If you’re looking to understand and categorize political texts across multiple languages, you’re in the right place! This guide will walk you through using the XLM-RoBERTa model fine-tuned on the rich Manifesto Corpus. Without further ado, let’s dive in!

What is the XLM-RoBERTa Model?

The XLM-RoBERTa model is a robust machine learning model that has been trained on approximately 1.6 million annotated statements from the Manifesto Corpus. This model excels at categorizing texts into 56 distinct political topics based on materials developed by the Manifesto Project. It works best with 38 languages contained in the corpus, ensuring versatile performance whether you are processing statements in English, French, Spanish, or any of the other supported languages.

Getting Started

Before using the model, make sure you have the necessary libraries installed. You will need the `torch` and `transformers` libraries. If you haven’t installed them yet, you can do so via pip:

pip install torch transformers

Using the Model

Here’s how you can implement the XLM-RoBERTa model:

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load the model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("manifesto-project/manifestoberta-xlm-roberta-56policy-topics-context-2023-1-1", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large")

# Prepare your sentence and context
sentence = "These principles are under threat."
context = "Human rights and international humanitarian law are fundamental pillars of a secure global system. These principles are under threat. Some of the world's most powerful states choose to sell arms to human-rights abusing states."

# Tokenize the inputs
inputs = tokenizer(sentence, context, return_tensors='pt', max_length=300, padding='max_length', truncation=True)

# Get model predictions
logits = model(**inputs).logits
probabilities = torch.softmax(logits, dim=1).tolist()[0]

# Format probabilities into a dictionary
probabilities = {model.config.id2label[index]: round(probability * 100, 2) for index, probability in enumerate(probabilities)}
probabilities = dict(sorted(probabilities.items(), key=lambda item: item[1], reverse=True))
print(probabilities)

# Get predicted class
predicted_class = model.config.id2label[logits.argmax().item()]
print(predicted_class)

Understanding the Code: An Analogy

Imagine you are a librarian in a grand library that houses books on various subjects. You have an organizational tool—the XLM-RoBERTa model—that helps you catalog these books based on their content.

• Loading the Model: Think of loading the model as unlocking the library doors so you can access the database of information.
• Context and Sentence: Just like a librarian needs the title and author (sentence) and the back cover summary (context) to categorize a book, the model requires both pieces of information.
• Tokenization: Tokenizing is like breaking down the text into manageable bits—similar to cataloging the books into sections according to their subjects.
• Model Predictions: Finally, when you query the database for a specific subject, the model’s predictions give you the categories under which the text can be classified, allowing you to see how the content fits into the broader library.

Troubleshooting Common Issues

• Performance Issues: If you find the model is not performing well, ensure that the sentence and context are properly formatted and relevant.
• Import Errors: Check if all required libraries are installed correctly. If you experience errors with import statements, make sure you run `pip install` any necessary packages.
• Memory Errors: If the model runs out of memory, consider reducing the batch size or maximizing the length configuration during tokenization.

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Wrapping Up

The power of the XLM-RoBERTa model is at your fingertips, ready to help you categorize and understand political texts in multiple languages. With the steps outlined above, you can successfully implement this model in your own projects.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.