ferMorales/Gaceta_UNAM_BGE_M3_V2

Gaceta UNAM BGE-M3 V2 (Dense + Sparse)

Dataset Overview

Gaceta UNAM BGE-M3 V2 is a dataset of semantic embeddings for text chunks extracted from issues of Gaceta UNAM, generated with BAAI/bge-m3. It includes both dense and sparse (lexical) representations, so it can be used directly for hybrid retrieval (dense ANN + sparse lexical matching) without re-encoding the corpus.

• Creator: Fernando Morales (ferMorales)
• Embedding Model: BAAI/bge-m3
• Language: Spanish
• License: Other

Summary

Property	Value
File	embeddings.parquet
Embedding Model	BAAI/bge-m3
Total Records (chunks)	207,888
Unique Issues (source_file)	5,628
Embedding Dimension (dense)	1024
Sparse Vectors	Variable-length (token id → weight)
Time Coverage	1954-08-23 to 2026-02-09

Schema (Columns)

Column	Type	Description
chunk_id	string	Unique chunk identifier (UUID5 derived from source)
doc_id	string	Document/issue identifier
chunk_index	int64	Chunk position within the source document
corpus	string	Corpus segment / decade family (gum00, gum10, …)
decade	string	Decade grouping derived from the document
issue_date	string	Issue date (YYYY-MM-DD); may be the literal "unknown-date"
source_pdf	string	Path to the original PDF
source_file	string	Path to the intermediate JSON file
text	string	Chunk text used for embedding
embedding	fixed_size_list<float32>[1024]	L2-normalized BGE-M3 dense vector
sparse_indices	list<uint32>	BGE-M3 lexical token IDs
sparse_values	list<float32>	BGE-M3 lexical token weights (parallel to sparse_indices)

The sparse representation comes directly from BGEM3FlagModel.encode(..., return_sparse=True)["lexical_weights"] — each chunk has a variable-length list of (token_id, weight) pairs split into two parallel columns.

Corpus Distribution

Corpus	Chunks
gum10	60,285
gum90	48,962
gum80	48,687
gum00	27,988
gum70	13,989
gum60	4,695
gum50	3,282

Coverage notes

• 623 chunks have issue_date = "unknown-date" (date could not be parsed from the source). Filter these out for time-based analyses.
• All other rows have non-null values for every schema field.
• Chunks are produced from OCR'd historical documents — minor noise may remain in text.
• All data for 2004 is missing due to upstream scraper issues.

Usage

Load with datasets / pandas

from datasets import load_dataset
ds = load_dataset("ferMorales/Gaceta_UNAM_BGE_M3_V2", split="train")
print(ds[0]["text"][:200])
print(len(ds[0]["embedding"]))           # 1024
print(len(ds[0]["sparse_indices"]))      # variable

import pandas as pd
df = pd.read_parquet("hf://datasets/ferMorales/Gaceta_UNAM_BGE_M3_V2/embeddings.parquet")

Hybrid retrieval (dense + sparse)

The sparse columns are stored as parallel list<uint32> / list<float32> arrays. To convert one row back into a {token_id: weight} dict:

def row_to_sparse(row):
    return dict(zip(row["sparse_indices"], row["sparse_values"]))

For Qdrant, you can ingest these directly into a collection that has both a dense Vector config (size 1024, cosine) and a SparseVectorParams named vector — pass indices=row["sparse_indices"] and values=row["sparse_values"].

Encoding queries with the same model

from FlagEmbedding import BGEM3FlagModel
model = BGEM3FlagModel("BAAI/bge-m3", use_fp16=True)
out = model.encode(["mi consulta"], return_dense=True, return_sparse=True)
dense_q = out["dense_vecs"][0]                  # shape (1024,)
sparse_q = out["lexical_weights"][0]            # {token_id: weight}

Recommended Use

1. Hybrid semantic search — combine dense cosine + sparse lexical scoring for improved recall on rare terms / proper nouns / OCR artifacts.
2. Historical RAG — retrieval with full source traceability via source_pdf, source_file, and chunk_index.
3. Metadata filtering — slice by corpus, decade, issue_date, or doc_id before re-ranking.

Citation

If you use this dataset, please cite both the embedding model and this release:

@misc{gaceta_unam_bgem3_v2,
  author = {Fernando Morales},
  title  = {Gaceta UNAM BGE-M3 V2 (dense + sparse)},
  year   = {2026},
  url    = {https://huggingface.co/datasets/ferMorales/Gaceta_UNAM_BGE_M3_V2},
}