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>
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name, kind, lang, domain, version, purity, signature, description, tags, uses_functions, uses_types, returns, returns_optional, error_type, imports, params, output, tested, tests, test_file_path, file_path, notes
| name | kind | lang | domain | version | purity | signature | description | tags | uses_functions | uses_types | returns | returns_optional | error_type | imports | params | output | tested | tests | test_file_path | file_path | notes | |||||||||||||||||||||||||
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| gliner2_load_model | function | py | datascience | 1.0.0 | impure | def gliner2_load_model(model_name: str = 'fastino/gliner2-large-v1', device: str = 'auto') -> Any | Carga (y cachea por (model_name, device)) un modelo GLiNER2 (NER+RE joint). GLiNER2 extrae entidades y relaciones en una sola pasada con schema unificado. ~2x mas rapido que GLiNER + GLiREL separados. LICENSE: Apache 2.0. |
|
false | error_go_core |
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Instancia GLiNER2 cacheada por (model_name, device). Tiene metodos .create_schema().entities(...).relations(...) y .extract(text, schema=schema, threshold=0.3). | true |
|
python/functions/datascience/tests/test_gliner2_load_model.py | python/functions/datascience/gliner2_load_model.py | LICENSE: fastino/gliner2-large-v1 es Apache 2.0 — uso comercial OK. Diferencia con gliner_load_model: GLiNER hace solo NER, GLiNER2 hace NER+RE en una sola pasada (joint schema). Para pipelines de grafo usar GLiNER2 cuando se necesiten ambas tareas simultaneamente. impure: descarga red/disco la primera vez, mantiene estado en _MODEL_CACHE. Tamanio: fastino/gliner2-large-v1 ~500 MB. Primera carga 15-30s en CPU. Inferencia CPU: 10-50 KB texto/s con schema tipico (3 entity + 8 relation labels). |
Ejemplo
from datascience.gliner2_load_model import gliner2_load_model
model = gliner2_load_model(device="auto")
schema = (model.create_schema()
.entities(["person", "organization", "location"])
.relations(["works_at", "ceo_of", "located_in"]))
result = model.extract(
"Pablo Isla es el CEO de Inditex, empresa con sede en Arteixo.",
schema=schema,
threshold=0.3,
)
# result["entities"] -> {"person": ["Pablo Isla"], "organization": ["Inditex"], ...}
# result["relation_extraction"] -> {"ceo_of": [("Pablo Isla", "Inditex")], ...}
Instalacion
cd python && uv pip install gliner2
# o con el extra NLP completo:
cd python && uv pip install -e '.[nlp]'