Colbertv2 Camembert L4 Mmarcofr

🚀 colbertv2-camembert-L4-mmarcoFR

This is a lightweight ColBERTv2 model for French that can be used for semantic search. It encodes queries and passages into matrices of token-level embeddings and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators.

🚀 Quick Start

✨ Features

• Encodes queries and passages into matrices of token - level embeddings.
• Efficiently finds contextually matching passages using scalable vector - similarity operators.
• Suitable for semantic search in French.

📦 Installation

Using RAGatouille

First, you will need to install the following libraries:

pip install -U ragatouille

Using ColBERT - AI

First, you will need to install the following libraries:

pip install git+https://github.com/stanford-futuredata/ColBERT.git torch faiss-gpu==1.7.2

💻 Usage Examples

Basic Usage

Using RAGatouille

from ragatouille import RAGPretrainedModel

index_name: str = "my_index" # The name of your index, i.e. the name of your vector database
documents: list = ["Ceci est un premier document.", "Voici un second document.", "etc."] # Corpus

# Step 1: Indexing.
RAG = RAGPretrainedModel.from_pretrained("antoinelouis/colbertv2-camembert-L4-mmarcoFR")
RAG.index(name=index_name, collection=documents)

# Step 2: Searching.
RAG = RAGPretrainedModel.from_index(index_name) # if not already loaded
RAG.search(query="Comment effectuer une recherche avec ColBERT ?", k=10)

Using ColBERT - AI

from colbert import Indexer, Searcher
from colbert.infra import Run, RunConfig

n_gpu: int = 1 # Set your number of available GPUs
experiment: str = "colbert" # Name of the folder where the logs and created indices will be stored
index_name: str = "my_index" # The name of your index, i.e. the name of your vector database
documents: list = ["Ceci est un premier document.", "Voici un second document.", "etc."] # Corpus

# Step 1: Indexing. This step encodes all passages into matrices, stores them on disk, and builds data structures for efficient search.
with Run().context(RunConfig(nranks=n_gpu,experiment=experiment)):
    indexer = Indexer(checkpoint="antoinelouis/colbertv2-camembert-L4-mmarcoFR")
    indexer.index(name=index_name, collection=documents)

# Step 2: Searching. Given the model and index, you can issue queries over the collection to retrieve the top-k passages for each query.
with Run().context(RunConfig(nranks=n_gpu,experiment=experiment)):
    searcher = Searcher(index=index_name) # You don't need to specify checkpoint again, the model name is stored in the index.
    results = searcher.search(query="Comment effectuer une recherche avec ColBERT ?", k=10)
    # results: tuple of tuples of length k containing ((passage_id, passage_rank, passage_score), ...)

📚 Documentation

Evaluation

The model is evaluated on the smaller development set of mMARCO - fr, which consists of 6,980 queries for a corpus of 8.8M candidate passages. We report the mean reciprocal rank (MRR), normalized discounted cumulative gainand (NDCG), mean average precision (MAP), and recall at various cut - offs (R@k). Below, we compare its performance with other publicly available French ColBERT models fine - tuned on the same dataset. To see how it compares to other neural retrievers in French, check out the DécouvrIR leaderboard.