Datasets:

louisbrulenaudet
/

lemone-docs-embedded

	---
	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](mailto:[email protected])

	<img src="assets/thumbnail.webp">

	# Lemone-embedded, pre-built embeddings dataset for French taxation.

	<div class="not-prose bg-gradient-to-r from-gray-50-to-white text-gray-900 border" style="border-radius: 8px; padding: 0.5rem 1rem;">
	<p>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.</p>
	</div>

	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:

	```python
	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:

	```python
	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.

	```BibTeX
	@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 [[email protected]](mailto:[email protected]).