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---
license: bigscience-bloom-rail-1.0
language:
- fr
- en
pipeline_tag: feature-extraction
---
# Note
We now strongly recommend using the [Bloomz-3b-retriever-v2](https://huggingface.co/cmarkea/bloomz-3b-retriever-v2) model, **which offers significantly superior performance**.
Bloomz-3b-retriever
---------------------
We introduce the Bloomz-3b-retriever based on the [Bloomz-3b-sft-chat model](https://huggingface.co/cmarkea/bloomz-3b-sft-chat). This model enables the creation of an embedding representation of text and queries for a retrieval task, linking queries to documents. The model is designed to be cross-language, meaning it is language-agnostic (English/French). This model is ideal for Open Domain Question Answering (ODQA), projecting queries and text with an algebraic structure to bring them closer together.
Training
--------
It is a bi-encoder trained on a corpus of context/query pairs, with 50% in English and 50% in French. The language distribution for queries and contexts is evenly split (1/4 French-French, 1/4 French-English, 1/4 English-French, 1/4 English-English). The learning objective is to bring the embedding representation of queries and associated contexts closer using a contrastive method. The loss function is defined in [Deep Metric Learning using Triplet Network](https://arxiv.org/abs/1412.6622).
Benchmark
---------
Based on the SQuAD evaluation dataset (comprising 6000 queries distributed over 1200 contexts grouped into 35 themes), we compare the performance in terms of the average top contexter value for a query (Top-mean), the standard deviation of the average top (Top-std), and the percentage of correct queries within the top-1, top-5, and top-10. We compare the model with a TF-IDF trained on the SQuAD train sub-dataset (we want a fixed algebraic structure for the vector database instead of a variable structure every time we add a new document, then the IDF part has frozen), CamemBERT, Sentence-BERT, and finally our model. We observe these performances in both monolingual and cross-language contexts (query in French and context in English).
Model (FR/FR) | Top-mean | Top-std | Top-1 (%) | Top-5 (%) | Top-10 (%) |
|----------------------------------------------------------------------------------------------------:|:--------:|:-------:|:---------:|:---------:|:----------:|
| TF-IDF | 128 | 269 | 23 | 46 | 56 |
| [CamemBERT](https://huggingface.co/camembert/camembert-base) | 417 | 347 | 1 | 2 | 3 |
| [Sentence-BERT](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) | 11 | 41 | 43 | 71 | 82 |
| [Bloomz-560m-retriever](https://huggingface.co/cmarkea/bloomz-560m-retriever) | 10 | 47 | 51 | 78 | 86 |
| [Bloomz-3b-retriever](https://huggingface.co/cmarkea/bloomz-3b-retriever) | 9 | 37 | 50 | 79 | 87 |
Model (EN/FR) | Top-mean | Top-std | Top-1 (%) | Top-5 (%) | Top-10 (%) |
|----------------------------------------------------------------------------------------------------:|:--------:|:-------:|:---------:|:---------:|:-----------:|
| TF-IDF | 607 | 334 | 0 | 0 | 0 |
| [CamemBERT](https://huggingface.co/camembert/camembert-base) | 432 | 345 | 0 | 1 | 1 |
| [Sentence-BERT](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) | 12 | 47 | 44 | 73 | 83 |
| [Bloomz-560m-retriever](https://huggingface.co/cmarkea/bloomz-560m-retriever) | 10 | 44 | 49 | 77 | 86 |
| [Bloomz-3b-retriever](https://huggingface.co/cmarkea/bloomz-3b-retriever) | 9 | 38 | 50 | 78 | 87 |
We observed that TF-IDF loses robustness in cross-language scenarios (even showing lower performance than CamemBERT, which is a model specialized in French). This can be explained by the fact that a Bag-Of-Words method cannot support this type of issue because, for a given sentence between two languages, the embedding vectors will be significantly different.
CamemBERT exhibits poor performance, not because it poorly groups contexts and queries by themes, but because a meta-cluster appears, separating contexts and queries (as illustrated in the image below), making this type of modeling inappropriate in a retriever context.
How to Use Bloomz-3b-retriever
--------------------------------
The following example utilizes the API Pipeline of the Transformers library.
```python
import numpy as np
from transformers import pipeline
from scipy.spatial.distance import cdist
retriever = pipeline('feature-extraction', 'cmarkea/bloomz-3b-retriever')
# Inportant: take only last token!
infer = lambda x: [ii[0][-1] for ii in retriever(x)]
list_of_contexts = [...]
emb_contexts = np.concatenate(infer(list_of_contexts), axis=0)
list_of_queries = [...]
emb_queries = np.concatenate(infer(list_of_queries), axis=0)
# Important: take l2 distance!
dist = cdist(emb_queries, emb_contexts, 'euclidean')
top_k = lambda x: [
[list_of_contexts[qq] for qq in ii]
for ii in dist.argsort(axis=-1)[:,:x]
]
# top 5 nearest contexts for each queries
top_contexts = top_k(5)
```
Citation
--------
```bibtex
@online{DeBloomzRet,
AUTHOR = {Cyrile Delestre},
ORGANIZATION = {Cr{\'e}dit Mutuel Ark{\'e}a},
URL = {https://huggingface.co/cmarkea/bloomz-3b-retriever},
YEAR = {2023},
KEYWORDS = {NLP ; Transformers ; LLM ; Bloomz},
}
``` |