Model: poltextlab/xlm-roberta-large-norwegian-cap-v3
Author: poltextlab
Pipeline: text-classification
Library: transformers
Tags: transformers, pytorch, xlm-roberta, text-classification, no, base_model:FacebookAI/xlm-roberta-large, base_model:finetune:FacebookAI/xlm-roberta-large, license:cc-by-4.0, model-index, endpoints_compatible, region:us
Base models (finetune): id: FacebookAI/xlm-roberta-large
Card metadata: model-index: name: poltextlab/xlm-roberta-large-norwegian-cap-v3
results: task: type: text-classification
metrics: name: Accuracy
type: accuracy
value: 89%
verified: no, name: F1-Score
type: f1
value: 89%
verified: no
tags: text-classification, transformers
metrics: accuracy, f1_score
language: no
base_model: xlm-roberta-large
pipeline_tag: text-classification
library_name: transformers
license: cc-by-4.0
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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, 43(2), 295-317. https://doi.org/10.1177/08944393241259434
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Country: country
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Created: 2024-03-11 20:15:36