egutierrez
dabc945eda
Extrae al registry funciones del proyecto interno footprint_aurgi: - core (6): slugify_ascii, normalize_for_join, cp_provincia_es, infer_provincia_from_cp, safe_read_csv_fallback, csv_to_parquet_duckdb - geo puras (7): haversine_km, point_in_ring, point_in_polygon, point_in_polygons_bbox, polygon_bbox, extent_with_padding, distance_bucket - geo I/O (4): load_geojson_polygons, load_boundary_gdf, add_basemap_osm, add_basemap_with_timeout - valhalla client (4): valhalla_route, valhalla_isochrone, valhalla_isochrones_async, valhalla_matrix_1_to_n - datascience stats (7): trimmed_mean, geometric_mean, detect_distribution_type, best_central_tendency, summary_stats, kde_density_levels, alpha_shape_concave_hull - datascience fuzzy (3): fuzzy_merge_adaptive (rapidfuzz), words_to_dataset, remove_words_from_column - datascience viz (2): plot_kde_2d, plot_heatmap_log - infra (4): compress_pdf_ghostscript, render_table_page_pdfpages, add_header_logo, osm2pgsql_ingest - pipelines (4): setup_geo_stack_docker, compute_centers_reachability, generate_isochrones_by_zone, count_points_per_zone - types geo (4): LonLat, BBox, IsochroneRequest, Centro Incluye: - apps/footprint_geo_stack/ (PostGIS + Martin + Valhalla via docker-compose) - 131/132 tests pasan (1 skip esperado: osm2pgsql en PATH) - Issue tracker dev/issues/0052-footprint-aurgi-extraction.md - Atribucion uniforme: source_repo internal:footprint_aurgi, source_license internal-aurgi - Build con 9 agentes en paralelo (8 wave 1 + 1 wave 2 pipelines) Tambien commitea trabajo previo no commiteado: aggregate_extraction_results, chunk_with_overlap, clean_pdf_text, merge_entity_aliases, extract_graph_gliner2, extract_relations_mrebel, extract_triples_spacy_es, gliner2/mrebel/marianmt/rebel/spacy_es load_model, parse_rebel_output, translate_es_to_en, issue 0050/0051. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
"""Carga (y cachea) el modelo mREBEL-base (variante rapida, 250M params)."""
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from __future__ import annotations
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from typing import Any
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from python.functions.datascience.mrebel_load_model import mrebel_load_model
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def mrebel_base_load_model(
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model_name: str = "Babelscape/mrebel-base",
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src_lang: str = "es_XX",
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tgt_lang: str = "tp_XX",
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) -> tuple[Any, Any]:
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"""Loads (and caches) the mREBEL-base tokenizer and model.
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Thin wrapper over ``mrebel_load_model`` with the base checkpoint as
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default (250M params, ~900 MB). Faster than the large variant at the
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cost of some recall on complex sentences.
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LICENSE NOTICE: Babelscape/mrebel-base is licensed under CC BY-NC-SA 4.0
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(Creative Commons Non-Commercial Share-Alike). Do NOT use in commercial
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products without replacing this model.
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Args:
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model_name: HuggingFace Hub model ID. Defaults to the base checkpoint.
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src_lang: Source language code for the mBART tokenizer.
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tgt_lang: Target language token for the decoder (always ``"tp_XX"``).
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Returns:
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Tuple ``(tokenizer, model)`` ready for inference.
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Raises:
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ImportError: if ``transformers`` is not installed.
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OSError: if the model cannot be downloaded or loaded from disk.
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"""
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return mrebel_load_model(
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model_name=model_name,
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src_lang=src_lang,
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tgt_lang=tgt_lang,
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)
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