license: cc-by-nc-nd-4.0
metrics:
- f1
- accuracy
base_model:
- FacebookAI/xlm-roberta-base
language:
- en
- hi
- mr
- bn
- ta
- te
- ml
- ur
pipeline_tag: text-classification
tags:
- sexism
- hate
- indic
- empowerment
- gender
Model Card for Model ID
Classifies polarised gendered discourse for all indic languages.
0=Neutral 1=Sexist and misogynistic 2=Empowering
Model Details
genAMI, paper forthcoming
Author Details
Praachi Kumar
Research Fellow
United Nations University - MERIT
Model Description
- Developed by: Praachi Kumar
- Model type: Fine-tuned XLM-RoBERTa base for sequence classification
- Language(s) (NLP): Multi, focus on Indic
- License: Non commercial, no derrivatives
- Paper: Forthcoming
Uses
Social science research, intended for academic and nonacademic use
Bias, Risks, and Limitations
Social science approaches to annotation, single annotator coded
Recommendations
Please contact me at [email protected] for instructions on further use
How to Get Started with the Model
Forthcoming
Training Details
Training Data
English language Tweets
Metrics
English Tweets:
Macro Average F1 Score: 0.83
Balanced Accuracy: 0.88
Multilingual Tweets:
Macro Average F1 Score: 0.76
Balanced Accuracy: 0.76
Results
Forthcoming
Citation
Model
BibTeX:
@misc{genami2025, author = {Praachi Kumar}, title = {genAMI}, year = {2025}, month = {March}, day = {13}, howpublished = {\url{https://doi.org/10.57967/hf/5784}} }
APA: Kumar, P. (2025). genAMI. Hugging Face. https://doi.org/10.57967/hf/5784
Paper: Forthcoming