ferMorales/Gaceta_UNAM_BGE_M3

Gaceta UNAM Embeddings (Parquet)

Dataset of semantic embeddings for text fragments (chunks) from Gaceta UNAM issues, in Parquet format, ready for vector indexing and RAG workflows. Generated with the BAAI/BGE-M3 model.

Summary

• File: embeddings.parquet
• Embedding model: BAAI/bge-m3
• Records (chunks): 170,424
• Unique documents (doc_id): 5,536
• Unique chunks (chunk_id): 170,424
• Embedding dimension: 1024
• Generated at UTC: 2026-02-27T09:24:57.375456+00:00
• Time coverage (issue_date, non-empty): from 1954-08-23 to 2026-02-09
• Empty issue_date: 481 records

Schema (columns)

• doc_id (string): document/issue identifier.
• chunk_id (string): unique chunk identifier.
• chunk_index (int64): chunk position within the document.
• corpus (string): corpus segment/family (e.g., gum10, gum80).
• decade (string): time grouping derived from the document.
• issue_date (string): issue date (expected format YYYY-MM-DD; may be empty in a few cases).
• page_start (int64): chunk start page.
• page_end (int64): chunk end page.
• source_pdf (string): source PDF path.
• source_file (string): source JSONL file path.
• embedding (list): normalized semantic vector (length 1024).
• text (string): chunk text used for embedding.

Quality and coverage

• All schema fields are present in every row (no null).
• issue_date has some empty values (481), so for temporal filtering it is recommended to exclude empty strings.
• Observed page range:
  • page_start: 1 to 126
  • page_end: 1 to 128

Corpus distribution (Decades)

• gum10: 46,317
• gum80: 41,576
• gum90: 41,088
• gum00: 21,283
• gum70: 12,655
• gum60: 4,438
• gum50: 3,067

Recommended use

This parquet is intended for:

1. Semantic search by vector similarity (embedding).
2. Historical RAG with source traceability (source_pdf, source_file, pages).
3. Metadata filtering (issue_date, corpus, doc_id).

Notes

• The text comes from OCR/historical document processing; minor noise may remain.
• Due to scraper issues, all data for the year 2004 was lost.

Shoutout to BAAI

https://huggingface.co/BAAI/bge-m3