Hugging Face
Models
Datasets
Spaces
Buckets
new
Docs
Enterprise
Pricing
Website
Tasks
HuggingChat
Collections
Languages
Organizations
Community
Blog
Posts
Daily Papers
Learn
Discord
Forum
GitHub
Solutions
Team & Enterprise
Hugging Face PRO
Enterprise Support
Inference Providers
Inference Endpoints
Storage Buckets
Log In
Sign Up
13
Ronty
Kurapika993
Follow
regisss's profile picture
adamm-hf's profile picture
sudanenator's profile picture
3 followers
·
5 following
AI & ML interests
None yet
Recent Activity
reacted
to
their
post
with 🤗
3 days ago
Built a small Streamlit + CLI demo for generating context-dependent toxicity datasets using OpenAI models. GitHub: https://github.com/Mayukhga83/Toximatics-Contextual-Toxicity-Data-Generator Demo: https://toximatics-contextual-toxicity-data-generator-fnn9mzm7bkuzmta4.streamlit.app/ The core idea is that the same utterance can become toxic or benign depending on the surrounding social situation. With is generation framework you can create such datasets at scale. The pipeline supports: direct context augmentation given the seed utterance new utterance-context pair generation given seed utterances multistage generation for diverse examples validation with a critic model CSV / JSONL export Example: Utterance: “You are so lucky to work from home.” Benign context: A friend congratulates someone on improved work-life balance. Toxic context: A colleague dismisses someone struggling with childcare and burnout. The project is connected to recent work on contextual toxicity understanding https://aclanthology.org/2024.sigdial-1.65/.
posted
an
update
3 days ago
Built a small Streamlit + CLI demo for generating context-dependent toxicity datasets using OpenAI models. GitHub: https://github.com/Mayukhga83/Toximatics-Contextual-Toxicity-Data-Generator Demo: https://toximatics-contextual-toxicity-data-generator-fnn9mzm7bkuzmta4.streamlit.app/ The core idea is that the same utterance can become toxic or benign depending on the surrounding social situation. With is generation framework you can create such datasets at scale. The pipeline supports: direct context augmentation given the seed utterance new utterance-context pair generation given seed utterances multistage generation for diverse examples validation with a critic model CSV / JSONL export Example: Utterance: “You are so lucky to work from home.” Benign context: A friend congratulates someone on improved work-life balance. Toxic context: A colleague dismisses someone struggling with childcare and burnout. The project is connected to recent work on contextual toxicity understanding https://aclanthology.org/2024.sigdial-1.65/.
updated
a model
over 1 year ago
Kurapika993/sentiment
View all activity
Organizations
Kurapika993
's models
2
Sort: Recently updated
Kurapika993/sentiment
Text Classification
•
67M
•
Updated
Nov 3, 2024
•
2
Kurapika993/Toxic_classifier_bert
Text Classification
•
Updated
May 28, 2023
•
5