Datasets:
dataset_info:
features:
- name: text
dtype: string
- name: title_main
dtype: string
- name: id_sub
dtype: string
- name: url_sourcepage
dtype: string
- name: date_publication
dtype: string
- name: hash
dtype: string
- name: lemone_pro_embeddings
sequence: float64
splits:
- name: train
num_bytes: 187013397
num_examples: 16073
download_size: 119486532
dataset_size: 187013397
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: apache-2.0
task_categories:
- question-answering
language:
- fr
tags:
- tax
- legal
- fiscalite
- droit
- taxation
pretty_name: Lemone-embeded dataset for French tax RAG over legal documents
size_categories:
- 10K<n<100K
Dataset Description
- Repository: https://huggingface.co/datasets/louisbrulenaudet/lemone-docs-embedded
- Point of Contact: Louis Brulé Naudet
Lemone-embedded, pre-built embeddings dataset for French taxation.
This database presents the embeddings generated by the Lemone-embed-pro model and aims at a large-scale distribution of the model even for the GPU-poor.
This sentence transformers model, specifically designed for French taxation, has been fine-tuned on a dataset comprising 43 million tokens, integrating a blend of semi-synthetic and fully synthetic data generated by GPT-4 Turbo and Llama 3.1 70B, which have been further refined through evol-instruction tuning and manual curation.
The model is tailored to meet the specific demands of information retrieval across large-scale tax-related corpora, supporting the implementation of production-ready Retrieval-Augmented Generation (RAG) applications. Its primary purpose is to enhance the efficiency and accuracy of legal processes in the taxation domain, with an emphasis on delivering consistent performance in real-world settings, while also contributing to advancements in legal natural language processing research.
This is a sentence-transformers model finetuned from Alibaba-NLP/gte-multilingual-base. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
Usage with ChromaDB
We recommend integration via a vector-store to produce an optimal RAG pipeline. Here's a code extract for producing such a database with ChromaDB:
import chromadb
import polars as pl
from chromadb.config import Settings
from chromadb.utils import embedding_functions
from torch.cuda import is_available
client = chromadb.PersistentClient(
path="./chroma.db",
settings=Settings(anonymized_telemetry=False)
)
sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(
model_name="louisbrulenaudet/lemone-embed-pro",
device="cuda" if is_available() else "cpu",
trust_remote_code=True
)
collection = client.get_or_create_collection(
name="tax",
embedding_function=sentence_transformer_ef
)
dataframe = pl.scan_parquet('hf://datasets/louisbrulenaudet/lemone-docs-embedded/data/train-00000-of-00001.parquet').filter(
pl.col(
"text"
).is_not_null()
).collect()
collection.add(
embeddings=dataframe["lemone_pro_embeddings"].to_list(),
documents=dataframe["text"].to_list(),
metadatas=dataframe.drop(
[
"lemone_pro_embeddings",
"text"
]
).to_dicts(),
ids=[
str(i) for i in range(0, dataframe.shape[0])
]
)
Here is a code for reproduction of this dataset:
import hashlib
from datetime import datetime
from typing import (
IO,
TYPE_CHECKING,
Any,
Dict,
List,
Type,
Tuple,
Union,
Mapping,
TypeVar,
Callable,
Optional,
Sequence,
)
import chromadb
import polars as pl
from chromadb.config import Settings
from chromadb.utils import embedding_functions
from torch.cuda import is_available
client = chromadb.Client(
settings=Settings(anonymized_telemetry=False)
)
sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(
model_name="louisbrulenaudet/lemone-embed-pro",
device="cuda" if is_available() else "cpu",
trust_remote_code=True
)
collection = client.get_or_create_collection(
name="tax",
embedding_function=sentence_transformer_ef
)
bofip_dataframe = pl.scan_parquet(
"hf://datasets/louisbrulenaudet/bofip/data/train-00000-of-00001.parquet"
).with_columns(
[
(
pl.lit("Bulletin officiel des finances publiques - impôts").alias(
"title_main"
)
),
(
pl.col("debut_de_validite")
.str.strptime(pl.Date, format="%Y-%m-%d")
.dt.strftime("%Y-%m-%d 00:00:00")
).alias("date_publication"),
(
pl.col("contenu")
.map_elements(lambda x: hashlib.sha256(str(x).encode()).hexdigest(), return_dtype=pl.Utf8)
.alias("hash")
)
]
).rename(
{
"contenu": "text",
"permalien": "url_sourcepage",
"identifiant_juridique": "id_sub",
}
).select(
[
"text",
"title_main",
"id_sub",
"url_sourcepage",
"date_publication",
"hash"
]
)
books: List[str] = [
"hf://datasets/louisbrulenaudet/code-douanes/data/train-00000-of-00001.parquet",
"hf://datasets/louisbrulenaudet/code-impots/data/train-00000-of-00001.parquet",
"hf://datasets/louisbrulenaudet/code-impots-annexe-i/data/train-00000-of-00001.parquet",
"hf://datasets/louisbrulenaudet/code-impots-annexe-ii/data/train-00000-of-00001.parquet",
"hf://datasets/louisbrulenaudet/code-impots-annexe-iii/data/train-00000-of-00001.parquet",
"hf://datasets/louisbrulenaudet/code-impots-annexe-iv/data/train-00000-of-00001.parquet",
"hf://datasets/louisbrulenaudet/code-impositions-biens-services/data/train-00000-of-00001.parquet",
"hf://datasets/louisbrulenaudet/livre-procedures-fiscales/data/train-00000-of-00001.parquet"
]
legi_dataframe = pl.concat(
[
pl.scan_parquet(
book
) for book in books
]
).with_columns(
[
(
pl.lit("https://www.legifrance.gouv.fr/codes/article_lc/")
.add(pl.col("id"))
.alias("url_sourcepage")
),
(
pl.col("dateDebut")
.cast(pl.Int64)
.map_elements(
lambda x: datetime.fromtimestamp(x / 1000).strftime("%Y-%m-%d %H:%M:%S"),
return_dtype=pl.Utf8
)
.alias("date_publication")
),
(
pl.col("texte")
.map_elements(lambda x: hashlib.sha256(str(x).encode()).hexdigest(), return_dtype=pl.Utf8)
.alias("hash")
)
]
).rename(
{
"texte": "text",
"num": "id_sub",
}
).select(
[
"text",
"title_main",
"id_sub",
"url_sourcepage",
"date_publication",
"hash"
]
)
print("Starting embeddings production...")
dataframe = pl.concat(
[
bofip_dataframe,
legi_dataframe
]
).filter(
pl.col(
"text"
).is_not_null()
).with_columns(
pl.col("text").map_elements(
lambda x: sentence_transformer_ef(
[x]
)[0].tolist(),
return_dtype=pl.List(pl.Float64)
).alias("lemone_pro_embeddings")
).collect()
Citation
If you use this code in your research, please use the following BibTeX entry.
@misc{louisbrulenaudet2024,
author = {Louis Brulé Naudet},
title = {Lemone-Embed: A Series of Fine-Tuned Embedding Models for French Taxation},
year = {2024}
howpublished = {\url{https://huggingface.co/datasets/louisbrulenaudet/lemone-embed-pro}},
}
Feedback
If you have any feedback, please reach out at louisbrulenaudet@icloud.com.