pipeline_tag: sentence-similarity
language: fr
license: mit
datasets:
- unicamp-dl/mmarco
metrics:
- recall
tags:
- feature-extraction
- sentence-similarity
library_name: sentence-transformers
biencoder-camembert-L4-mmarcoFR
🛠️ Usage | 📊 Evaluation | 🤖 Training | 🔗 Citation
This is a sentence-transformers model. It maps questions and paragraphs 768-dimensional dense vectors and should be used for semantic search. The model uses an CamemBERT-L4 backbone, which is a pruned version of the pre-trained CamemBERT checkpoint with 51% less parameters, obtained by dropping the top-layers from the original model. The model was trained on the French portion of the mMARCO retrieval dataset.
Usage
Here are some examples for using this model with Sentence-Transformers, FlagEmbedding, or Huggingface Transformers.
Using Sentence-Transformers
Start by installing the library: pip install -U sentence-transformers. Then, you can use the model like this:
from sentence_transformers import SentenceTransformer
queries = ["Ceci est un exemple de requête.", "Voici un second exemple."]
passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."]
model = SentenceTransformer('antoinelouis/biencoder-camembert-L4-mmarcoFR')
q_embeddings = model.encode(queries, normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
similarity = q_embeddings @ p_embeddings.T
print(similarity)
Using FlagEmbedding
Start by installing the library: pip install -U FlagEmbedding. Then, you can use the model like this:
from FlagEmbedding import FlagModel
queries = ["Ceci est un exemple de requête.", "Voici un second exemple."]
passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."]
model = FlagModel('antoinelouis/biencoder-camembert-L4-mmarcoFR')
q_embeddings = model.encode(queries, normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
similarity = q_embeddings @ p_embeddings.T
print(similarity)
Using Transformers
Start by installing the library: pip install -U transformers. Then, you can use the model like this:
import torch
from torch.nn.functional import normalize
from transformers import AutoTokenizer, AutoModel
def mean_pooling(model_output, attention_mask):
""" Perform mean pooling on-top of the contextualized word embeddings, while ignoring mask tokens in the mean computation."""
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
queries = ["Ceci est un exemple de requête.", "Voici un second exemple."]
passages = ["Ceci est un exemple de passage.", "Et voilà un deuxième exemple."]
tokenizer = AutoTokenizer.from_pretrained('antoinelouis/biencoder-camembert-L4-mmarcoFR')
model = AutoModel.from_pretrained('antoinelouis/biencoder-camembert-L4-mmarcoFR')
q_input = tokenizer(queries, padding=True, truncation=True, return_tensors='pt')
p_input = tokenizer(passages, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
q_output = model(**encoded_queries)
p_output = model(**encoded_passages)
q_embeddings = mean_pooling(q_output, q_input['attention_mask'])
q_embedddings = normalize(q_embeddings, p=2, dim=1)
p_embeddings = mean_pooling(p_output, p_input['attention_mask'])
p_embedddings = normalize(p_embeddings, p=2, dim=1)
similarity = q_embeddings @ p_embeddings.T
print(similarity)
Evaluation
We evaluate the model on the smaller development set of mMARCO-fr, which consists of 6,980 queries for a corpus of 8.8M candidate passages. Below, we compare the model performance with other CamemBERT-based biencoder models fine-tuned on the same dataset. We report the mean reciprocal rank (MRR), normalized discounted cumulative gainand (NDCG), mean average precision (MAP), and recall at various cut-offs (R@k).
| model | #Param. | Size | R@500 | R@100(↑) | R@10 | MRR@10 | NDCG@10 | MAP@10 | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | biencoder-camembert-base-mmarcoFR | 111M | 445MB | 89.1 | 77.8 | 51.5 | 28.5 | 33.7 | 27.9 |
| 2 | biencoder-camembert-L10-mmarcoFR | 96M | 386MB | 87.8 | 76.7 | 49.5 | 27.5 | 32.5 | 27.0 |
| 3 | biencoder-camembert-L8-mmarcoFR | 82M | 329MB | 87.4 | 75.9 | 48.9 | 26.7 | 31.8 | 26.2 |
| 4 | biencoder-camembert-L6-mmarcoFR | 68M | 272MB | 86.7 | 74.9 | 46.7 | 25.7 | 30.4 | 25.1 |
| 5 | biencoder-camembert-L4-mmarcoFR | 54M | 216MB | 85.4 | 72.1 | 44.2 | 23.7 | 28.3 | 23.2 |
| 6 | biencoder-camembert-L2-mmarcoFR | 40M | 159MB | 81.0 | 66.3 | 38.5 | 20.1 | 24.3 | 19.7 |
Training
Data
We use the French training samples from the mMARCO dataset, a multilingual machine-translated version of MS MARCO that contains 8.8M passages and 539K training queries. We do not employ the BM25 negatives provided by the official dataset but instead sample harder negatives mined from 12 distinct dense retrievers, using the msmarco-hard-negatives distillation dataset.
Implementation
The model is initialized from the camembert-L4 checkpoint and optimized via the cross-entropy loss (as in DPR) with a temperature of 0.05. It is fine-tuned on one 32GB NVIDIA V100 GPU for 17.4k steps (or 40 epochs) using the AdamW optimizer with a batch size of 1152, a peak learning rate of 2e-5 with warm up along the first 1736 steps and linear scheduling. We set the maximum sequence lengths for both the questions and passages to 128 tokens. We use the cosine similarity to compute relevance scores.
Citation
@online{louis2023,
author = 'Antoine Louis',
title = 'biencoder-camembert-L4-mmarcoFR: A Biencoder Model Trained on French mMARCO',
publisher = 'Hugging Face',
month = 'may',
year = '2023',
url = 'https://huggingface.co/antoinelouis/biencoder-camembert-L4-mmarcoFR',
}