---
pipeline_tag: sentence-similarity
language: fr
license: mit
datasets:
- unicamp-dl/mmarco
metrics:
- recall
tags:
- colbert
- passage-retrieval
base_model: camembert-base
library_name: RAGatouille
inference: false
model-index:
- name: colbertv1-camembert-base-mmarcoFR
  results:
    - task:
        type: sentence-similarity
        name: Passage Retrieval
      dataset:
        type: unicamp-dl/mmarco
        name: mMARCO-fr
        config: french
        split: validation
      metrics:
        - type: recall_at_1000
          name: Recall@1000
          value: 89.7
        - type: recall_at_500
          name: Recall@500
          value: 88.4
        - type: recall_at_100
          name: Recall@100
          value: 80.0
        - type: recall_at_10
          name: Recall@10
          value: 54.2
        - type: mrr_at_10
          name: MRR@10
          value: 29.5
---

# colbertv1-camembert-base-mmarcoFR

This is a [ColBERTv1](https://doi.org/10.48550/arXiv.2004.12832) 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](https://github.com/bclavie/RAGatouille) or [colbert-ai](https://github.com/stanford-futuredata/ColBERT).

### Using RAGatouille

First, you will need to install the following libraries:

```bash
pip install -U ragatouille
```

Then, you can use the model like this:

```python
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:

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

Then, you can use the model like this:

```python
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](https://ir-datasets.com/mmarco.html#mmarco/v2/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*](https://huggingface.co/spaces/antoinelouis/decouvrir) leaderboard.

| model                                                                                                      | #Param.(↓) |  Size | Dim. | Index | R@1000 | R@500 | R@100 | R@10 | MRR@10 |     
|:-----------------------------------------------------------------------------------------------------------|-----------:|------:|-----:|------:|-------:|------:|------:|-----:|-------:|
| [colbertv2-camembert-L4-mmarcoFR](https://huggingface.co/antoinelouis/colbertv2-camembert-L4-mmarcoFR)     |        54M | 0.2GB |   32 |   9GB |   91.9 |  90.3 |  81.9 | 56.7 |   32.3 | 
| [FraColBERTv2](https://huggingface.co/bclavie/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](https://huggingface.co/datasets/unicamp-dl/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](https://microsoft.github.io/msmarco/Datasets.html#passage-ranking-dataset).

#### Implementation

The model is initialized from the [camembert-base](https://huggingface.co/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](https://doi.org/10.48550/arXiv.2004.12832)) 
and the in-batch sampled softmax cross-entropy loss (as in [ColBERTv2](https://doi.org/10.48550/arXiv.2112.01488)). 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

```bibtex
@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',
}
```