Monoelectra Base

🚀 Cross-Encoder for Text Ranking

This model is designed for text ranking, porting the model from lightning - ir to Sentence Transformers and Transformers, and can be used as a reranker in a retrieve - rerank pipeline.

🚀 Quick Start

This model is a port of the webis/monoelectra-base model from lightning-ir to Sentence Transformers and Transformers.

The original model was introduced in the paper A Systematic Investigation of Distilling Large Language Models into Cross-Encoders for Passage Re-ranking. See https://github.com/webis-de/rank-distillm for code used to train the original model.

The model can be used as a reranker in a 2 - stage "retrieve - rerank" pipeline, where it reorders passages returned by a retriever model (e.g. an embedding model or BM25) given some query. See SBERT.net Retrieve & Re-rank for more details.

✨ Features

• Ported from lightning-ir to Sentence Transformers and Transformers.
• Can be used as a reranker in a 2 - stage "retrieve - rerank" pipeline.

📦 Installation

The usage is easy when you have SentenceTransformers installed.

pip install sentence-transformers

💻 Usage Examples

Basic Usage with Sentence Transformers

from sentence_transformers import CrossEncoder

model = CrossEncoder("cross-encoder/monoelectra-base", trust_remote_code=True)
scores = model.predict([
    ("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
    ("How many people live in Berlin?", "Berlin is well known for its museums."),
])
print(scores)
# [ 8.122868 -4.292924]

Basic Usage with Transformers

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained("cross-encoder/monoelectra-base", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("cross-encoder/monoelectra-base")

features = tokenizer(
    [
        ("How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."),
        ("How many people live in Berlin?", "Berlin is well known for its museums."),
    ],
    padding=True,
    truncation=True,
    return_tensors="pt",
)

model.eval()
with torch.no_grad():
    scores = model(**features).logits.view(-1)
print(scores)
# tensor([ 8.1229, -4.2929])

📄 License

This model is licensed under the Apache 2.0 license.