Model: poltextlab/xlm-roberta-large-norwegian-party-cap-v3
Author: poltextlab
Pipeline: text-classification
Tags: pytorch, xlm-roberta, zero-shot-classification, text-classification, no, base_model:FacebookAI/xlm-roberta-large, base_model:finetune:FacebookAI/xlm-roberta-large, license:cc-by-4.0, region:us
Base models (finetune): id: FacebookAI/xlm-roberta-large
Card metadata: license: cc-by-4.0
language: no
tags: zero-shot-classification, text-classification, pytorch
metrics: accuracy, f1-score
base_model: FacebookAI/xlm-roberta-large
extra_gated_prompt: Our models are intended for academic use only. If you are not affiliated with an academic institution, please provide a rationale for using our models. Please allow us a few business days to manually review subscriptions.
If you use our models for your work or research, please cite this paper: Sebők, M., Máté, Á., Ring, O., Kovács, V., & Lehoczki, R. (2024). Leveraging Open Large Language Models for Multilingual Policy Topic Classification: The Babel Machine Approach. Social Science Computer Review, 0(0). https://doi.org/10.1177/08944393241259434
extra_gated_fields: Name: text
Country: country
Institution: text
Institution Email: text
Please specify your academic use case: text
Likes: 0 | All-time downloads: 9641
Created: 2024-10-14 13:31:07