Fine-tuning was performed using PyLate, with contrastive training on the rag-comprehensive-triplets dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

Dataset

reasonir/reasonir-data

Description

This dataset has been used for the English language and contains 101,000 examples, designed for rag-comprehensive-triplets, using a data preprocessing script from the BRIGHT dataset.

Fine-Tuning Details

The model was trained using the Contrastive Training.
Evaluated with pylate.evaluation.colbert_triplet.ColBERTTripletEvaluator

Metric	Value
accuracy	0.997

Usage

First install the PyLate library:

pip install -U pylate

Retrieval

PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.

Indexing documents

First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:

python

import torch
from pylate import indexes, models, retrieve

# Step 1: Load the ColBERT model and Move the model to GPU if available, otherwise use CPU
model = models.ColBERT(
    model_name_or_path=("fjmgAI/reason-colBERT-150M-GTE-ModernColBERT", trust_remote_code=True)
)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model.to(device)

# Step 2: Initialize the Voyager index
index = indexes.Voyager(
    index_folder="pylate-index",
    index_name="index",
    override=True,  # This overwrites the existing index if any
)

# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]

documents_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
    show_progress_bar=True,
)

# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=documents_embeddings,
)

Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:

python

# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
    index_folder="pylate-index",
    index_name="index",
)

Retrieving top-k documents for queries

Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:

python

# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)

# Step 2: Encode the queries
queries_embeddings = model.encode(
    ["query for document 3", "query for document 1"],
    batch_size=32,
    is_query=True,  #  # Ensure that it is set to False to indicate that these are queries
    show_progress_bar=True,
)

# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
    queries_embeddings=queries_embeddings,
    k=10,  # Retrieve the top 10 matches for each query
)

Reranking

If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:

python

import torch
from pylate import rank, models

queries = [
    "query A",
    "query B",
]

documents = [
    ["document A", "document B"],
    ["document 1", "document C", "document B"],
]

documents_ids = [
    [1, 2],
    [1, 3, 2],
]

model = models.ColBERT(
    model_name_or_path=("fjmgAI/reason-colBERT-150M-GTE-ModernColBERT", trust_remote_code=True),
)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model.to(device)

queries_embeddings = model.encode(
    queries,
    is_query=True,
)

documents_embeddings = model.encode(
    documents,
    is_query=False,
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)

Framework Versions

Python: 3.10.12
Sentence Transformers: 4.0.2
PyLate: 1.2.0
Transformers: 4.48.2
PyTorch: 2.5.1+cu121
Accelerate: 1.2.1
Datasets: 3.3.1
Tokenizers: 0.21.0

Purpose

This tuned model is designed to be used in scenarios that require efficient embedding-based retrieval through reasoning comparing embeddings at the token level with its MaxSim operation, ideal for question-answering and document retrieval.

Developed by: fjmgAI
License: Unfortunately, since the ReasonIR data has been released under a cc-by-nc-4.0 license, we cannot release this model under an Apache 2.0 license. However, the authors of ReasonIR released code to generate the data. Anyone willing to reproduce the data could then easily reproduce this model under an Apache 2.0