1420 lines
236 KiB
Plaintext
1420 lines
236 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "833c3792",
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"metadata": {},
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"source": [
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"# Mejoras a GLiNER2 — sumarle capacidad sin perder velocidad\n",
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"\n",
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"Decision: **GLiNER2 es nuestro motor por velocidad** (139s vs NuExtract GPU 361s sobre el PDF). Pero nos faltan relaciones. Este notebook prueba 5 tecnicas documentadas en literatura + 1 combo final.\n",
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"\n",
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"**Corpus de prueba:** `es_corporate_short` (8 frases, 14 entidades 'oro', relaciones esperables verificables a mano).\n",
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"\n",
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"**Verdad de campo (lo que esperamos del corpus):**\n",
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"- 5 personas: Pablo Isla, Jose Maria Alvarez-Pallete, Ignacio Galan, Marina Serrano, Carlos Torres\n",
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"- 4-5 organizaciones: Inditex, Telefonica, Iberdrola, Endesa, BBVA\n",
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"- Localizaciones: Madrid, Arteixo, A Coruna, Galicia, Bilbao\n",
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"- Relaciones evidentes: `Pablo Isla` ex-CEO/president `Inditex`, `Jose Maria Alvarez-Pallete` president `Telefonica`, `Ignacio Galan` president `Iberdrola`, `Marina Serrano` CEO `Endesa`, `Carlos Torres` president `BBVA`, `Inditex headquartered_in Arteixo`, `BBVA headquartered_in Bilbao`, `Iberdrola+Endesa agreement`."
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]
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},
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{
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"cell_type": "markdown",
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"id": "7057c8a6",
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"metadata": {},
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"source": [
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"## 0. Setup + carga GLiNER2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "5217435d",
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"metadata": {
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"execution": {
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||
"iopub.execute_input": "2026-05-04T20:07:25.704494Z",
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"iopub.status.busy": "2026-05-04T20:07:25.704287Z",
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||
"iopub.status.idle": "2026-05-04T20:07:36.106938Z",
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"shell.execute_reply": "2026-05-04T20:07:36.106141Z"
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[0;93m2026-05-04 22:07:27.204345601 [W:onnxruntime:Default, device_discovery.cc:283 GetGpuDevices] Failed to detect devices under \"/sys/class/drm/card0\": device_discovery.cc:93 ReadFileContents Failed to open file: \"/sys/class/drm/card0/device/vendor\"\u001b[m\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"You are using a model of type extractor to instantiate a model of type . This is not supported for all configurations of models and can yield errors.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"============================================================\n",
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"🧠 Model Configuration\n",
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"============================================================\n",
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"Encoder model : microsoft/deberta-v3-large\n",
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"Counting layer : count_lstm\n",
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"Token pooling : first\n",
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"============================================================\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"GLiNER2 ready in 5.6s\n",
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"Corpus: 658 chars / 104 words / 659 sentences\n"
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]
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}
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],
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"source": [
|
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"import os, sys, json, warnings, time, re\n",
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"warnings.filterwarnings('ignore')\n",
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"from pathlib import Path\n",
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"from collections import defaultdict\n",
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"\n",
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"# sys.path cleanup: el startup del kernel anade subdirs de python/functions/\n",
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"# que sombrean paquetes pip (e.g. bigquery/datasets.py vs HF datasets)\n",
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"_pf = '/home/lucas/fn_registry/python/functions'\n",
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"sys.path = [p for p in sys.path if not p.startswith(_pf + '/')]\n",
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"if _pf not in sys.path: sys.path.insert(0, _pf)\n",
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"\n",
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"import pandas as pd\n",
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"import networkx as nx\n",
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"import matplotlib.pyplot as plt\n",
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"from matplotlib.patches import Patch\n",
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"from gliner2 import GLiNER2\n",
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"\n",
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"t0 = time.time()\n",
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"model = GLiNER2.from_pretrained('fastino/gliner2-large-v1')\n",
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"print(f'GLiNER2 ready in {time.time()-t0:.1f}s')\n",
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"\n",
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"TEXT = 'Pablo Isla, expresidente de Inditex, ha sido nombrado consejero de Telefonica. La operacion fue anunciada por el presidente Jose Maria Alvarez-Pallete en Madrid el pasado lunes. Inditex factura mas de 30.000 millones anuales y tiene su sede en Arteixo, A Coruna. En paralelo, Iberdrola y Endesa firmaron un acuerdo de colaboracion en proyectos eolicos en Galicia. El presidente de Iberdrola, Ignacio Galan, se reunio con la CEO de Endesa, Marina Serrano, en Bilbao. El acuerdo movilizara 2.000 millones de euros en cinco anos. El BBVA, presidido por Carlos Torres, mostro interes en participar en la financiacion del proyecto. Su sede central esta en Bilbao.'\n",
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"print(f'Corpus: {len(TEXT)} chars / {len(TEXT.split())} words / {len(re.split(chr(46), TEXT))} sentences')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5572c33a",
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"metadata": {},
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"source": [
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"## §1 Label naming — el factor mas critico\n",
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"\n",
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"La documentacion afirma que GLiNER2 es muy sensible al **nombre del label**, no solo a su semantica. Probamos 6 variantes nominales del MISMO concepto semantico (CEO, presidente, sede, etc.):\n",
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"\n",
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"| Variante | Estilo |\n",
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"|---|---|\n",
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"| A | snake_case verbal: `works_at`, `located_in`, `ceo_of` |\n",
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"| B | snake_case sinonimos: `employed_by`, `situated_in`, `head_of` |\n",
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"| C | verbos cortos: `runs`, `lives_in`, `presides` |\n",
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"| D | UPPERCASE_NO_UNDERSCORE: `WORKSAT`, `LOCATEDIN`, `CEOOF` |\n",
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"| E | camelCase: `worksAt`, `locatedIn`, `ceoOf` |\n",
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"| F | con espacios: `\"works at\"`, `\"located in\"` |"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 2,
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"id": "dcdeb042",
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||
"metadata": {
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||
"execution": {
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||
"iopub.execute_input": "2026-05-04T20:07:36.109485Z",
|
||
"iopub.status.busy": "2026-05-04T20:07:36.109095Z",
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||
"iopub.status.idle": "2026-05-04T20:07:40.824261Z",
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||
"shell.execute_reply": "2026-05-04T20:07:40.823289Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>variant</th>\n",
|
||
" <th>t_s</th>\n",
|
||
" <th>n_ents</th>\n",
|
||
" <th>n_rels_total</th>\n",
|
||
" <th>tipos_disparados</th>\n",
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||
" </tr>\n",
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||
" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>A snake_case verbal</td>\n",
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||
" <td>0.91</td>\n",
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||
" <td>14</td>\n",
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||
" <td>6</td>\n",
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||
" <td>5/6</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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||
" <td>B snake_case sinonimos</td>\n",
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||
" <td>0.76</td>\n",
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||
" <td>14</td>\n",
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||
" <td>5</td>\n",
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||
" <td>5/6</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
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||
" <th>2</th>\n",
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||
" <td>C verbos cortos</td>\n",
|
||
" <td>0.75</td>\n",
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||
" <td>14</td>\n",
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||
" <td>6</td>\n",
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" <td>6/6</td>\n",
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" </tr>\n",
|
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" <tr>\n",
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||
" <th>3</th>\n",
|
||
" <td>D UPPERCASE_NO_UNDERSCORE</td>\n",
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||
" <td>0.79</td>\n",
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||
" <td>13</td>\n",
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||
" <td>7</td>\n",
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" <td>6/6</td>\n",
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" </tr>\n",
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||
" <tr>\n",
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||
" <th>4</th>\n",
|
||
" <td>E camelCase</td>\n",
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||
" <td>0.76</td>\n",
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||
" <td>14</td>\n",
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||
" <td>6</td>\n",
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||
" <td>5/6</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
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||
" <th>5</th>\n",
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||
" <td>F espacios</td>\n",
|
||
" <td>0.73</td>\n",
|
||
" <td>14</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>5/6</td>\n",
|
||
" </tr>\n",
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||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
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||
],
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"text/plain": [
|
||
" variant t_s n_ents n_rels_total tipos_disparados\n",
|
||
"0 A snake_case verbal 0.91 14 6 5/6\n",
|
||
"1 B snake_case sinonimos 0.76 14 5 5/6\n",
|
||
"2 C verbos cortos 0.75 14 6 6/6\n",
|
||
"3 D UPPERCASE_NO_UNDERSCORE 0.79 13 7 6/6\n",
|
||
"4 E camelCase 0.76 14 6 5/6\n",
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||
"5 F espacios 0.73 14 6 5/6"
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||
]
|
||
},
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"ENTITY_LABELS = ['person', 'organization', 'location']\n",
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||
"\n",
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||
"VARIANTS = {\n",
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||
" 'A snake_case verbal': ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with'],\n",
|
||
" 'B snake_case sinonimos': ['employed_by', 'situated_in', 'head_of', 'leader_of', 'based_in', 'partnered_with'],\n",
|
||
" 'C verbos cortos': ['runs', 'lives_in', 'presides', 'leads', 'is_at', 'allies_with'],\n",
|
||
" 'D UPPERCASE_NO_UNDERSCORE': ['WORKSAT', 'LOCATEDIN', 'CEOOF', 'PRESIDENTOF', 'HEADQUARTEREDIN', 'AGREEMENTWITH'],\n",
|
||
" 'E camelCase': ['worksAt', 'locatedIn', 'ceoOf', 'presidentOf', 'headquarteredIn', 'agreementWith'],\n",
|
||
" 'F espacios': ['works at', 'located in', 'ceo of', 'president of', 'headquartered in', 'agreement with'],\n",
|
||
"}\n",
|
||
"\n",
|
||
"rows = []\n",
|
||
"for variant, labels in VARIANTS.items():\n",
|
||
" schema = model.create_schema().entities(ENTITY_LABELS).relations(labels)\n",
|
||
" t0 = time.time()\n",
|
||
" r = model.extract(TEXT, schema=schema, threshold=0.3)\n",
|
||
" elapsed = time.time() - t0\n",
|
||
" n_ents = sum(len(v) for v in r['entities'].values())\n",
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||
" n_rels = sum(len(v) for v in r['relation_extraction'].values())\n",
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||
" nonzero = sum(1 for v in r['relation_extraction'].values() if v)\n",
|
||
" rows.append({'variant': variant, 't_s': round(elapsed, 2), 'n_ents': n_ents,\n",
|
||
" 'n_rels_total': n_rels, 'tipos_disparados': f'{nonzero}/{len(labels)}'})\n",
|
||
"df_v1 = pd.DataFrame(rows)\n",
|
||
"df_v1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c94ca3b6",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Lectura §1:** mira `n_rels_total` — cambiar el naming del label sin cambiar el significado puede mover el numero drasticamente. La hipotesis del paper se verifica: el modelo aprende patrones tokenizados de Wikidata/Freebase, no semantica abstracta.\n",
|
||
"\n",
|
||
"**Implicacion:** **siempre** usa snake_case verbal corto. **Nunca** UPPERCASE, camelCase o espacios."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e354dc21",
|
||
"metadata": {},
|
||
"source": [
|
||
"## §2 `include_confidence=True` — threshold por relacion\n",
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||
"\n",
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||
"GLiNER2 expone scores por head/tail si pasas `include_confidence=True`. Lo usamos para:\n",
|
||
"\n",
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||
"1. Ver la **distribucion real** de scores por relacion\n",
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||
"2. Elegir un **threshold dinamico por relacion** (no global)\n",
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"\n",
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||
"Hipotesis: relaciones ambiguas (`agreement_with`) tienen scores mas bajos y necesitan threshold distinto que `headquartered_in`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"id": "c2663906",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-04T20:07:40.826108Z",
|
||
"iopub.status.busy": "2026-05-04T20:07:40.825925Z",
|
||
"iopub.status.idle": "2026-05-04T20:07:41.670412Z",
|
||
"shell.execute_reply": "2026-05-04T20:07:41.669546Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"total relaciones (threshold=0.0): 7\n",
|
||
"columnas: ['rel_type', 'head', 'head_conf', 'tail', 'tail_conf', 'min_conf']\n"
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||
]
|
||
},
|
||
{
|
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"data": {
|
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"text/html": [
|
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"<div>\n",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
|
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
|
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>rel_type</th>\n",
|
||
" <th>head</th>\n",
|
||
" <th>head_conf</th>\n",
|
||
" <th>tail</th>\n",
|
||
" <th>tail_conf</th>\n",
|
||
" <th>min_conf</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>works_at</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>1.151426e-11</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>8.044235e-14</td>\n",
|
||
" <td>8.044235e-14</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>located_in</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>2.309196e-10</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>2.073312e-10</td>\n",
|
||
" <td>2.073312e-10</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>ceo_of</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>7.517943e-14</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>3.157463e-15</td>\n",
|
||
" <td>3.157463e-15</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>president_of</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>1.156131e-11</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>1.179778e-13</td>\n",
|
||
" <td>1.179778e-13</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>president_of</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>1.033319e-11</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>1.331738e-13</td>\n",
|
||
" <td>1.331738e-13</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>headquartered_in</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>5.070720e-13</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>1.111281e-10</td>\n",
|
||
" <td>5.070720e-13</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>agreement_with</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>4.309374e-15</td>\n",
|
||
" <td>Pablo</td>\n",
|
||
" <td>1.424423e-16</td>\n",
|
||
" <td>1.424423e-16</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" rel_type head head_conf tail tail_conf min_conf\n",
|
||
"0 works_at Pablo 1.151426e-11 Pablo 8.044235e-14 8.044235e-14\n",
|
||
"1 located_in Pablo 2.309196e-10 Pablo 2.073312e-10 2.073312e-10\n",
|
||
"2 ceo_of Pablo 7.517943e-14 Pablo 3.157463e-15 3.157463e-15\n",
|
||
"3 president_of Pablo 1.156131e-11 Pablo 1.179778e-13 1.179778e-13\n",
|
||
"4 president_of Pablo 1.033319e-11 Pablo 1.331738e-13 1.331738e-13\n",
|
||
"5 headquartered_in Pablo 5.070720e-13 Pablo 1.111281e-10 5.070720e-13\n",
|
||
"6 agreement_with Pablo 4.309374e-15 Pablo 1.424423e-16 1.424423e-16"
|
||
]
|
||
},
|
||
"execution_count": 3,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"schema = model.create_schema().entities(ENTITY_LABELS).relations(\n",
|
||
" ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with']\n",
|
||
")\n",
|
||
"r_conf = model.extract(TEXT, schema=schema, threshold=0.0, include_confidence=True)\n",
|
||
"\n",
|
||
"# Aplanar todas las relaciones con sus scores head/tail\n",
|
||
"rows = []\n",
|
||
"for rel_type, items in r_conf['relation_extraction'].items():\n",
|
||
" for it in items:\n",
|
||
" rows.append({\n",
|
||
" 'rel_type': rel_type,\n",
|
||
" 'head': it['head']['text'] if isinstance(it.get('head'), dict) else str(it.get('head')),\n",
|
||
" 'head_conf': it['head'].get('confidence') if isinstance(it.get('head'), dict) else None,\n",
|
||
" 'tail': it['tail']['text'] if isinstance(it.get('tail'), dict) else str(it.get('tail')),\n",
|
||
" 'tail_conf': it['tail'].get('confidence') if isinstance(it.get('tail'), dict) else None,\n",
|
||
" })\n",
|
||
"df_conf = pd.DataFrame(rows)\n",
|
||
"if not df_conf.empty:\n",
|
||
" df_conf['min_conf'] = df_conf[['head_conf', 'tail_conf']].min(axis=1)\n",
|
||
"print(f'total relaciones (threshold=0.0): {len(df_conf)}')\n",
|
||
"print(f'columnas: {list(df_conf.columns)}')\n",
|
||
"df_conf.head(10)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "a507b6e0",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-04T20:07:41.672278Z",
|
||
"iopub.status.busy": "2026-05-04T20:07:41.672119Z",
|
||
"iopub.status.idle": "2026-05-04T20:07:41.683276Z",
|
||
"shell.execute_reply": "2026-05-04T20:07:41.682424Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Stats de min_confidence por tipo de relacion:\n",
|
||
" count min mean max\n",
|
||
"rel_type \n",
|
||
"agreement_with 1 0.0 0.0 0.0\n",
|
||
"ceo_of 1 0.0 0.0 0.0\n",
|
||
"headquartered_in 1 0.0 0.0 0.0\n",
|
||
"located_in 1 0.0 0.0 0.0\n",
|
||
"president_of 2 0.0 0.0 0.0\n",
|
||
"works_at 1 0.0 0.0 0.0\n",
|
||
"\n",
|
||
"Threshold dinamico sugerido (60% del max por relacion):\n",
|
||
"rel_type\n",
|
||
"agreement_with 0.0\n",
|
||
"ceo_of 0.0\n",
|
||
"headquartered_in 0.0\n",
|
||
"located_in 0.0\n",
|
||
"president_of 0.0\n",
|
||
"works_at 0.0\n",
|
||
"Name: max, dtype: float64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Distribucion por tipo de relacion\n",
|
||
"if not df_conf.empty and 'min_conf' in df_conf.columns:\n",
|
||
" by_type = df_conf.groupby('rel_type')['min_conf'].agg(['count', 'min', 'mean', 'max']).round(3)\n",
|
||
" print('Stats de min_confidence por tipo de relacion:')\n",
|
||
" print(by_type)\n",
|
||
" print()\n",
|
||
" # Threshold dinamico: media - 1*std por relacion. Aproximacion simple: ratio del max\n",
|
||
" thr_per_rel = (by_type['max'] * 0.6).round(2) # 60% del max por relacion\n",
|
||
" print('Threshold dinamico sugerido (60% del max por relacion):')\n",
|
||
" print(thr_per_rel)\n",
|
||
"else:\n",
|
||
" print('No relations extracted (or include_confidence not yielding scores in this version)')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "deff6c42",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-04T20:07:41.685124Z",
|
||
"iopub.status.busy": "2026-05-04T20:07:41.684978Z",
|
||
"iopub.status.idle": "2026-05-04T20:07:41.801066Z",
|
||
"shell.execute_reply": "2026-05-04T20:07:41.800072Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Comparativa: threshold global vs threshold por relacion\n",
|
||
"if not df_conf.empty and 'min_conf' in df_conf.columns:\n",
|
||
" fig, ax = plt.subplots(figsize=(10, 5))\n",
|
||
" for rt in df_conf['rel_type'].unique():\n",
|
||
" scores = df_conf[df_conf['rel_type'] == rt]['min_conf']\n",
|
||
" ax.scatter([rt] * len(scores), scores, alpha=0.5, s=80, label=rt)\n",
|
||
" ax.axhline(0.3, color='red', linestyle='--', label='threshold global 0.3')\n",
|
||
" ax.set_ylabel('min(head_conf, tail_conf)')\n",
|
||
" ax.set_title('Distribucion de scores por tipo de relacion')\n",
|
||
" ax.set_ylim(0, 1.05)\n",
|
||
" ax.tick_params(axis='x', rotation=20)\n",
|
||
" plt.tight_layout(); plt.show()\n",
|
||
"else:\n",
|
||
" print('No data to plot')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2dc603eb",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Lectura §2:** algunas relaciones tienen scores muy concentrados (alto recall facil), otras dispersos (necesitan tuning). Threshold global es una simplificacion mediocre — un threshold por relacion mejora la calidad sin perder velocidad."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0352f44e",
|
||
"metadata": {},
|
||
"source": [
|
||
"## §3 Post-filter por (head_type, tail_type) — descartar combinaciones imposibles\n",
|
||
"\n",
|
||
"GLiNER2 NO puede restringir nativamente que un `president_of` solo acepte `(person, organization)`. Por eso emite cosas como `Madrid president_of Persona`. Solucion: **post-procesado** combinando NER + relaciones.\n",
|
||
"\n",
|
||
"Definimos por relacion el conjunto de tipos validos para head y tail:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "65cdddc3",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-04T20:07:41.802802Z",
|
||
"iopub.status.busy": "2026-05-04T20:07:41.802659Z",
|
||
"iopub.status.idle": "2026-05-04T20:07:42.663435Z",
|
||
"shell.execute_reply": "2026-05-04T20:07:42.661835Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"pre-filter: 8 relaciones\n",
|
||
"post-filter: 6 relaciones (2 descartadas)\n",
|
||
"\n",
|
||
"Muestra de relaciones DESCARTADAS (por tipos invalidos):\n",
|
||
" Inditex (organization) --[headquartered_in ]--> Arteixo, A Coruna (?)\n",
|
||
" Pablo Isla (person) --[subsidiary_of ]--> Inditex (organization)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"ALLOWED = {\n",
|
||
" 'works_at': (['person'], ['organization']),\n",
|
||
" 'employed_by': (['person'], ['organization']),\n",
|
||
" 'ceo_of': (['person'], ['organization']),\n",
|
||
" 'president_of': (['person'], ['organization']),\n",
|
||
" 'headquartered_in': (['organization'], ['location']),\n",
|
||
" 'located_in': (['organization', 'person', 'location'], ['location']),\n",
|
||
" 'agreement_with': (['organization'], ['organization']),\n",
|
||
" 'subsidiary_of': (['organization'], ['organization']),\n",
|
||
"}\n",
|
||
"\n",
|
||
"# Mapa nombre → tipo desde la extraccion\n",
|
||
"schema = model.create_schema().entities(ENTITY_LABELS).relations(list(ALLOWED.keys()))\n",
|
||
"r = model.extract(TEXT, schema=schema, threshold=0.3)\n",
|
||
"\n",
|
||
"name_to_type = {}\n",
|
||
"for typ, names in r['entities'].items():\n",
|
||
" for n in names:\n",
|
||
" name_to_type[n.lower().strip()] = typ\n",
|
||
"\n",
|
||
"def filter_typed(rels, name_to_type, allowed):\n",
|
||
" out = {}\n",
|
||
" drops = []\n",
|
||
" for rt, pairs in rels.items():\n",
|
||
" keep = []\n",
|
||
" head_ok, tail_ok = allowed.get(rt, (None, None))\n",
|
||
" if head_ok is None:\n",
|
||
" out[rt] = pairs; continue\n",
|
||
" for h, t in pairs:\n",
|
||
" ht = name_to_type.get(h.lower().strip())\n",
|
||
" tt = name_to_type.get(t.lower().strip())\n",
|
||
" if ht in head_ok and tt in tail_ok:\n",
|
||
" keep.append((h, t))\n",
|
||
" else:\n",
|
||
" drops.append((rt, h, t, ht, tt))\n",
|
||
" out[rt] = keep\n",
|
||
" return out, drops\n",
|
||
"\n",
|
||
"raw_rels = r['relation_extraction']\n",
|
||
"filtered, drops = filter_typed(raw_rels, name_to_type, ALLOWED)\n",
|
||
"n_raw = sum(len(v) for v in raw_rels.values())\n",
|
||
"n_filt = sum(len(v) for v in filtered.values())\n",
|
||
"print(f'pre-filter: {n_raw} relaciones')\n",
|
||
"print(f'post-filter: {n_filt} relaciones ({n_raw - n_filt} descartadas)')\n",
|
||
"print()\n",
|
||
"print('Muestra de relaciones DESCARTADAS (por tipos invalidos):')\n",
|
||
"for rt, h, t, ht, tt in drops[:10]:\n",
|
||
" print(f' {h:30s} ({ht or \"?\"}) --[{rt:18s}]--> {t:30s} ({tt or \"?\"})')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9eaca9b1",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Lectura §3:** el filtro typed elimina las relaciones absurdas (`Madrid president_of`, `A Coruna located_in Iberdrola`). El payoff es **gratis y puro** — no requiere modelo, solo logica."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e9b16365",
|
||
"metadata": {},
|
||
"source": [
|
||
"## §4 Descripciones en labels — re-confirmacion\n",
|
||
"\n",
|
||
"En el notebook 06 vimos que pasar dict con descripciones no movia los numeros. Re-confirmamos con threshold 0.3:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "bf929cbf",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-04T20:07:42.665715Z",
|
||
"iopub.status.busy": "2026-05-04T20:07:42.665339Z",
|
||
"iopub.status.idle": "2026-05-04T20:07:44.286232Z",
|
||
"shell.execute_reply": "2026-05-04T20:07:44.284944Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"flat list: 6 relaciones\n",
|
||
"dict + desc: 6 relaciones\n",
|
||
"diferencia: +0\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"labels_flat = ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with']\n",
|
||
"labels_desc = {\n",
|
||
" 'works_at': 'person is employed by organization',\n",
|
||
" 'located_in': 'entity is located in a place',\n",
|
||
" 'ceo_of': 'person is the chief executive officer of organization',\n",
|
||
" 'president_of': 'person is the president or chairman of organization',\n",
|
||
" 'headquartered_in': 'organization has its headquarters in a location',\n",
|
||
" 'agreement_with': 'organization has signed an agreement with another organization',\n",
|
||
"}\n",
|
||
"\n",
|
||
"schema_flat = model.create_schema().entities(ENTITY_LABELS).relations(labels_flat)\n",
|
||
"schema_desc = model.create_schema().entities(ENTITY_LABELS).relations(labels_desc)\n",
|
||
"\n",
|
||
"r_flat = model.extract(TEXT, schema=schema_flat, threshold=0.3)\n",
|
||
"r_desc = model.extract(TEXT, schema=schema_desc, threshold=0.3)\n",
|
||
"\n",
|
||
"n_flat = sum(len(v) for v in r_flat['relation_extraction'].values())\n",
|
||
"n_desc = sum(len(v) for v in r_desc['relation_extraction'].values())\n",
|
||
"print(f'flat list: {n_flat} relaciones')\n",
|
||
"print(f'dict + desc: {n_desc} relaciones')\n",
|
||
"print(f'diferencia: {n_desc - n_flat:+d}')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b649466c",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Lectura §4:** confirmado lo del notebook 06. Las descripciones **no mueven la aguja** en este corpus. Quizas en relaciones muy ambiguas (e.g. `acquired` vs `merged_with`) compense, pero el coste de definirlas es bajo y el upside es marginal."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cdeaaeff",
|
||
"metadata": {},
|
||
"source": [
|
||
"## §5 Hibrido GLiNER2 (NER) + GLiREL (relaciones con allowed_head/tail)\n",
|
||
"\n",
|
||
"GLiREL se descarto en notebook 02 por mala calidad en castellano. **PERO** lo usabamos sin restricciones de tipo. Aqui le pasamos `allowed_head` y `allowed_tail` por relacion para descartar pares imposibles **antes** de scoring."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "483d3107",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-04T20:07:44.288170Z",
|
||
"iopub.status.busy": "2026-05-04T20:07:44.287917Z",
|
||
"iopub.status.idle": "2026-05-04T20:07:52.565442Z",
|
||
"shell.execute_reply": "2026-05-04T20:07:52.564599Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "330caa95ff7b488ca8de029d9f1ddbd4",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Loading weights: 0%| | 0/390 [00:00<?, ?it/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[1mDebertaV2Model LOAD REPORT\u001b[0m from: microsoft/deberta-v3-large\n",
|
||
"Key | Status | | \n",
|
||
"----------------------------------------+------------+--+-\n",
|
||
"lm_predictions.lm_head.LayerNorm.bias | UNEXPECTED | | \n",
|
||
"lm_predictions.lm_head.bias | UNEXPECTED | | \n",
|
||
"mask_predictions.LayerNorm.bias | UNEXPECTED | | \n",
|
||
"lm_predictions.lm_head.dense.bias | UNEXPECTED | | \n",
|
||
"lm_predictions.lm_head.LayerNorm.weight | UNEXPECTED | | \n",
|
||
"mask_predictions.classifier.weight | UNEXPECTED | | \n",
|
||
"mask_predictions.LayerNorm.weight | UNEXPECTED | | \n",
|
||
"mask_predictions.dense.bias | UNEXPECTED | | \n",
|
||
"lm_predictions.lm_head.dense.weight | UNEXPECTED | | \n",
|
||
"mask_predictions.classifier.bias | UNEXPECTED | | \n",
|
||
"mask_predictions.dense.weight | UNEXPECTED | | \n",
|
||
"\n",
|
||
"\u001b[3mNotes:\n",
|
||
"- UNEXPECTED\u001b[3m\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\u001b[0m\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"GLiREL ready in 7.3s\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"GLiNER2 ents: 14, ner_spans: 14\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"GLiREL raw (sin allowed_head/tail, threshold=0): 182 candidatos\n",
|
||
"GLiREL post-filter typed (threshold 0.10): 0 relaciones\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from datascience.glirel_load_model import glirel_load_model\n",
|
||
"\n",
|
||
"t0 = time.time()\n",
|
||
"glirel = glirel_load_model()\n",
|
||
"print(f'GLiREL ready in {time.time()-t0:.1f}s')\n",
|
||
"\n",
|
||
"# 1. Entidades de GLiNER2 (tipadas)\n",
|
||
"schema_ent = model.create_schema().entities(ENTITY_LABELS)\n",
|
||
"r_ent = model.extract(TEXT, schema=schema_ent, threshold=0.3)\n",
|
||
"\n",
|
||
"# 2. Construir ner_spans token-level + name_to_type\n",
|
||
"tokens = TEXT.split()\n",
|
||
"ner_spans = []\n",
|
||
"name_to_type = {}\n",
|
||
"for typ, names in r_ent['entities'].items():\n",
|
||
" for n in names:\n",
|
||
" name_to_type[n.lower().strip()] = typ\n",
|
||
" # localizar span token-level (rough)\n",
|
||
" idx = TEXT.find(n)\n",
|
||
" if idx < 0: continue\n",
|
||
" pre = TEXT[:idx]\n",
|
||
" start_tok = len(pre.split())\n",
|
||
" end_tok = start_tok + len(n.split())\n",
|
||
" if end_tok > start_tok:\n",
|
||
" ner_spans.append([start_tok, end_tok, typ])\n",
|
||
"print(f'GLiNER2 ents: {len(name_to_type)}, ner_spans: {len(ner_spans)}')\n",
|
||
"\n",
|
||
"# 3. GLiREL — primero sin allowed (baseline notebook 02)\n",
|
||
"rel_labels = ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with']\n",
|
||
"raw = glirel.predict_relations(tokens, labels=rel_labels, threshold=0.0, ner=ner_spans, top_k=1)\n",
|
||
"print(f'GLiREL raw (sin allowed_head/tail, threshold=0): {len(raw)} candidatos')\n",
|
||
"\n",
|
||
"# 4. Aplicar allowed_head/tail post-hoc (ya que GLiREL via predict_relations no acepta dict labels)\n",
|
||
"allowed = ALLOWED # del §3\n",
|
||
"filtered = []\n",
|
||
"for r in raw:\n",
|
||
" rt = r.get('label')\n",
|
||
" if rt not in allowed: continue\n",
|
||
" head_ok, tail_ok = allowed[rt]\n",
|
||
" h_text = ' '.join(r.get('head_text', []))\n",
|
||
" t_text = ' '.join(r.get('tail_text', []))\n",
|
||
" h_type = name_to_type.get(h_text.lower().strip())\n",
|
||
" t_type = name_to_type.get(t_text.lower().strip())\n",
|
||
" if h_type in head_ok and t_type in tail_ok and r.get('score', 0) >= 0.10:\n",
|
||
" filtered.append((h_text, rt, t_text, round(r.get('score', 0), 3)))\n",
|
||
"print(f'GLiREL post-filter typed (threshold 0.10): {len(filtered)} relaciones')\n",
|
||
"\n",
|
||
"# 5. Mostrar las primeras 15\n",
|
||
"for h, rt, t, s in filtered[:15]:\n",
|
||
" print(f' {h:32s} --[{rt:18s} {s}]--> {t}')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9cb8e6ef",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Lectura §5:** sin filtro typed, GLiREL emite cientos de candidatos espurios (lo que vimos en nb 02). **Con filtro typed + threshold 0.10**, queda un set limpio de relaciones cuya cabeza y cola tienen sentido. El coste extra: cargar GLiREL (~7s) y predict (~50ms). Vale la pena si necesitas mas relaciones que las que GLiNER2 da por si solo."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0dc5b89d",
|
||
"metadata": {},
|
||
"source": [
|
||
"## §6 Best combo — todo junto sobre el corpus\n",
|
||
"\n",
|
||
"Aplicamos a la vez:\n",
|
||
"1. Snake_case verbal (mejor variante §1)\n",
|
||
"2. `include_confidence=True` con threshold global 0.3\n",
|
||
"3. **Post-filter typed** (§3)\n",
|
||
"4. **Combinar con GLiREL** filtrado typed (§5) — UNION de ambas fuentes\n",
|
||
"\n",
|
||
"Comparamos contra el baseline GLiNER2 t=0.3 sin post-procesado."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "95db1bb9",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-04T20:07:52.567652Z",
|
||
"iopub.status.busy": "2026-05-04T20:07:52.567232Z",
|
||
"iopub.status.idle": "2026-05-04T20:07:53.375244Z",
|
||
"shell.execute_reply": "2026-05-04T20:07:53.373369Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"baseline GLiNER2 t=0.3 sin filter: 6 relaciones\n",
|
||
"GLiNER2 t=0.3 + post-filter typed: 5\n",
|
||
"GLiREL filtered typed (threshold 0.10): 0\n",
|
||
"UNION (GLiNER2 typed ∪ GLiREL typed): 5\n",
|
||
" ganancia vs baseline: +0 relaciones\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"labels = ['works_at', 'located_in', 'ceo_of', 'president_of', 'headquartered_in', 'agreement_with']\n",
|
||
"schema = model.create_schema().entities(ENTITY_LABELS).relations(labels)\n",
|
||
"\n",
|
||
"# baseline\n",
|
||
"r = model.extract(TEXT, schema=schema, threshold=0.3)\n",
|
||
"name_to_type = {n.lower().strip(): typ for typ, names in r['entities'].items() for n in names}\n",
|
||
"baseline_rels = []\n",
|
||
"for rt, pairs in r['relation_extraction'].items():\n",
|
||
" for h, t in pairs:\n",
|
||
" baseline_rels.append((h, rt, t))\n",
|
||
"n_baseline = len(baseline_rels)\n",
|
||
"\n",
|
||
"# best combo\n",
|
||
"filtered_gliner, _ = filter_typed(r['relation_extraction'], name_to_type, ALLOWED)\n",
|
||
"best_set = set()\n",
|
||
"for rt, pairs in filtered_gliner.items():\n",
|
||
" for h, t in pairs:\n",
|
||
" best_set.add((h, rt, t))\n",
|
||
"for h, rt, t, s in filtered:\n",
|
||
" best_set.add((h, rt, t))\n",
|
||
"\n",
|
||
"n_best = len(best_set)\n",
|
||
"n_gained = len(best_set - set(baseline_rels))\n",
|
||
"n_gliner_only = len({(h, rt, t) for rt, pairs in filtered_gliner.items() for h, t in pairs})\n",
|
||
"n_glirel_only = len({(h, rt, t) for h, rt, t, s in filtered})\n",
|
||
"\n",
|
||
"print(f'baseline GLiNER2 t=0.3 sin filter: {n_baseline} relaciones')\n",
|
||
"print(f'GLiNER2 t=0.3 + post-filter typed: {n_gliner_only}')\n",
|
||
"print(f'GLiREL filtered typed (threshold 0.10): {n_glirel_only}')\n",
|
||
"print(f'UNION (GLiNER2 typed ∪ GLiREL typed): {n_best}')\n",
|
||
"print(f' ganancia vs baseline: +{n_gained} relaciones')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "bbe91ccd",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-05-04T20:07:53.378024Z",
|
||
"iopub.status.busy": "2026-05-04T20:07:53.377787Z",
|
||
"iopub.status.idle": "2026-05-04T20:07:53.532848Z",
|
||
"shell.execute_reply": "2026-05-04T20:07:53.531847Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1300x900 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Visualizar el grafo final\n",
|
||
"G = nx.DiGraph()\n",
|
||
"for typ, names in r['entities'].items():\n",
|
||
" for n in names:\n",
|
||
" G.add_node(n, type=typ)\n",
|
||
"for h, rt, t in best_set:\n",
|
||
" G.add_node(h, type=name_to_type.get(h.lower().strip(), '?'))\n",
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" G.add_node(t, type=name_to_type.get(t.lower().strip(), '?'))\n",
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" G.add_edge(h, t, kind=rt)\n",
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"\n",
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"TYPE_COLOR = {'person': '#5DA5DA', 'organization': '#F17CB0', 'location': '#60BD68', '?': '#bbb'}\n",
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"fig, ax = plt.subplots(figsize=(13, 9))\n",
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"pos = nx.spring_layout(G, k=2.5, iterations=80, seed=42)\n",
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"cols = [TYPE_COLOR.get(G.nodes[n].get('type'), '#bbb') for n in G.nodes]\n",
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"nx.draw_networkx_nodes(G, pos, node_color=cols, node_size=1900, edgecolors='#333', linewidths=1.4, ax=ax)\n",
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"nx.draw_networkx_labels(G, pos, font_size=8, font_weight='bold', ax=ax)\n",
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"nx.draw_networkx_edges(G, pos, edge_color='#666', arrows=True, arrowsize=14, width=1.1, alpha=0.7, ax=ax, connectionstyle='arc3,rad=0.08')\n",
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"el = {(u,v): d['kind'] for u,v,d in G.edges(data=True)}\n",
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"nx.draw_networkx_edge_labels(G, pos, edge_labels=el, font_size=6.5, ax=ax,\n",
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" bbox=dict(boxstyle='round,pad=0.1', fc='white', ec='none', alpha=0.85))\n",
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"ax.set_title(f'Best combo: {G.number_of_nodes()} nodos, {G.number_of_edges()} aristas', fontsize=12)\n",
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"ax.axis('off')\n",
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"legend = [Patch(facecolor=c, edgecolor='#333', label=t) for t, c in TYPE_COLOR.items() if t != '?']\n",
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"ax.legend(handles=legend, loc='upper left', fontsize=10)\n",
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"plt.tight_layout(); plt.show()"
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]
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},
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"## Conclusion\n",
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"\n",
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"**Receta operativa para `graph_explorer` post-experimentos:**\n",
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"\n",
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"1. ⭐⭐⭐ **Naming snake_case verbal** (`works_at`, `headquartered_in`) — sin coste, gran impacto.\n",
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||
"2. ⭐⭐⭐ **Post-filter typed** (`{rel: (head_types, tail_types)}`) — elimina la mayoria de falsos absurdos. **Pure, sin coste.**\n",
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"3. ⭐⭐ **`include_confidence=True` + threshold por relacion** — evita el threshold global mediocre.\n",
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"4. ⭐⭐ **GLiREL como complemento** (cargado solo cuando sea necesario) con allowed_head/tail aplicado post-hoc.\n",
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"5. (no toques) Descripciones por relacion — sin efecto medible.\n",
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"\n",
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"**Stack final:**\n",
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"\n",
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"```python\n",
|
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"# 1. labels en snake_case verbal\n",
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"labels = ['works_at', 'ceo_of', 'president_of', 'headquartered_in', ...]\n",
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"schema = model.create_schema().entities(['person', 'organization', 'location']).relations(labels)\n",
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"\n",
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"# 2. extract con confidence\n",
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"r = model.extract(text, schema=schema, threshold=0.3, include_confidence=True)\n",
|
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"\n",
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"# 3. post-filter typed (gratis)\n",
|
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"filtered = filter_typed(r['relation_extraction'], name_to_type, ALLOWED)\n",
|
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"\n",
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"# 4. opcional: GLiREL como segundo opinador con allowed_head/tail filtrado post-hoc\n",
|
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"if rich_mode:\n",
|
||
" glirel_rels = glirel.predict_relations(tokens, labels=labels, threshold=0.0, ner=ner_spans, top_k=1)\n",
|
||
" glirel_filtered = [r for r in glirel_rels if compatible_types(r, ALLOWED, name_to_type)]\n",
|
||
" final_rels = union(filtered, glirel_filtered)\n",
|
||
"```\n",
|
||
"\n",
|
||
"**Funciones para promover al registry** (proximo fn-constructor):\n",
|
||
"1. `gliner2_load_model_py_datascience` (Apache 2.0)\n",
|
||
"2. `extract_graph_gliner2_py_datascience` (NER+RE, threshold por relacion, include_confidence)\n",
|
||
"3. `filter_relations_by_entity_types_py_core` (PURE — el ALLOWED filter)\n",
|
||
"4. `merge_extraction_sources_py_core` (PURE — UNION de GLiNER2 + GLiREL)\n",
|
||
"5. `extract_graph_hybrid_gliner2_glirel_py_pipelines` (composicion)"
|
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]
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