egutierrez
faac610745
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>
2.4 KiB
2.4 KiB
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 | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| rebel_load_model | function | py | datascience | 1.0.0 | impure | def rebel_load_model(model_name: str = 'Babelscape/rebel-large') -> tuple[Any, Any] | Carga (y cachea) el tokenizer y modelo REBEL (BART-based, ~1.5 GB). Solo ingles. Licencia Apache 2.0 — uso comercial permitido. Cache por model_name. |
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false | error_go_core |
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tupla (tokenizer, model) listos para inferencia, cacheados por model_name. | false | python/functions/datascience/rebel_load_model.py | LICENCIA: Apache 2.0 — uso comercial permitido (a diferencia de mREBEL que es CC BY-NC-SA). Solo funciona bien con texto en INGLES. Para espanol usar mrebel_load_model. REBEL usa el mismo wire format que mREBEL, por lo que parse_rebel_output es compatible. Diferencia vs mREBEL: no emite el prefijo tp_XX de idioma en el output (parse_rebel_output lo maneja porque ya hace .replace('tp_XX', '')). impure: descarga red/disco la primera vez, mantiene estado en _MODEL_CACHE. Cache separada de mrebel_load_model (modulo distinto). |
Ejemplo
from python.functions.datascience.rebel_load_model import rebel_load_model
from python.functions.datascience.parse_rebel_output import parse_rebel_output
tokenizer, model = rebel_load_model()
text = "Pablo Isla is the CEO of Inditex, based in Arteixo."
inputs = tokenizer(text, return_tensors="pt", max_length=512, truncation=True)
generated = model.generate(**inputs, num_beams=4, length_penalty=1.0, max_length=256)
decoded = tokenizer.decode(generated[0], skip_special_tokens=False)
triplets = parse_rebel_output(decoded)
Comparacion REBEL vs mREBEL
| REBEL | mREBEL | |
|---|---|---|
| Licencia | Apache 2.0 (comercial OK) | CC BY-NC-SA 4.0 (no comercial) |
| Idiomas | Solo ingles | 30+ (es_XX, en_XX, fr_XX...) |
| Tamanio | ~1.5 GB | ~2.4 GB (large) / ~900 MB (base) |
| Base | BART | mBART-50 |
Tamanio y latencia
Babelscape/rebel-large: ~1.5 GB en disco.- Primera carga: 20-60 s en CPU.
- Inferencia CPU: 3-10 s por frase (mas rapido que mREBEL por ser BART vs mBART).