llama-nemotron-rerank

Model Overview

Description

The Llama Nemotron Reranking 1B model is optimized for providing a logit score that represents how relevant a document(s) is to a given query. The model was fine-tuned for multilingual, cross-lingual text question-answering retrieval, with support for long documents (up to 8192 tokens). This model was evaluated on 26 languages: English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish.

This model is a component in a text retrieval system to improve the overall accuracy. A text retrieval system often uses an embedding model (dense) or lexical search (sparse) index to return relevant text passages given the input. A reranking model can be used to rerank the potential candidate into a final order. The reranking model has the question-passage pairs as an input and therefore, can process cross attention between the words. It’s not feasible to apply a Ranking model on all documents in the knowledge base, therefore, ranking models are often deployed in combination with embedding models.

This model is ready for commercial use.

The Llama Nemotron Reranking 1B model is a part of the NeMo Retriever collection of NIM, which provide state-of-the-art, commercially-ready models and microservices, optimized for the lowest latency and highest throughput. It features a production-ready information retrieval pipeline with enterprise support. The models that form the core of this solution have been trained using responsibly selected, auditable data sources. With multiple pre-trained models available as starting points, developers can also readily customize them for their domain-specific use cases, such as information technology, human resource help assistants, and research & development research assistants.

We are excited to announce the open sourcing of this commercial embedding model. For users interested in deploying this model in production environments, it is also available via the model API in NVIDIA Inference Microservices (NIM) at llama-nemotron-rerank-1b-v2.

License/Terms of use

Use of this model is governed by the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1). Additional Information: Llama 3.2 Community Model License Agreement; Built with Llama.

This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.

Intended use

The Llama Nemotron Reranking 1B model is most suitable for users who want to improve their multilingual retrieval tasks by reranking a set of candidates for a given question.

Model Architecture

Architecture Type: Transformer Network Architecture: Fine-tuned ranker model from the meta-llama/Llama-3.2-1B model.

The Llama Nemotron Reranking 1B model is a transformer cross-encoder fine-tuned with contrastive learning. We employ bi-directional attention when fine-tuning for higher accuracy. The last embedding output by the decoder model is used with a mean pooling strategy, and a binary classification head is fine-tuned for the ranking task.

Ranking models for text ranking are typically trained as a cross-encoder for sentence classification. This involves predicting the relevancy of a sentence pair (for example, question and chunked passages). The CrossEntropy loss is used to maximize the likelihood of passages containing information to answer the question and minimize the likelihood for (negative) passages that do not contain information to answer the question.

We trained the model on public datasets described in the Dataset and Training section.

Input

Input Type: Pair of Texts Input Format: List of text pairs Input Parameters: 1D Other Properties Related to Input: The model was trained on question and answering over text documents from multiple languages. It was evaluated to work successfully with up to a sequence length of 8192 tokens. Longer texts are recommended to be either chunked or truncated.

Output

Output Type: Floats Output Format: List of floats Output Parameters: 1D Other Properties Related to Output: Each value corresponds to a raw logit. Users can choose to apply a Sigmoid activation function to the logits to convert them into probabilities during model usage.

Installation

The model requires transformers version 4.44 or above.

pip install transformers>=4.44

Usage

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name_or_path = "nvidia/llama-nemotron-rerank-1b-v2"

device = "cuda:0"
max_length = 512

queries = [
    "how much protein should a female eat?",
]
documents = [
    "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
    "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments.",
    "Calorie intake should not fall below 1,200 a day in women or 1,500 a day in men, except under the supervision of a health professional."
]

# Create pairs from queries and documents
pairs = [[q, d] for q in queries for d in documents]

def prompt_template(q, p):
    """Format query and passage with a prompt template."""
    return f"question:{q} \n \n passage:{p}"

tokenizer = AutoTokenizer.from_pretrained(
    model_name_