---
pipeline_tag: text-classification
language: fr
license: mit
datasets:
- unicamp-dl/mmarco
metrics:
- recall
tags:
- passage-reranking
library_name: sentence-transformers
base_model: dbmdz/electra-base-french-europeana-cased-discriminator
---

# crossencoder-electra-base-french-mmarcoFR

This is a cross-encoder model for French. It performs cross-attention between a question-passage pair and outputs a relevance score. 
The model should be used as a reranker for semantic search: given a query and a set of potentially relevant passages retrieved by an efficient first-stage 
retrieval system (e.g., BM25 or a fine-tuned dense single-vector bi-encoder), encode each query-passage pair and sort the passages in a decreasing order of 
relevance according to the model's predicted scores.

## Usage

Here are some examples for using the model with [Sentence-Transformers](#using-sentence-transformers), [FlagEmbedding](#using-flagembedding), or [Huggingface Transformers](#using-huggingface-transformers).

#### Using Sentence-Transformers

Start by installing the [library](https://www.SBERT.net): `pip install -U sentence-transformers`. Then, you can use the model like this:

```python
from sentence_transformers import CrossEncoder

pairs = [('Question', 'Paragraphe 1'), ('Question', 'Paragraphe 2') , ('Question', 'Paragraphe 3')]

model = CrossEncoder('antoinelouis/crossencoder-electra-base-french-mmarcoFR')
scores = model.predict(pairs)
print(scores)
```

#### Using FlagEmbedding

Start by installing the [library](https://github.com/FlagOpen/FlagEmbedding/): `pip install -U FlagEmbedding`. Then, you can use the model like this:

```python
from FlagEmbedding import FlagReranker

pairs = [('Question', 'Paragraphe 1'), ('Question', 'Paragraphe 2') , ('Question', 'Paragraphe 3')]

reranker = FlagReranker('antoinelouis/crossencoder-electra-base-french-mmarcoFR')
scores = reranker.compute_score(pairs)
print(scores)
```

#### Using HuggingFace Transformers

Start by installing the [library](https://huggingface.co/docs/transformers): `pip install -U transformers`. Then, you can use the model like this:

```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

pairs = [('Question', 'Paragraphe 1'), ('Question', 'Paragraphe 2') , ('Question', 'Paragraphe 3')]

tokenizer = AutoTokenizer.from_pretrained('antoinelouis/crossencoder-electra-base-french-mmarcoFR')
model = AutoModelForSequenceClassification.from_pretrained('antoinelouis/crossencoder-electra-base-french-mmarcoFR')
model.eval()

with torch.no_grad():
    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
    scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```

***

## Evaluation

We evaluate the model on 500 random training queries from [mMARCO-fr](https://ir-datasets.com/mmarco.html#mmarco/v2/fr/) (which were excluded from training) by reranking 
subsets of candidate passages comprising of at least one relevant and up to 200 BM25 negative passages for each query. Below, we compare the model performance with other 
cross-encoder models fine-tuned on the same dataset. We report the R-precision (RP), mean reciprocal rank (MRR), and recall at various cut-offs (R@k).

|    | model                                                                                                                        | Vocab. | #Param. |  Size |     RP |   MRR@10 |  R@10(↑) |   R@20 |   R@50 |   R@100 |
|---:|:-----------------------------------------------------------------------------------------------------------------------------|:-------|--------:|------:|-------:|---------:|---------:|-------:|-------:|--------:|
|  1 | [crossencoder-camembert-base-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-camembert-base-mmarcoFR)             |     fr |    110M | 443MB |  35.65 |    50.44 |    82.95 |  91.50 |  96.80 |   98.80 |
|  2 | [crossencoder-mMiniLMv2-L12-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-mMiniLMv2-L12-mmarcoFR)               | fr,99+ |    118M | 471MB |  34.37 |    51.01 |    82.23 |  90.60 |  96.45 |   98.40 |
|  3 | [crossencoder-distilcamembert-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-distilcamembert-mmarcoFR)           |     fr |     68M | 272MB |  27.28 |    43.71 |    80.30 |  89.10 |  95.55 |   98.60 |
|  4 | **crossencoder-electra-base-french-mmarcoFR**                                                                                |     fr |    110M | 443MB |  28.32 |    45.28 |    79.22 |  87.15 |  93.15 |   95.75 |
|  5 | [crossencoder-mMiniLMv2-L6-mmarcoFR](https://huggingface.co/antoinelouis/crossencoder-mMiniLMv2-L6-mmarcoFR)                 | fr,99+ |    107M | 428MB |  33.92 |    49.33 |    79.00 |  88.35 |  94.80 |   98.20 |

***

## Training

#### Data

We use the French training samples 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 1M question-passage pairs from the official ~39.8M 
[training triples](https://microsoft.github.io/msmarco/Datasets.html#passage-ranking-dataset) with a positive-to-negative ratio of 4 (i.e., 25% of the pairs are 
relevant and 75% are irrelevant).

#### Implementation

The model is initialized from the [dbmdz/electra-base-french-europeana-cased-discriminator](https://huggingface.co/dbmdz/electra-base-french-europeana-cased-discriminator) checkpoint and optimized via the binary cross-entropy loss 
(as in [monoBERT](https://doi.org/10.48550/arXiv.1910.14424)). It is fine-tuned on one 32GB NVIDIA V100 GPU for 10 epochs (i.e., 312.4k steps) using the AdamW optimizer 
with a batch size of 32, a peak learning rate of 2e-5 with warm up along the first 500 steps and linear scheduling. We set the maximum sequence length of the
concatenated question-passage pairs to 512 tokens. We use the sigmoid function to get scores between 0 and 1.

***

## Citation

```bibtex
@online{louis2023,
   author    = 'Antoine Louis',
   title     = 'crossencoder-electra-base-french-mmarcoFR: A Cross-Encoder Model Trained on 1M sentence pairs in French',
   publisher = 'Hugging Face',
   month     = 'september',
   year      = '2023',
   url       = 'https://huggingface.co/antoinelouis/crossencoder-electra-base-french-mmarcoFR',
}
```