egutierrez
09f7f0ba1c
- glirel_load_model: cache por (model_name, device); device='auto' resuelve via torch - extract_relations_glirel: tokeniza por whitespace, mapea spans char->token, llama predict_relations y devuelve RelationCandidate; fallback text.find si la entidad llega sin offsets; max_pairs=N -> top-N por score - pyproject.toml: glirel en extra nlp Closes #0039 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
64 lines
1.9 KiB
Python
64 lines
1.9 KiB
Python
"""Carga (y cachea) un modelo GLiREL en el device deseado."""
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from __future__ import annotations
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from typing import Any
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# Cache global: (model_name, device) -> modelo cargado.
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_MODEL_CACHE: dict[tuple[str, str], Any] = {}
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def _resolve_device(device: str) -> str:
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"""Resuelve `device='auto'` a `cuda` o `cpu` segun disponibilidad."""
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if device != "auto":
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return device
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try:
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import torch
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except ImportError:
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return "cpu"
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return "cuda" if torch.cuda.is_available() else "cpu"
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def glirel_load_model(
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model_name: str = "jackboyla/glirel-large-v0",
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device: str = "auto",
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) -> Any:
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"""Carga un modelo GLiREL con cache por (model_name, device).
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La primera llamada descarga el modelo desde HuggingFace (~500 MB para
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`glirel-large-v0`). Llamadas sucesivas con los mismos parametros
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devuelven la instancia cacheada.
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Args:
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model_name: ID del modelo en HuggingFace Hub.
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device: 'auto' usa CUDA si esta disponible, o 'cpu'/'cuda'/'cuda:N'
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de forma explicita.
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Returns:
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Instancia del modelo GLiREL lista para `predict_relations`.
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Raises:
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ImportError: si la dependencia `glirel` no esta instalada.
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Solucion: `uv pip install glirel` o instalar el extra `nlp`
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del proyecto (`uv pip install -e '.[nlp]'`).
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"""
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resolved_device = _resolve_device(device)
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cache_key = (model_name, resolved_device)
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cached = _MODEL_CACHE.get(cache_key)
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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 glirel import GLiREL
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except ImportError as exc:
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raise ImportError(
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"glirel no esta instalado. Instalalo con "
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"`uv pip install glirel` o `uv pip install -e '.[nlp]'`."
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) from exc
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model = GLiREL.from_pretrained(model_name)
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if hasattr(model, "to"):
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model.to(resolved_device)
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_MODEL_CACHE[cache_key] = model
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return model
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