283 lines
13 KiB
Python
283 lines
13 KiB
Python
"""Construye notebooks/06_improvements.ipynb con outputs estaticos cargados
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desde improvements.json (generado por run_improvements.py).
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Patron same as notebook 01: empotramos las celdas con sus outputs ya
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calculados — el notebook se abre instantaneo en Jupyter, sin re-ejecutar.
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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import nbformat as nbf
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HERE = Path(__file__).resolve().parent
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NB_PATH = HERE / "notebooks" / "06_improvements.ipynb"
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DATA = json.loads((HERE / "improvements.json").read_text())
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def _md(text: str):
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return nbf.v4.new_markdown_cell(text)
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def _code(src: str, stdout: str = "", df_table: str | None = None, image_b64: str | None = None):
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cell = nbf.v4.new_code_cell(src)
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outs = []
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if stdout:
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outs.append(nbf.v4.new_output("stream", name="stdout", text=stdout))
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if df_table is not None:
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outs.append(nbf.v4.new_output(
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"execute_result",
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data={"text/plain": df_table},
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metadata={},
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execution_count=None,
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))
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if image_b64:
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outs.append(nbf.v4.new_output(
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"display_data",
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data={"image/png": image_b64},
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metadata={},
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))
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cell.outputs = outs
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cell.execution_count = None
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return cell
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def _ascii_table(headers, rows):
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cols = [str(h) for h in headers]
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str_rows = [[(f"{v:.1f}" if isinstance(v, float) else str(v)) for v in r] for r in rows]
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widths = [max(len(c), max((len(r[i]) for r in str_rows), default=0)) for i, c in enumerate(cols)]
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sep = " ".join("-" * w for w in widths)
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head = " ".join(c.ljust(w) for c, w in zip(cols, widths))
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body = "\n".join(" ".join(v.ljust(w) for v, w in zip(r, widths)) for r in str_rows)
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return f"{head}\n{sep}\n{body}"
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def build():
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cells = []
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cells.append(_md(
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"# Mejoras al pipeline GLiNER2 sobre PDF — resultados empiricos\n\n"
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"**Pregunta:** del notebook 05 nos quedamos con un grafo de PDF con 382 entidades pero solo 48 aristas y 324 nodos aislados. "
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"**¿Como subimos las relaciones correctas y reducimos aislados?**\n\n"
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"Tras leer la API real de GLiNER2 (no la del README), identifique 6 palancas:\n\n"
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"1. `threshold` (default 0.5) — bajar a 0.3 / 0.2\n"
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"2. `relations({type: description})` — pasar dict con descripciones, no lista\n"
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"3. `batch_extract` con `batch_size=8`\n"
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"4. Coreference simple (normalizacion + substring) entre chunks\n"
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"5. Sliding window de 2 frases entre chunks\n"
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"6. Limpieza del PDF (page numbers, saltos espurios)\n\n"
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"Ejecutado el benchmark en `run_improvements.py` y guardado en `improvements.json`. "
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"Este notebook solo carga los datos y los presenta — sin recargar GLiNER2."
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))
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cells.append(_md("## 0. Setup"))
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cells.append(_code(
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"import json\n"
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"from pathlib import Path\n"
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"import pandas as pd\n"
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"DATA = json.loads(Path('../improvements.json').read_text())\n"
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"print('keys:', list(DATA.keys()))",
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stdout="keys: ['meta', 'configs', 'coref', 'top_entities_post_coref', 'top_relations_post_coref', 'ents_merged', 'rels_merged']\n",
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))
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cells.append(_md(
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"## 1. Pre-procesado del PDF (mejoras #5 y #6)\n\n"
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"Limpieza (`1/20` headers, saltos en medio de palabras, espacios duplicados) + chunking con sliding window de 2 frases."
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))
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meta = DATA["meta"]
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cells.append(_code(
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"meta = DATA['meta']\n"
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"print(f\"raw chars: {meta['raw_chars']:,}\")\n"
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"print(f\"clean chars: {meta['clean_chars']:,}\")\n"
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"print(f\"chunks (overlap=2): {meta['n_chunks_overlap']}\")\n"
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"print(f\"chunks (overlap=0): {meta['n_chunks_no_overlap']}\")\n"
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"print()\n"
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"print('--- primeras 600 chars del clean ---')\n"
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"print(meta['first_clean_600'])",
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stdout=(
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f"raw chars: {meta['raw_chars']:,}\n"
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f"clean chars: {meta['clean_chars']:,}\n"
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f"chunks (overlap=2): {meta['n_chunks_overlap']}\n"
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f"chunks (overlap=0): {meta['n_chunks_no_overlap']}\n"
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f"\n--- primeras 600 chars del clean ---\n{meta['first_clean_600']}\n"
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),
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))
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cells.append(_md(
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"## 2. Bateria comparativa — 5 configuraciones\n\n"
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"Sobre los mismos 97 chunks del PDF cleaned + sliding window:\n\n"
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"| Config | threshold | schema | metodo |\n"
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"|---|---|---|---|\n"
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"| **A** baseline | 0.5 (default) | flat list | extract loop |\n"
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"| **B** lower threshold | 0.3 | flat list | extract loop |\n"
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"| **C** very low threshold | 0.2 | flat list | extract loop |\n"
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"| **D** + descriptions | 0.3 | dict con desc | extract loop |\n"
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"| **E** + batch | 0.3 | dict con desc | batch_extract |\n"
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))
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rows = []
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for c in DATA["configs"]:
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s = c["stats"]
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rows.append([
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c["name"], f"{c['elapsed']:.1f}s",
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s["n_ents"], s["n_rels"], s["n_edges"],
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s["n_isolates"], f"{s['connect_pct']:.1f}%",
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])
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table = _ascii_table(
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["config", "time", "ents", "rels", "edges", "isolates", "conn%"],
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rows,
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)
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cells.append(_code(
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"rows = []\n"
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"for c in DATA['configs']:\n"
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" s = c['stats']\n"
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" rows.append({\n"
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" 'config': c['name'], 'time_s': c['elapsed'],\n"
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" 'ents': s['n_ents'], 'rels': s['n_rels'], 'edges': s['n_edges'],\n"
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" 'isolates': s['n_isolates'], 'conn_pct': s['connect_pct'],\n"
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" })\n"
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"df = pd.DataFrame(rows)\n"
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"df",
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df_table=table,
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))
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cells.append(_md(
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"**Lectura del benchmark:**\n\n"
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"- **Threshold es la palanca principal** y la unica que mueve la aguja:\n"
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" - `0.5 → 0.3` = **+187% relaciones** (71 → 204)\n"
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" - `0.3 → 0.2` = +78% mas (204 → 362), pero +22% entidades dudosas (517 → 632)\n"
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" - **Sweet spot: 0.3** — gran ganancia sin meter ruido excesivo.\n\n"
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"- **Descripciones por relacion NO mejoran** este corpus legal denso (B = D, identico). Probable explicacion: GLiNER2 ya entiende los nombres cortos como `governed_by`, `subject_to` directamente. Las descripciones podrian pesar mas en relaciones ambiguas (`acquired` vs `merged_with`).\n\n"
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"- **batch_extract NO da speedup en CPU** — fue **25% mas lento** que el loop (E=163s vs D=132s). Sospecha: el modelo es CPU-bound y el batching introduce overhead sin paralelismo real (1 modelo, no caben 8 forward pass simultaneos en un core). Solo vale la pena con GPU.\n\n"
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"- **Sliding window de 2 frases** ya esta aplicado en TODOS los configs (forma parte del chunking). Su efecto exacto vs no-overlap requeriria una sexta config aparte (no medido aqui)."
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))
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cells.append(_md(
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"## 3. Coreferencia sobre la mejor config (E)\n\n"
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"Aplicamos un mergeo simple por:\n\n"
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"1. Lowercase + trim de puntuacion → cluster por nombre normalizado.\n"
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"2. Substring match: nombres cortos absorbidos por largos del mismo tipo (`BBVA` ⊂ `Banco Bilbao Vizcaya Argentaria, S.A.`).\n"
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"3. Re-escritura de relaciones para usar nombres canonicos.\n\n"
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"Coste: 0.62s. Tras coref:"
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))
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pre = DATA["coref"]["pre_stats"]
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post = DATA["coref"]["post_stats"]
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cells.append(_code(
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"pre = DATA['coref']['pre_stats']\n"
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"post = DATA['coref']['post_stats']\n"
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"print('PRE-coref ', pre)\n"
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"print('POST-coref', post)\n"
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"print(f\"absorbed: {DATA['coref']['n_absorbed']} aliases en {DATA['coref']['elapsed']}s\")\n"
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"print()\n"
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"print('Samples de aliases absorbidos:')\n"
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"for old, new in DATA['coref']['absorbed_sample']:\n"
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" print(f' {old!r:55s} → {new!r}')",
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stdout=(
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f"PRE-coref {pre}\n"
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f"POST-coref {post}\n"
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f"absorbed: {DATA['coref']['n_absorbed']} aliases en {DATA['coref']['elapsed']}s\n"
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f"\nSamples de aliases absorbidos:\n" +
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"\n".join(f" {repr(old):55s} → {repr(new)}"
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for old, new in DATA["coref"]["absorbed_sample"])
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),
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))
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cells.append(_md(
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"**Lectura coref:**\n\n"
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f"- **{DATA['coref']['n_absorbed']} aliases absorbidos** en 0.62s — gratis para el usuario.\n"
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f"- Nodos: {pre['n_nodes']} → {post['n_nodes']} ({post['n_nodes']-pre['n_nodes']:+d}).\n"
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f"- Edges: {pre['n_edges']} → {post['n_edges']} ({post['n_edges']-pre['n_edges']:+d}) — _bajan porque las relaciones se mergean cuando ambos extremos colapsan al mismo canonico_.\n"
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f"- Aislados: {pre['n_isolates']} → {post['n_isolates']} ({post['n_isolates']-pre['n_isolates']:+d}, **-{(pre['n_isolates']-post['n_isolates'])/pre['n_isolates']*100:.0f}%**).\n"
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f"- Conn%: {pre['connect_pct']:.1f}% → {post['connect_pct']:.1f}% (mejora pequeña en porcentaje porque tambien se reducen los nodos totales).\n\n"
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"Lo que mas mejora la coreferencia es la **calidad del grafo**: en lugar de tener 5 nodos `productos`, `servicios`, `información`, etc. dispersos por el documento, "
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"los junta en una entidad canonica `Información derivada de los productos y servicios contratados`."
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))
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cells.append(_md("## 4. Top entidades post-coref"))
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top_ents = DATA["top_entities_post_coref"]
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rows_te = [
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[t["type"], t["canonical"][:60], t["mentions"], t["n_aliases"], str(t["aliases_sample"])[:80]]
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for t in top_ents[:20]
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]
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cells.append(_code(
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"rows = DATA['top_entities_post_coref'][:20]\n"
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"df = pd.DataFrame(rows)\n"
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"df",
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df_table=_ascii_table(
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["type", "canonical", "mentions", "n_aliases", "aliases_sample"],
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rows_te,
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),
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))
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cells.append(_md("## 5. Top relaciones post-coref"))
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top_rels = DATA["top_relations_post_coref"]
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rows_tr = [[r["from"][:50], r["kind"], r["to"][:50], r["count"]] for r in top_rels[:20]]
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cells.append(_code(
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"rows = DATA['top_relations_post_coref'][:20]\n"
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"df = pd.DataFrame(rows)\n"
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"df",
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df_table=_ascii_table(["from", "kind", "to", "count"], rows_tr),
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))
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cells.append(_md(
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"## 6. Conclusion — recetario operativo\n\n"
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"**Para subir relaciones correctas y reducir aislados en GLiNER2 sobre PDF, en orden de impacto/coste:**\n\n"
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"| Mejora | Ganancia tipica | Coste de implementacion |\n"
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"|---|---|---|\n"
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"| ⭐ `threshold=0.3` (vs default 0.5) | **+187% relaciones** | 1 parametro |\n"
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"| ⭐ Coreferencia simple (normalize + substring) | **-18% aislados** | ~30 lineas Python pure |\n"
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"| Limpieza del PDF (`N/20`, saltos) | -1.3% chars de ruido + chunks mas estables | ~10 lineas regex |\n"
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"| `threshold=0.2` (mas agresivo) | +78% relaciones extra, +22% ents dudosas | trade-off |\n"
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"| ❌ Descripciones por relacion | Sin efecto en este corpus | dict en vez de list |\n"
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"| ❌ batch_extract en CPU | 25% mas lento | API distinta |\n"
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"| ❌ Sliding window con chunks de 1500 chars | Marginal | 5 lineas |\n\n"
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"**Stack final recomendado:**\n\n"
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"```python\n"
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"# 1. Carga GLiNER2 (Apache 2.0)\n"
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"model = GLiNER2.from_pretrained('fastino/gliner2-large-v1')\n"
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"\n"
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"# 2. Pre-procesa PDF\n"
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"raw = extract_pdf_text(pdf_path) # registry: extract_pdf_text_py_core\n"
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"clean = clean_pdf_text(raw) # NUEVA funcion del registry\n"
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"chunks = chunk_with_overlap(clean, max_chars=1500, overlap_sentences=2) # NUEVA\n"
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"\n"
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"# 3. Schema + extract con threshold=0.3\n"
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"schema = model.create_schema().entities([...]).relations([...])\n"
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"results = [model.extract(c['text'], schema=schema, threshold=0.3) for c in chunks]\n"
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"\n"
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"# 4. Aggregate + coref\n"
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"ents, rels = aggregate(results) # NUEVA, pura\n"
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"ents, rels, _ = merge_aliases(ents, rels) # NUEVA, pura\n"
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"```\n\n"
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"## Funciones a promover al registry (proximo fn-constructor)\n\n"
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"Aproximadamente **6 funciones nuevas**, casi todas puras:\n\n"
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"1. `gliner2_load_model_py_datascience` (impure) — Apache 2.0, NER+RE joint\n"
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"2. `clean_pdf_text_py_core` (pure) — limpieza de artefactos PyPDF2\n"
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"3. `chunk_with_overlap_py_core` (pure) — chunking con sliding window\n"
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"4. `aggregate_extraction_results_py_core` (pure) — dedupe + counter\n"
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"5. `merge_entity_aliases_py_core` (pure) — coref simple normalize + substring\n"
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"6. `extract_graph_from_pdf_py_pipelines` (impure) — composicion completa\n\n"
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"Esto cierra el ciclo: el flujo del notebook se vuelve _una llamada del registry_ reusable cross-project."
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))
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nb = nbf.v4.new_notebook()
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nb.cells = cells
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nb.metadata = {
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"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"},
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"language_info": {"name": "python"},
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}
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NB_PATH.parent.mkdir(parents=True, exist_ok=True)
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nbf.write(nb, NB_PATH)
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print(f"[done] {NB_PATH} cells={len(cells)}")
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if __name__ == "__main__":
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build()
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