Model: poltextlab/xlm-roberta-large-pooled-cap-v3
Author: poltextlab
Pipeline: text-classification
Library: transformers
Tags: transformers, pytorch, xlm-roberta, text-classification, es, pt, pl, it, hu, de, fr, en, nl, da, license:cc-by-4.0, model-index, endpoints_compatible, region:us
Card metadata: model-index: name: poltextlab/xlm-roberta-large-pooled-cap-v3
results: task: type: text-classification
metrics: name: Accuracy
type: accuracy
value: 82%
verified: no, name: F1-Score
type: f1
value: 82%
verified: no
language: es, pt, pl, it, hu, de, fr, en, nl, da
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
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Country: country
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license: cc-by-4.0
Likes: 2 | All-time downloads: 42228
Created: 2024-02-02 10:12:59