Blablablab/multilingual-style-representation

Multilingual Style Representation

This is the Style Representation model, presented in `Leveraging Multilingual Training for Authorship Representation: Enhancing Generalization across Languages and Domains`.

The Style Representation model encodes documents written by the same author as nearby vectors in the embedding space. The model can be used for authorship attribution, style similarity, machine-generated text detection, and more.

For training and evaluation code, refer to our repository here.

For the Style Representation model based on Llama-3.2, refer to Blablablab/multilingual-style-representation-Llama-3.2.

Model Details

• —Model Type: Sentence Transformer
• —Base model: FacebookAI/xlm-roberta-large
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 1024 dimensions
• —Similarity Function: Cosine Similarity

Usage

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Blablablab/multilingual-style-representation")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

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