Xlm Roberta Large Pooled Cap V3

🚀 xlm-roberta-large-pooled-cap-v3

This is an xlm-roberta-large benchmark model fine-tuned on training data with texts labeled by major topic codes from the Comparative Agendas Project, aiming to provide reliable text classification capabilities.

🚀 Quick Start

Model Description

An xlm-roberta-large benchmark model finetuned on training data containing texts labelled with major topic codes from the Comparative Agendas Project.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
    model="poltextlab/xlm-roberta-large-pooled-cap-v3",
    task="text-classification",
    tokenizer=tokenizer,
    use_fast=False,
    token="<your_hf_read_only_token>"
)

text = "We will place an immediate 6-month halt on the finance driven closure of beds and wards, and set up an independent audit of needs and facilities."
pipe(text)

Gated Access

⚠️ Important Note

Due to the gated access, you must pass the token parameter when loading the model. In earlier versions of the Transformers package, you may need to use the use_auth_token parameter instead.

📄 License

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

Required Information for Academic Use