.
No commits in the last 6 months.
Use this if you are a researcher focused on early risk detection in textual data, especially within mental health domains, and need a foundation for building and testing predictive models.
Not ideal if you are looking for an out-of-the-box solution for immediate real-world deployment or if your primary interest is in domains other than early risk detection from text.
Stars
7
Forks
1
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Sep 17, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/brunneis/ilab-erisk-2020"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
prrao87/tweet-stance-prediction
Applying NLP transfer learning techniques to predict Tweet stance toward a topic
JRC1995/BERT-Disaster-Classification-Capsule-Routing
Exploration of BERT-BiLSTM models with Layer Aggregation (attention-based and...
blazejdolicki/bert-sarcasm-detection
Sarcasm detection with BERT
SuryaVamsi-P/Conflict-NLP-Topic-Modeling-Sentiment-Analysis-using-LLMs
Extracts insights from 26K+ protest events using BERTopic, Top2Vec, and LLMs for real-world...
tk-yasuno/disaster-question-answer
Japanese disaster-focused question answering system : Utilizing the bert-base-japanese-v3 +...