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>
53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
"""Carga (y cachea) el modelo REBEL para extraccion de relaciones en ingles."""
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from __future__ import annotations
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from typing import Any
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# Cache global: model_name -> (tokenizer, model)
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_MODEL_CACHE: dict[str, tuple[Any, Any]] = {}
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def rebel_load_model(
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model_name: str = "Babelscape/rebel-large",
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) -> tuple[Any, Any]:
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"""Loads (and caches) the REBEL tokenizer and model. English only.
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REBEL is a BART-based seq2seq model (~1.5 GB) for relation extraction,
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trained on English Wikipedia (KELM). It extracts triplets (head, relation,
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tail) from English text.
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LICENSE: Apache 2.0 — commercial use permitted.
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The first call downloads the model from HuggingFace Hub (~1.5 GB).
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Subsequent calls with the same ``model_name`` return the cached instance.
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Args:
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model_name: HuggingFace Hub model ID. Default is the large variant.
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Returns:
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Tuple ``(tokenizer, model)`` both 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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cached = _MODEL_CACHE.get(model_name)
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if cached is not None:
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return cached
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try:
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
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except ImportError as exc:
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raise ImportError(
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"transformers no esta instalado. Instalalo con "
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"`uv pip install transformers` o `uv pip install -e '.[nlp]'`."
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) from exc
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
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model.eval()
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_MODEL_CACHE[model_name] = (tokenizer, model)
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return tokenizer, model
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