File size: 6,569 Bytes
b8fcf2a 8204edd b8fcf2a 8204edd f3a79d2 55d45c2 8204edd bd81cc7 8204edd bd81cc7 8204edd bd81cc7 8204edd bd81cc7 8204edd bd81cc7 8204edd bd81cc7 b8fcf2a d6dbdb4 b8fcf2a c951e8f b8fcf2a 8204edd b8fcf2a 8204edd b8fcf2a 8204edd b8fcf2a 8204edd b8fcf2a d6dbdb4 8204edd b8fcf2a 8204edd b8fcf2a 8204edd b8fcf2a 8204edd b8fcf2a 8204edd b8fcf2a 8204edd b8fcf2a 8204edd b8fcf2a d6dbdb4 b8fcf2a 8204edd b8fcf2a 8204edd b8fcf2a 8204edd c951e8f b8fcf2a 8204edd b8fcf2a 8204edd b8fcf2a 8204edd b8fcf2a 8204edd b8fcf2a 8204edd b8fcf2a c951e8f b8fcf2a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
---
pipeline_tag: sentence-similarity
language: fr
license: mit
datasets:
- unicamp-dl/mmarco
metrics:
- recall
tags:
- passage-retrieval
library_name: sentence-transformers
base_model: dbmdz/electra-base-french-europeana-cased-discriminator
model-index:
- name: biencoder-electra-base-french-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_500
name: Recall@500
value: 81.6
- type: recall_at_100
name: Recall@100
value: 69.0
- type: recall_at_10
name: Recall@10
value: 43.5
- type: map_at_10
name: MAP@10
value: 22.9
- type: ndcg_at_10
name: nDCG@10
value: 28.0
- type: mrr_at_10
name: MRR@10
value: 23.4
---
# biencoder-electra-base-french-mmarcoFR
This is a dense single-vector bi-encoder model for **French** that can be used for semantic search. The model maps queries and passages to 768-dimensional dense vectors which are used to compute relevance through cosine similarity.
## 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 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-electra-base-french-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](https://github.com/FlagOpen/FlagEmbedding/): `pip install -U FlagEmbedding`. Then, you can use the model like this:
```python
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-electra-base-french-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](https://huggingface.co/docs/transformers): `pip install -U transformers`. Then, you can use the model like this:
```python
from transformers import AutoTokenizer, AutoModel
from torch.nn.functional import normalize
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-electra-base-french-mmarcoFR')
model = AutoModel.from_pretrained('antoinelouis/biencoder-electra-base-french-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
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).
To see how it compares to other neural retrievers in French, check out the [*DécouvrIR*](https://huggingface.co/spaces/antoinelouis/decouvrir) leaderboard.
## 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 do not employ the BM25 netaives provided by the official dataset but instead sample harder negatives mined from 12 distinct dense retrievers, using the [msmarco-hard-negatives](https://huggingface.co/datasets/sentence-transformers/msmarco-hard-negatives) distillation dataset.
#### 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 cross-entropy loss (as in [DPR](https://doi.org/10.48550/arXiv.2004.04906)) with a temperature of 0.05. It is fine-tuned on one 32GB
NVIDIA V100 GPU for 20 epochs (i.e., 62.4k steps) using the AdamW optimizer with a batch size of 160, a peak learning rate of 2e-5 with warm up along the first 500
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
```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',
}
``` |