colbertv1-camembert-base-mmarcoFR
This is a ColBERTv1 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.
Usage
Here are some examples for using the model with RAGatouille or colbert-ai.
Using RAGatouille
First, you will need to install the following libraries:
pip install -U ragatouille
Then, you can use the model like this:
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/colbertv1-camembert-base-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
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
Then, you can use the model like this:
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/colbertv1-camembert-base-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), ...)
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.
model | #Param.(↓) | Size | Dim. | Index | R@1000 | R@500 | R@100 | R@10 | MRR@10 |
---|---|---|---|---|---|---|---|---|---|
colbertv2-camembert-L4-mmarcoFR | 54M | 0.2GB | 32 | 9GB | 91.9 | 90.3 | 81.9 | 56.7 | 32.3 |
FraColBERTv2 | 111M | 0.4GB | 128 | 28GB | 90.0 | 88.9 | 81.2 | 57.1 | 32.4 |
colbertv1-camembert-base-mmarcoFR | 111M | 0.4GB | 128 | 28GB | 89.7 | 88.4 | 80.0 | 54.2 | 29.5 |
NB: Index corresponds to the size of the mMARCO-fr index (8.8M passages) on disk when using ColBERTv2's residual compression mechanism.
Training
Data
We use the French training set from the mMARCO dataset, a multilingual machine-translated version of MS MARCO that contains 8.8M passages and 539K training queries. We sample 12.8M (q, p+, p-) triples from the official ~39.8M training triples.
Implementation
The model is initialized from the camembert-base checkpoint and optimized via a combination of the pairwise softmax cross-entropy loss computed over predicted scores for the positive and hard negative passages (as in ColBERTv1) and the in-batch sampled softmax cross-entropy loss (as in ColBERTv2). It was trained on a single Tesla V100 GPU with 32GBs of memory during 200k steps using a batch size of 64 and the AdamW optimizer with a constant learning rate of 3e-06. The embedding dimension was set to 128, and the maximum sequence lengths for questions and passages length were fixed to 32 and 256 tokens, respectively.
Citation
@online{louis2024decouvrir,
author = 'Antoine Louis',
title = 'DécouvrIR: A Benchmark for Evaluating the Robustness of Information Retrieval Models in French',
publisher = 'Hugging Face',
month = 'mar',
year = '2024',
url = 'https://huggingface.co./spaces/antoinelouis/decouvrir',
}
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Evaluation results
- Recall@1000 on mMARCO-frvalidation set self-reported89.700
- Recall@500 on mMARCO-frvalidation set self-reported88.400
- Recall@100 on mMARCO-frvalidation set self-reported80.000
- Recall@10 on mMARCO-frvalidation set self-reported54.200
- MRR@10 on mMARCO-frvalidation set self-reported29.500