Xlm Roberta Large Pooled Cap Minor

🚀 xlm-roberta-large-pooled-cap-minor

An xlm-roberta-large model fine - tuned for multilingual text classification, leveraging minor topic codes from the Comparative Agendas Project.

🚀 Quick Start

Basic Usage

from transformers import AutoTokenizer, pipeline

tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")
pipe = pipeline(
    model="poltextlab/xlm-roberta-large-pooled-cap-minor",
    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.

✨ Features

• Multilingual Support: Finetuned on multilingual (English, Danish) training data.
• Zero - Shot Classification: Suitable for zero - shot classification tasks.
• Text Classification: Specifically designed for text classification with high accuracy.

📦 Installation

This architecture uses the sentencepiece tokenizer. In order to run the model before transformers==4.27, you need to install it manually.

📚 Documentation

Model Description

An xlm - roberta - large model finetuned on multilingual (English, Danish) training data labelled with [minor topic codes](https://www.comparativeagendas.net/pages/master - codebook) from the Comparative Agendas Project.

Model Performance

The model was evaluated on a test set of 15 349 English examples (20% of the English data).

• Accuracy: 0.65
• Weighted Average F1 - score: 0.64

Inference Platform

This model is used by the CAP Babel Machine, an open - source and free natural language processing tool, designed to simplify and speed up projects for comparative research.

Cooperation

Model performance can be significantly improved by extending our training sets. We appreciate every submission of CAP - coded corpora (of any domain and language) at poltextlab{at}poltextlab{dot}com or by using the CAP Babel Machine.

Debugging and Issues

If you encounter a RuntimeError when loading the model using the from_pretrained() method, adding ignore_mismatched_sizes = True should solve the issue.

📄 License

This project is licensed under the MIT License.