Update README.md

README.md

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 ## Dataset Description
 - **Repository:** https://huggingface.co/datasets/louisbrulenaudet/lemone-docs-embeded
-- **Point of Contact:** [Louis Brulé Naudet](mailto:[email protected])
+- **Point of Contact:** [Louis Brulé Naudet](mailto:[email protected])
+
+<img src="assets/thumbnail.webp">
+
+# Lemone-embeded, 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-embeded/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]).