{ "cells": [ { "cell_type": "markdown", "id": "b06c454b", "metadata": { "id": "b06c454b" }, "source": [ "# NER Community Notes PT-BR — Pipeline Final v4\n", "\n", "Pipeline end-to-end para extração e curadoria de entidades nomeadas em notas\n", "da comunidade brasileira.\n", "\n", "## Etapas\n", "\n", "| # | Etapa | Tecnologia | Saída |\n", "|---|-------|------------|-------|\n", "| A | Extração | GLiNER + regex | `df_entidades_gliner.parquet` |\n", "| B | Triagem | Heurística | `fila_revisao.parquet` |\n", "| C | Revisão | Marco Mini (LLM local) | `revisoes_marco.parquet` |\n", "| D | Aplicação | Merge das decisões | `df_entidades_final.parquet` |\n", "| E | Análises | pandas | métricas + relatórios |\n", "\n", "## Características\n", "\n", "- **Extração híbrida**: GLiNER para semântica, regex para padrões formais.\n", "- **Threshold por tipo**: cada categoria com seu corte calibrado.\n", "- **Resolução de overlap**: regex > GLiNER em conflito (IoU ≥ 0.5).\n", "- **Sliding window**: textos > 1500 chars não são truncados silenciosamente.\n", "- **Canonicalização**: variações (\"Bolsa Familia\", \"BOLSA FAMÍLIA\") agregam corretas.\n", "- **Triagem inteligente para LLM**: revisa apenas casos duvidosos e tipos complexos,\n", " evitando rodar 1.4M de entidades pelo LLM.\n", "- **Aplicação determinística**: decisões do LLM aplicadas com audit trail.\n", "- **Checkpoint completo**: cada etapa retomável após disconnect.\n", "\n", "## Fluxo recomendado\n", "\n", "1. Roda com `LIMIT_ROWS = 1000` ponta a ponta.\n", "2. Confere análises da Etapa E.\n", "3. Roda com `LIMIT_ROWS = None`." ] }, { "cell_type": "code", "source": [ "# ============================================================\n", "# CÉLULA 1 — Carrega ou reutiliza a tabela mestra\n", "# ============================================================\n", "\n", "!pip -q install gdown pandas pyarrow tqdm\n", "\n", "import os\n", "import json\n", "import time\n", "import shutil\n", "import warnings\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from pathlib import Path\n", "from tqdm.auto import tqdm\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "DATA_PATH = Path(\"data/df_master_final.parquet\")\n", "DRIVE_FILE_ID = \"1OfLj_BzLX7AP9creSg2ua8rgydthSXhc\"\n", "\n", "if \"df\" in globals():\n", " print(\"Usando df já carregado na sessão.\")\n", "else:\n", " if not DATA_PATH.exists():\n", " import gdown\n", " DATA_PATH.parent.mkdir(parents=True, exist_ok=True)\n", " url = f\"https://drive.google.com/uc?id={DRIVE_FILE_ID}\"\n", " print(\"Baixando a base mestra...\")\n", " gdown.download(url, output=str(DATA_PATH), quiet=False, fuzzy=True)\n", "\n", " df = pd.read_parquet(DATA_PATH)\n", " print(f\"df carregado de: {DATA_PATH}\")\n", "\n", "print(f\"O dataset possui {df.shape[0]:,} linhas e {df.shape[1]:,} colunas.\")\n", "display(df.head(2))\n", "display(pd.DataFrame({\"coluna\": df.columns, \"dtype\": [str(df[c].dtype) for c in df.columns]}).head(50))\n" ], "metadata": { "colab": {}, "id": "WUs9aBSTZf3E" }, "id": "WUs9aBSTZf3E", "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": null, "id": "c70a1f49", "metadata": { "colab": {}, "id": "c70a1f49" }, "outputs": [], "source": [ "# ============================================================\n", "# 1) Configuração, logging e carga do df\n", "# ============================================================\n", "\n", "import logging\n", "import os\n", "import re\n", "import json\n", "import time\n", "import shutil\n", "import gc\n", "from pathlib import Path\n", "from urllib.parse import urlparse, unquote\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from tqdm.auto import tqdm\n", "\n", "# ------------------------------------------------------------\n", "# Logging\n", "# ------------------------------------------------------------\n", "logging.basicConfig(\n", " level=logging.INFO,\n", " format=\"%(asctime)s | %(levelname)s | %(message)s\",\n", " datefmt=\"%H:%M:%S\",\n", " force=True,\n", ")\n", "logger = logging.getLogger(\"ner_pipeline\")\n", "\n", "# ------------------------------------------------------------\n", "# Entrada\n", "# ------------------------------------------------------------\n", "DATA_PATH = Path(\"data/df_master_final.parquet\")\n", "TEXT_COL = \"texto_nota\"\n", "\n", "# Comece com 1000 ou 5000 para validar.\n", "LIMIT_ROWS = None\n", "\n", "# Tamanho dos blocos de checkpoint.\n", "CHUNK_SIZE = 1000\n", "\n", "# Batch GLiNER (T4=16, L4/A100=32-64)\n", "GLINER_BATCH_SIZE = 16\n", "\n", "# Sliding window\n", "MAX_TEXT_CHARS = 1500\n", "WINDOW_OVERLAP = 200\n", "\n", "# ------------------------------------------------------------\n", "# Camada LLM\n", "# ------------------------------------------------------------\n", "# Limite máximo de revisões LLM (mesmo se a triagem encontrar mais).\n", "# Calibre conforme tempo disponível: ~1-2s/revisão em T4.\n", "# 5000 revisões = ~1.5-3h em T4.\n", "LIMITE_REVISOES_LLM = 5000\n", "\n", "# Modos de triagem:\n", "# - \"tipos_complexos_baixa_conf\": só tipos ambíguos com score baixo (mais focado)\n", "# - \"todos_baixa_conf\": qualquer tipo com score baixo\n", "# - \"amostra_estratificada\": amostra fixa por tipo (validação)\n", "MODO_TRIAGEM = \"tipos_complexos_baixa_conf\"\n", "\n", "# Pula revisão LLM (só extração GLiNER+regex). Em geral, mantenha False.\n", "SKIP_LLM = False\n", "\n", "# ------------------------------------------------------------\n", "# Saída\n", "# ------------------------------------------------------------\n", "# Para salvar no Drive, monte primeiro:\n", "# from google.colab import drive; drive.mount(\"/content/drive\")\n", "# E troque para:\n", "# ARTIFACT_DIR = Path(\"/content/drive/MyDrive/community_notes_ner_pipeline\")\n", "ARTIFACT_DIR = Path(\"/content/community_notes_ner_pipeline\")\n", "\n", "EXTRACT_DIR = ARTIFACT_DIR / \"extracao_gliner\"\n", "PARTS_DIR = EXTRACT_DIR / \"parts\"\n", "REVIEW_DIR = ARTIFACT_DIR / \"revisao_llm\"\n", "FINAL_DIR = ARTIFACT_DIR / \"final\"\n", "CONFIG_DIR = ARTIFACT_DIR / \"config\"\n", "LOG_DIR = ARTIFACT_DIR / \"logs\"\n", "MODEL_DIR = ARTIFACT_DIR / \"models\"\n", "\n", "for p in [ARTIFACT_DIR, EXTRACT_DIR, PARTS_DIR, REVIEW_DIR, FINAL_DIR,\n", " CONFIG_DIR, LOG_DIR, MODEL_DIR]:\n", " p.mkdir(parents=True, exist_ok=True)\n", "\n", "# Handler de log persistente\n", "fh = logging.FileHandler(LOG_DIR / \"pipeline.log\", encoding=\"utf-8\")\n", "fh.setFormatter(logging.Formatter(\"%(asctime)s | %(levelname)s | %(message)s\"))\n", "logger.addHandler(fh)\n", "\n", "# ------------------------------------------------------------\n", "# Carrega df de forma idempotente\n", "# ------------------------------------------------------------\n", "if \"df\" not in globals():\n", " if not DATA_PATH.exists():\n", " raise FileNotFoundError(\n", " f\"Não encontrei {DATA_PATH}. Carregue o df antes ou ajuste DATA_PATH.\"\n", " )\n", " df = pd.read_parquet(DATA_PATH)\n", " logger.info(f\"df carregado de {DATA_PATH}\")\n", "else:\n", " logger.info(\"Usando df já carregado na sessão.\")\n", "\n", "if TEXT_COL not in df.columns:\n", " raise ValueError(\n", " f\"Coluna {TEXT_COL!r} não existe. Disponíveis: {list(df.columns)}\"\n", " )\n", "\n", "df_work = df.reset_index(drop=True).copy()\n", "df_work.insert(0, \"row_id\", np.arange(len(df_work)))\n", "\n", "ID_COL = \"noteId\" if \"noteId\" in df_work.columns else \"row_id\"\n", "\n", "if LIMIT_ROWS is not None:\n", " df_work = df_work.head(LIMIT_ROWS).copy()\n", "\n", "META_COLS = [\n", " \"row_id\", \"noteId\", \"tweet_id\", \"data_nota\", \"topic\", \"topic_name\",\n", " \"categoria_ampla\", \"contexto_central\", \"macrotheme_id\", \"macrotheme_label\",\n", " \"status_comunidade\", \"status_comunidade_norm\", \"status_base_label\",\n", " \"label_3classes\", \"label_binary_strict\", \"label_publicacao\",\n", "]\n", "META_COLS = [c for c in META_COLS if c in df_work.columns]\n", "\n", "text_lens = df_work[TEXT_COL].astype(str).str.len()\n", "logger.info(f\"df original: {df.shape}\")\n", "logger.info(f\"df para processar: {df_work.shape}\")\n", "logger.info(\n", " f\"Texto - p50={int(text_lens.median())} \"\n", " f\"p95={int(text_lens.quantile(0.95))} max={int(text_lens.max())}\"\n", ")\n", "logger.info(\n", " f\"Janelas necessárias (>{MAX_TEXT_CHARS} chars): \"\n", " f\"{(text_lens > MAX_TEXT_CHARS).sum()}\"\n", ")\n", "logger.info(f\"Artefatos em: {ARTIFACT_DIR}\")\n", "\n", "display(df_work[[c for c in [ID_COL, TEXT_COL] if c in df_work.columns]].head(3))\n" ] }, { "cell_type": "code", "execution_count": null, "id": "c894d701", "metadata": { "colab": {}, "id": "c894d701" }, "outputs": [], "source": [ "# ============================================================\n", "# 2) Instalação e carga do GLiNER\n", "# ============================================================\n", "\n", "!pip -q install \"gliner==0.2.13\" \"huggingface_hub>=0.23,<1.0\" \"pyarrow>=14,<19\" \"tqdm>=4.66\" \"unidecode>=1.3\" \"PyYAML>=6.0\"\n", "\n", "import torch\n", "import yaml\n", "from gliner import GLiNER\n", "from unidecode import unidecode\n", "\n", "# Suprime warnings cosméticos do tokenizer (truncate without max_length)\n", "import warnings\n", "import transformers\n", "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"transformers\")\n", "transformers.logging.set_verbosity_error()\n", "\n", "GLINER_MODEL_ID = \"urchade/gliner_multi-v2.1\"\n", "\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "logger.info(f\"Dispositivo: {device}\")\n", "if device == \"cpu\":\n", " logger.warning(\"Sem GPU. Para 142k notas, habilite Runtime > Change runtime type.\")\n", "\n", "gliner_model = GLiNER.from_pretrained(GLINER_MODEL_ID).to(device)\n", "actual_device = next(gliner_model.parameters()).device\n", "logger.info(f\"GLiNER carregado | device real: {actual_device}\")\n", "\n", "if device == \"cuda\" and actual_device.type != \"cuda\":\n", " raise RuntimeError(f\"GPU disponível mas modelo em {actual_device}.\")\n", "\n", "HAS_BATCH_API = hasattr(gliner_model, \"batch_predict_entities\")\n", "logger.info(f\"Batch API disponível: {HAS_BATCH_API}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "9dc198e5", "metadata": { "colab": {}, "id": "9dc198e5" }, "outputs": [], "source": [ "# ============================================================\n", "# 3) Taxonomia + thresholds por tipo\n", "# ============================================================\n", "\n", "THRESHOLD_DEFAULT = 0.40\n", "\n", "# (label_en, label_pt, threshold, papel_padrao)\n", "TAXONOMY = [\n", " (\"person\", \"PESSOA\", 0.45, \"mencao\"),\n", " (\"public figure\", \"PESSOA_PUBLICA\", 0.45, \"mencao\"),\n", " (\"politician\", \"ATOR_POLITICO\", 0.45, \"mencao\"),\n", " (\"political party\", \"PARTIDO\", 0.50, \"mencao\"),\n", " (\"government program\", \"PROGRAMA_PUBLICO\", 0.40, \"mencao\"),\n", " (\"government agency\", \"ORGAO_PUBLICO\", 0.45, \"mencao\"),\n", " (\"court\", \"ORGAO_JUDICIARIO\", 0.50, \"mencao\"),\n", " (\"police or security force\", \"ORGAO_SEGURANCA\", 0.50, \"mencao\"),\n", " (\"news media outlet\", \"VEICULO_MIDIA\", 0.45, \"fonte_ou_evidencia\"),\n", " (\"digital platform\", \"PLATAFORMA_DIGITAL\", 0.45, \"mencao\"),\n", " (\"organization\", \"ORGANIZACAO\", 0.45, \"mencao\"),\n", " (\"location\", \"LOCAL\", 0.45, \"mencao\"),\n", " (\"political event\", \"EVENTO_POLITICO\", 0.55, \"mencao\"),\n", " (\"police operation\", \"OPERACAO_POLICIAL\", 0.55, \"mencao\"),\n", " (\"court case\", \"PROCESSO_JUDICIAL\", 0.55, \"fonte_ou_evidencia\"),\n", " (\"cited source\", \"FONTE_CITADA\", 0.55, \"fonte_ou_evidencia\"),\n", " # date, money, statistic, law e URL ficam por conta do regex (alta precisão)\n", "]\n", "\n", "LABEL_TO_PT = {a: b for a, b, _, _ in TAXONOMY}\n", "PT_TO_LABEL = {b: a for a, b, _, _ in TAXONOMY}\n", "LABEL_TO_THRESHOLD = {a: t for a, _, t, _ in TAXONOMY}\n", "PT_TO_PAPEL = {b: p for _, b, _, p in TAXONOMY}\n", "GLINER_LABELS = [a for a, _, _, _ in TAXONOMY]\n", "\n", "TIPOS_EVIDENCIA = {\n", " \"URL_DOMINIO\", \"FONTE_CITADA\", \"VEICULO_MIDIA\", \"ESTATISTICA\",\n", " \"LEI_NORMA\", \"PROCESSO_JUDICIAL\", \"DATA\", \"VALOR_MONETARIO\",\n", "}\n", "\n", "# Tipos exclusivos do regex (fora da taxonomia GLiNER)\n", "TIPOS_REGEX_EXTRAS = {\"URL_DOMINIO\", \"DATA\", \"VALOR_MONETARIO\",\n", " \"ESTATISTICA\", \"LEI_NORMA\", \"PROCESSO_JUDICIAL\"}\n", "\n", "# Conjunto completo de tipos válidos no sistema\n", "TIPOS_VALIDOS = set(LABEL_TO_PT.values()) | TIPOS_REGEX_EXTRAS\n", "\n", "THRESHOLD_GLINER_FLOOR = min(LABEL_TO_THRESHOLD.values()) - 0.05\n", "\n", "with open(CONFIG_DIR / \"taxonomy_v4.json\", \"w\", encoding=\"utf-8\") as f:\n", " json.dump({\n", " \"model_id\": GLINER_MODEL_ID,\n", " \"threshold_floor\": THRESHOLD_GLINER_FLOOR,\n", " \"threshold_default\": THRESHOLD_DEFAULT,\n", " \"taxonomy\": [\n", " {\"label_en\": a, \"label_pt\": b, \"threshold\": t, \"papel_padrao\": p}\n", " for a, b, t, p in TAXONOMY\n", " ],\n", " \"tipos_evidencia\": sorted(TIPOS_EVIDENCIA),\n", " \"tipos_regex_extras\": sorted(TIPOS_REGEX_EXTRAS),\n", " \"tipos_validos\": sorted(TIPOS_VALIDOS),\n", " }, f, ensure_ascii=False, indent=2)\n", "\n", "logger.info(f\"Taxonomia: {len(TAXONOMY)} tipos GLiNER + {len(TIPOS_REGEX_EXTRAS)} regex\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "51ea7e1f", "metadata": { "colab": {}, "id": "51ea7e1f" }, "outputs": [], "source": [ "# ============================================================\n", "# 4) Dicionários de domínio (YAML editável)\n", "# ============================================================\n", "\n", "DICT_PATH = CONFIG_DIR / \"dicionarios.yaml\"\n", "\n", "DICT_DEFAULT = {\n", " \"entidades_genericas\": [\n", " \"governo\", \"programa\", \"proposta\", \"postagem\", \"mensagem\", \"contexto\",\n", " \"mentira\", \"imprensa\", \"veículos de imprensa\", \"canais de imprensa\",\n", " \"site\", \"sites\", \"notícia\", \"nota\", \"texto\", \"fato\", \"verdade\",\n", " \"boato\", \"fake\", \"fake news\", \"rede social\", \"redes sociais\",\n", " \"presidente\", \"ministro\", \"deputado\", \"senador\", # cargos sem nome\n", " ],\n", " \"programas_publicos\": {\n", " \"Auxílio Brasil\": [\"auxilio brasil\", \"auxílio brasil\"],\n", " \"Bolsa Família\": [\"bolsa familia\", \"bolsa família\", \"bolsa-familia\"],\n", " \"Minha Casa Minha Vida\": [\"minha casa minha vida\", \"programa minha casa minha vida\", \"mcmv\"],\n", " \"Prouni\": [\"prouni\"],\n", " \"Fies\": [\"fies\"],\n", " \"SUS\": [\"sus\", \"sistema unico de saude\", \"sistema único de saúde\"],\n", " \"CadÚnico\": [\"cadunico\", \"cadúnico\", \"cad unico\", \"cad único\"],\n", " \"Novo PAC\": [\"novo pac\", \"pac\"],\n", " \"Farmácia Popular\": [\"farmacia popular\", \"farmácia popular\"],\n", " \"Auxílio Emergencial\": [\"auxilio emergencial\", \"auxílio emergencial\"],\n", " \"Vale-Gás\": [\"vale gas\", \"vale gás\", \"vale-gas\", \"vale-gás\"],\n", " \"Pé-de-Meia\": [\"pé-de-meia\", \"pe-de-meia\", \"pé de meia\", \"pe de meia\"],\n", " },\n", " \"siglas_orgaos\": {\n", " \"STF\": [\"stf\", \"supremo tribunal federal\"],\n", " \"TSE\": [\"tse\", \"tribunal superior eleitoral\"],\n", " \"STJ\": [\"stj\", \"superior tribunal de justiça\"],\n", " \"MPF\": [\"mpf\", \"ministério público federal\"],\n", " \"PGR\": [\"pgr\", \"procuradoria-geral da república\"],\n", " \"PF\": [\"pf\", \"polícia federal\"],\n", " \"PRF\": [\"prf\", \"polícia rodoviária federal\"],\n", " \"IBGE\": [\"ibge\"],\n", " \"Ipea\": [\"ipea\"],\n", " \"Anvisa\": [\"anvisa\"],\n", " \"INSS\": [\"inss\"],\n", " \"TCU\": [\"tcu\"],\n", " \"CGU\": [\"cgu\"],\n", " \"AGU\": [\"agu\"],\n", " \"Banco Central\": [\"bcb\", \"banco central\", \"bc do brasil\"],\n", " },\n", " \"partidos_br\": {\n", " \"PT\": [\"pt\", \"partido dos trabalhadores\"],\n", " \"PL\": [\"pl\", \"partido liberal\"],\n", " \"MDB\": [\"mdb\"], \"PSDB\": [\"psdb\"], \"PSOL\": [\"psol\"], \"PDT\": [\"pdt\"],\n", " \"PCdoB\": [\"pcdob\", \"pc do b\", \"pc do brasil\"],\n", " \"PP\": [\"pp\", \"progressistas\"], \"PSD\": [\"psd\"],\n", " \"Novo\": [\"novo\", \"partido novo\"],\n", " \"Rede\": [\"rede\", \"rede sustentabilidade\"],\n", " \"União Brasil\": [\"uniao brasil\", \"união brasil\"],\n", " \"Republicanos\": [\"republicanos\"], \"Cidadania\": [\"cidadania\"],\n", " \"Avante\": [\"avante\"], \"Podemos\": [\"podemos\"],\n", " \"Solidariedade\": [\"solidariedade\"], \"PSB\": [\"psb\"],\n", " \"Agir\": [\"agir\"], \"Patriota\": [\"patriota\"], \"PRTB\": [\"prtb\"],\n", " \"PV\": [\"pv\", \"partido verde\"],\n", " },\n", "}\n", "\n", "if not DICT_PATH.exists():\n", " with open(DICT_PATH, \"w\", encoding=\"utf-8\") as f:\n", " yaml.safe_dump(DICT_DEFAULT, f, allow_unicode=True, sort_keys=False)\n", " logger.info(f\"Dicionário default escrito: {DICT_PATH}\")\n", "\n", "with open(DICT_PATH, \"r\", encoding=\"utf-8\") as f:\n", " DICTS = yaml.safe_load(f)\n", "\n", "ENTIDADES_GENERICAS = set(DICTS[\"entidades_genericas\"])\n", "\n", "def _build_lookup(canonical_dict):\n", " return {\n", " unidecode(var.lower().strip()): canonical\n", " for canonical, vars_ in canonical_dict.items()\n", " for var in vars_\n", " }\n", "\n", "PROGRAMAS_LOOKUP = _build_lookup(DICTS[\"programas_publicos\"])\n", "ORGAOS_LOOKUP = _build_lookup(DICTS[\"siglas_orgaos\"])\n", "PARTIDOS_LOOKUP = _build_lookup(DICTS[\"partidos_br\"])\n", "\n", "logger.info(\n", " f\"Dicionários: {len(ENTIDADES_GENERICAS)} genéricas | \"\n", " f\"{len(PROGRAMAS_LOOKUP)} programas | \"\n", " f\"{len(ORGAOS_LOOKUP)} órgãos | {len(PARTIDOS_LOOKUP)} partidos\"\n", ")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "92a8d5b5", "metadata": { "colab": {}, "id": "92a8d5b5" }, "outputs": [], "source": [ "# ============================================================\n", "# 5) Regex (alta precisão para padrões formais)\n", "# ============================================================\n", "\n", "RE_URL = re.compile(\n", " r\"(?:https?://[^\\s<>\\\"'\\]\\)]+|www\\.[^\\s<>\\\"'\\]\\)]+)\",\n", " flags=re.I,\n", ")\n", "\n", "RE_PERCENT = re.compile(\n", " r\"(?'\\\"“”\")\n", "\n", "\n", "def dominio_canonico(url):\n", " raw = limpar_url(url)\n", " if not raw:\n", " return \"\"\n", " raw_parse = \"https://\" + raw if raw.lower().startswith(\"www.\") else raw\n", " try:\n", " parsed = urlparse(raw_parse)\n", " except Exception:\n", " return \"\"\n", " dominio = (parsed.netloc or \"\").lower().replace(\"www.\", \"\")\n", " path = unquote(parsed.path or \"\")\n", " if dominio in {\"google.com\", \"google.com.br\"}:\n", " m = re.search(r\"/amp/s/([^/]+)\", path)\n", " if m:\n", " return m.group(1).lower().replace(\"www.\", \"\")\n", " if dominio.startswith(\"amp.\"):\n", " return dominio.replace(\"amp.\", \"\", 1)\n", " return dominio\n", "\n", "\n", "def pegar_contexto(texto, inicio, fim, largura=90):\n", " if pd.isna(texto):\n", " return \"\"\n", " texto = str(texto)\n", " a = max(0, int(inicio) - largura)\n", " b = min(len(texto), int(fim) + largura)\n", " return texto[a:b].replace(\"\\n\", \" \").strip()\n", "\n", "\n", "def validar_entidade(ent, texto):\n", " try:\n", " ini, fim = int(ent[\"inicio\"]), int(ent[\"fim\"])\n", " score = float(ent.get(\"score\", 0.0))\n", " except (KeyError, TypeError, ValueError):\n", " return False\n", " if ini < 0 or fim <= ini or fim > len(texto):\n", " return False\n", " if not (0.0 <= score <= 1.0):\n", " return False\n", " if not str(ent.get(\"texto_entidade\", \"\")).strip():\n", " return False\n", " return True\n", "\n", "\n", "def gerar_janelas(texto, max_chars=MAX_TEXT_CHARS, overlap=WINDOW_OVERLAP):\n", " n = len(texto)\n", " if n <= max_chars:\n", " yield texto, 0\n", " return\n", " start = 0\n", " while start < n:\n", " end = min(start + max_chars, n)\n", " if end < n:\n", " corte = texto.rfind(\". \", start, end)\n", " if corte == -1 or corte < end - 100:\n", " corte = texto.rfind(\" \", end - 50, end)\n", " if corte != -1 and corte > start + max_chars // 2:\n", " end = corte + 1\n", " yield texto[start:end], start\n", " if end >= n:\n", " break\n", " start = end - overlap\n", "\n", "logger.info(\"Utilitários carregados.\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "400d42e8", "metadata": { "id": "400d42e8" }, "outputs": [], "source": [ "# ============================================================\n", "# 7) Extração via regex\n", "# ============================================================\n", "\n", "def entidades_regex(texto):\n", " if pd.isna(texto) or not str(texto).strip():\n", " return []\n", " texto = str(texto)\n", " saida = []\n", "\n", " for m in RE_URL.finditer(texto):\n", " url = limpar_url(m.group(0))\n", " saida.append({\n", " \"texto_entidade\": url,\n", " \"tipo_entidade\": \"URL_DOMINIO\",\n", " \"label_modelo\": \"URL_DOMINIO\",\n", " \"inicio\": m.start(),\n", " \"fim\": m.start() + len(url),\n", " \"score\": 1.0,\n", " \"fonte_extracao\": \"regex_url\",\n", " \"contexto\": pegar_contexto(texto, m.start(), m.end()),\n", " \"papel_no_texto\": \"fonte_ou_evidencia\",\n", " \"dominio\": dominio_canonico(url),\n", " })\n", "\n", " def add_simples(match, tipo, fonte, score):\n", " ini, fim = match.start(), match.end()\n", " saida.append({\n", " \"texto_entidade\": match.group(0),\n", " \"tipo_entidade\": tipo,\n", " \"label_modelo\": tipo,\n", " \"inicio\": ini,\n", " \"fim\": fim,\n", " \"score\": float(score),\n", " \"fonte_extracao\": fonte,\n", " \"contexto\": pegar_contexto(texto, ini, fim),\n", " \"papel_no_texto\": \"fonte_ou_evidencia\" if tipo in TIPOS_EVIDENCIA else \"mencao\",\n", " \"dominio\": None,\n", " })\n", "\n", " for m in RE_PERCENT.finditer(texto):\n", " add_simples(m, \"ESTATISTICA\", \"regex_percentual\", 0.98)\n", " for m in RE_MONEY.finditer(texto):\n", " add_simples(m, \"VALOR_MONETARIO\", \"regex_valor\", 0.98)\n", " for m in RE_DATE.finditer(texto):\n", " add_simples(m, \"DATA\", \"regex_data\", 0.94)\n", " for m in RE_LAW.finditer(texto):\n", " add_simples(m, \"LEI_NORMA\", \"regex_lei_norma\", 0.96)\n", " for m in RE_PROCESSO.finditer(texto):\n", " add_simples(m, \"PROCESSO_JUDICIAL\", \"regex_processo\", 0.96)\n", "\n", " return saida\n" ] }, { "cell_type": "code", "execution_count": null, "id": "6cdc5144", "metadata": { "id": "6cdc5144" }, "outputs": [], "source": [ "# ============================================================\n", "# 8) Resolução de overlap\n", "# ============================================================\n", "\n", "FONTE_PRIORIDADE = {\n", " \"regex_url\": 100, \"regex_processo\": 95, \"regex_lei_norma\": 95,\n", " \"regex_valor\": 90, \"regex_percentual\": 90, \"regex_data\": 85,\n", " \"gliner\": 50,\n", "}\n", "\n", "\n", "def _iou(a, b):\n", " inter = max(0, min(a[\"fim\"], b[\"fim\"]) - max(a[\"inicio\"], b[\"inicio\"]))\n", " if inter == 0:\n", " return 0.0\n", " union = max(a[\"fim\"], b[\"fim\"]) - min(a[\"inicio\"], b[\"inicio\"])\n", " return inter / union if union > 0 else 0.0\n", "\n", "\n", "def resolver_overlaps(entidades, iou_threshold=0.5):\n", " if not entidades:\n", " return []\n", "\n", " def chave(e):\n", " return (\n", " FONTE_PRIORIDADE.get(e.get(\"fonte_extracao\", \"gliner\"), 0),\n", " float(e.get(\"score\", 0.0)),\n", " e[\"fim\"] - e[\"inicio\"],\n", " )\n", "\n", " ordenadas = sorted(entidades, key=chave, reverse=True)\n", " aceitas = []\n", " for cand in ordenadas:\n", " if any(_iou(cand, ja) >= iou_threshold for ja in aceitas):\n", " continue\n", " aceitas.append(cand)\n", " return sorted(aceitas, key=lambda e: (e[\"inicio\"], e[\"fim\"]))\n" ] }, { "cell_type": "code", "execution_count": null, "id": "18fb60d1", "metadata": { "colab": {}, "id": "18fb60d1" }, "outputs": [], "source": [ "# ============================================================\n", "# 9) Pós-processamento (correções + filtros + canonical)\n", "# ============================================================\n", "\n", "def aplicar_threshold_por_tipo(entidades):\n", " saida = []\n", " for e in entidades:\n", " if e.get(\"fonte_extracao\") != \"gliner\":\n", " saida.append(e)\n", " continue\n", " thr = LABEL_TO_THRESHOLD.get(e.get(\"label_modelo\"), THRESHOLD_DEFAULT)\n", " if float(e.get(\"score\", 0.0)) >= thr:\n", " saida.append(e)\n", " return saida\n", "\n", "\n", "def corrigir_tipos_via_dicionario(entidades):\n", " for e in entidades:\n", " norm = normalizar_txt(e[\"texto_entidade\"])\n", " if norm in PROGRAMAS_LOOKUP:\n", " e[\"tipo_entidade\"] = \"PROGRAMA_PUBLICO\"\n", " e[\"label_modelo\"] = \"government program\"\n", " e[\"papel_no_texto\"] = \"mencao\"\n", " elif norm in ORGAOS_LOOKUP:\n", " e[\"tipo_entidade\"] = \"ORGAO_PUBLICO\"\n", " e[\"label_modelo\"] = \"government agency\"\n", " elif norm in PARTIDOS_LOOKUP:\n", " e[\"tipo_entidade\"] = \"PARTIDO\"\n", " e[\"label_modelo\"] = \"political party\"\n", " return entidades\n", "\n", "\n", "def remover_genericos(entidades):\n", " tipos_protegidos = {\n", " \"URL_DOMINIO\", \"DATA\", \"VALOR_MONETARIO\", \"ESTATISTICA\",\n", " \"LEI_NORMA\", \"PROCESSO_JUDICIAL\",\n", " }\n", " genericos_norm = {normalizar_txt(x) for x in ENTIDADES_GENERICAS}\n", " return [\n", " e for e in entidades\n", " if (e[\"tipo_entidade\"] in tipos_protegidos\n", " or normalizar_txt(e[\"texto_entidade\"]) not in genericos_norm)\n", " ]\n", "\n", "\n", "def limpar_urls(entidades):\n", " for e in entidades:\n", " if e[\"tipo_entidade\"] == \"URL_DOMINIO\":\n", " url = limpar_url(e[\"texto_entidade\"])\n", " e[\"texto_entidade\"] = url\n", " e[\"dominio\"] = dominio_canonico(url)\n", " return entidades\n", "\n", "\n", "def recalcular_papel(entidades):\n", " for e in entidades:\n", " if e[\"tipo_entidade\"] in TIPOS_EVIDENCIA:\n", " e[\"papel_no_texto\"] = \"fonte_ou_evidencia\"\n", " elif e.get(\"papel_no_texto\") not in {\"mencao\", \"fonte_ou_evidencia\"}:\n", " e[\"papel_no_texto\"] = PT_TO_PAPEL.get(e[\"tipo_entidade\"], \"mencao\")\n", " return entidades\n", "\n", "\n", "def adicionar_canonico(entidades):\n", " for e in entidades:\n", " e[\"texto_canonico\"] = texto_canonico(e[\"texto_entidade\"], e[\"tipo_entidade\"])\n", " return entidades\n", "\n", "\n", "def posprocessar(entidades, texto):\n", " if not entidades:\n", " return []\n", " entidades = [e for e in entidades if validar_entidade(e, texto)]\n", " entidades = aplicar_threshold_por_tipo(entidades)\n", " for e in entidades:\n", " e.setdefault(\"dominio\", None)\n", " entidades = corrigir_tipos_via_dicionario(entidades)\n", " entidades = limpar_urls(entidades)\n", " entidades = remover_genericos(entidades)\n", " entidades = resolver_overlaps(entidades)\n", " entidades = recalcular_papel(entidades)\n", " entidades = adicionar_canonico(entidades)\n", " return entidades\n", "\n", "\n", "logger.info(\"Pós-processamento pronto.\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "b4c146e1", "metadata": { "colab": {}, "id": "b4c146e1" }, "outputs": [], "source": [ "# ============================================================\n", "# 10) Extração em batch (GLiNER + regex + pós-processamento)\n", "# ============================================================\n", "\n", "def _gliner_predict_batch(textos, model, labels, threshold):\n", " if HAS_BATCH_API:\n", " try:\n", " return model.batch_predict_entities(textos, labels, threshold=threshold)\n", " except TypeError:\n", " return model.batch_predict_entities(textos, labels)\n", " except Exception as e:\n", " logger.warning(f\"batch_predict falhou ({e}); fallback para loop.\")\n", " out = []\n", " for t in textos:\n", " try:\n", " out.append(model.predict_entities(t, labels, threshold=threshold))\n", " except TypeError:\n", " out.append(model.predict_entities(t, labels))\n", " return out\n", "\n", "\n", "def _gliner_para_entidades(preds, texto_janela, offset_global):\n", " saida = []\n", " for p in preds:\n", " label_en = p.get(\"label\")\n", " if label_en not in LABEL_TO_PT:\n", " continue\n", " ini_local = int(p.get(\"start\", 0))\n", " fim_local = int(p.get(\"end\", 0))\n", " if fim_local <= ini_local:\n", " continue\n", " trecho = (p.get(\"text\") or texto_janela[ini_local:fim_local]).strip()\n", " if not trecho:\n", " continue\n", " label_pt = LABEL_TO_PT[label_en]\n", " saida.append({\n", " \"texto_entidade\": trecho,\n", " \"tipo_entidade\": label_pt,\n", " \"label_modelo\": label_en,\n", " \"inicio\": ini_local + offset_global,\n", " \"fim\": fim_local + offset_global,\n", " \"score\": float(p.get(\"score\", 0.0)),\n", " \"fonte_extracao\": \"gliner\",\n", " \"contexto\": \"\",\n", " \"papel_no_texto\": PT_TO_PAPEL.get(label_pt, \"mencao\"),\n", " \"dominio\": None,\n", " })\n", " return saida\n", "\n", "\n", "def extrair_entidades_lote(textos, model=None, batch_size=GLINER_BATCH_SIZE):\n", " if model is None:\n", " model = gliner_model\n", " n = len(textos)\n", " resultado = [None] * n\n", " textos_norm = [\n", " re.sub(r\"\\s+\", \" \", str(t)).strip() if not pd.isna(t) else \"\"\n", " for t in textos\n", " ]\n", " pares = []\n", " for i, t in enumerate(textos_norm):\n", " if not t:\n", " resultado[i] = []\n", " continue\n", " for janela, offset in gerar_janelas(t):\n", " pares.append((i, janela, offset))\n", "\n", " buckets = {i: [] for i in range(n) if resultado[i] is None}\n", "\n", " for start in range(0, len(pares), batch_size):\n", " sub = pares[start:start + batch_size]\n", " textos_batch = [p[1] for p in sub]\n", " preds_batch = _gliner_predict_batch(\n", " textos_batch, model, GLINER_LABELS, THRESHOLD_GLINER_FLOOR\n", " )\n", " for (idx, janela, offset), preds in zip(sub, preds_batch):\n", " buckets[idx].extend(_gliner_para_entidades(preds, janela, offset))\n", "\n", " for i, t in enumerate(textos_norm):\n", " if resultado[i] is not None:\n", " continue\n", " ents = buckets.get(i, []) + entidades_regex(t)\n", " for e in ents:\n", " if not e.get(\"contexto\"):\n", " e[\"contexto\"] = pegar_contexto(t, e[\"inicio\"], e[\"fim\"])\n", " resultado[i] = posprocessar(ents, t)\n", "\n", " return resultado\n", "\n", "\n", "logger.info(\"Extração batch pronta.\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "452cf1bd", "metadata": { "colab": {}, "id": "452cf1bd" }, "outputs": [], "source": [ "# ============================================================\n", "# 11) Teste rápido\n", "# ============================================================\n", "\n", "N_TESTE = 5\n", "amostra = df_work.head(N_TESTE)\n", "textos_amostra = amostra[TEXT_COL].tolist()\n", "\n", "t0 = time.time()\n", "ents_amostra = extrair_entidades_lote(textos_amostra)\n", "dt = time.time() - t0\n", "logger.info(f\"Tempo {N_TESTE} notas: {dt:.2f}s ({dt/N_TESTE:.2f}s/nota)\")\n", "logger.info(f\"Projeção 142k: {142000 * dt / N_TESTE / 3600:.1f}h\")\n", "\n", "for (_, row), ents in zip(amostra.iterrows(), ents_amostra):\n", " print(\"=\" * 100)\n", " print(f\"{ID_COL}: {row[ID_COL]}\")\n", " print(str(row[TEXT_COL])[:500])\n", " if ents:\n", " cols = [\"texto_entidade\", \"texto_canonico\", \"tipo_entidade\",\n", " \"score\", \"fonte_extracao\", \"papel_no_texto\", \"dominio\"]\n", " df_show = pd.DataFrame(ents)\n", " display(df_show[[c for c in cols if c in df_show.columns]])\n", " else:\n", " print(\"Nenhuma entidade extraída.\")\n" ] }, { "cell_type": "markdown", "id": "b9f3d141", "metadata": { "id": "b9f3d141" }, "source": [ "# Etapa A — Extração massiva (GLiNER + regex)" ] }, { "cell_type": "code", "execution_count": null, "id": "90206274", "metadata": { "colab": {}, "id": "90206274" }, "outputs": [], "source": [ "# ============================================================\n", "# 12) Extração em massa com checkpoint\n", "# ============================================================\n", "\n", "SAVE_EVERY = 200\n", "\n", "\n", "def processar_bloco(df_bloco, part_path):\n", " tmp_path = part_path.with_suffix(\".tmp.parquet\")\n", " records = []\n", " n = len(df_bloco)\n", " indices = list(range(0, n, GLINER_BATCH_SIZE))\n", "\n", " for start in tqdm(indices, leave=False, desc=\"batches\"):\n", " end = min(start + GLINER_BATCH_SIZE, n)\n", " sub = df_bloco.iloc[start:end]\n", " textos_sub = sub[TEXT_COL].tolist()\n", " ents_sub = extrair_entidades_lote(textos_sub, batch_size=GLINER_BATCH_SIZE)\n", "\n", " for (_, row), ents in zip(sub.iterrows(), ents_sub):\n", " meta = {c: row[c] for c in META_COLS if c in row.index}\n", " for j, ent in enumerate(ents):\n", " rec = dict(meta)\n", " rec.update(ent)\n", " rec[\"entity_id\"] = f\"{row['row_id']}_{j}\"\n", " records.append(rec)\n", "\n", " if records and (end % SAVE_EVERY == 0 or end == n):\n", " pd.DataFrame(records).to_parquet(tmp_path, index=False)\n", "\n", " if records:\n", " pd.DataFrame(records).to_parquet(part_path, index=False)\n", " else:\n", " pd.DataFrame().to_parquet(part_path, index=False)\n", " if tmp_path.exists():\n", " tmp_path.unlink()\n", "\n", "\n", "n = len(df_work)\n", "blocos = list(range(0, n, CHUNK_SIZE))\n", "logger.info(f\"Etapa A: {n:,} notas em {len(blocos)} blocos | batch={GLINER_BATCH_SIZE}\")\n", "\n", "t0 = time.time()\n", "for start in tqdm(blocos, desc=\"Blocos\"):\n", " end = min(start + CHUNK_SIZE, n)\n", " part_path = PARTS_DIR / f\"entities_part_{start:07d}_{end:07d}.parquet\"\n", " if part_path.exists():\n", " continue\n", " df_bloco = df_work.iloc[start:end].copy()\n", " try:\n", " processar_bloco(df_bloco, part_path)\n", " except Exception as e:\n", " logger.error(f\"Falha bloco {start}-{end}: {e}\", exc_info=True)\n", " raise\n", "\n", "logger.info(f\"Extração: {(time.time() - t0) / 60:.2f} min\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "816bd9cc", "metadata": { "colab": {}, "id": "816bd9cc" }, "outputs": [], "source": [ "# ============================================================\n", "# 13) Consolidação da extração\n", "# ============================================================\n", "\n", "part_files = sorted(PARTS_DIR.glob(\"entities_part_*.parquet\"))\n", "if not part_files:\n", " raise FileNotFoundError(f\"Nenhum parquet em {PARTS_DIR}\")\n", "\n", "dfs = [pd.read_parquet(p) for p in part_files if pd.read_parquet(p).shape[0] > 0]\n", "df_entidades = pd.concat(dfs, ignore_index=True) if dfs else pd.DataFrame()\n", "\n", "if len(df_entidades) > 0:\n", " df_entidades = df_entidades.sort_values(\n", " [\"row_id\", \"inicio\", \"fim\", \"tipo_entidade\", \"score\"],\n", " ascending=[True, True, True, True, False],\n", " ).reset_index(drop=True)\n", "\n", "path_entities = EXTRACT_DIR / \"df_entidades_gliner.parquet\"\n", "path_entities_csv = EXTRACT_DIR / \"df_entidades_gliner.csv\"\n", "df_entidades.to_parquet(path_entities, index=False)\n", "df_entidades.to_csv(path_entities_csv, index=False, encoding=\"utf-8-sig\")\n", "\n", "logger.info(f\"Entidades extraídas: {len(df_entidades):,}\")\n", "logger.info(f\"Por nota: média={len(df_entidades)/max(len(df_work),1):.1f}\")\n", "logger.info(f\"Salvo: {path_entities}\")\n", "\n", "display(df_entidades.head(20))\n", "\n", "if len(df_entidades) > 0:\n", " print(\"\\nDistribuição por tipo:\")\n", " display(df_entidades[\"tipo_entidade\"].value_counts().rename(\"qtd\").to_frame())\n" ] }, { "cell_type": "markdown", "id": "aec56334", "metadata": { "id": "aec56334" }, "source": [ "# Etapa B — Triagem para revisão LLM\n", "\n", "A revisão pelo LLM é cara: ~1-2s por entidade. Para 1.4M entidades seria inviável.\n", "\n", "A triagem identifica os casos onde a revisão LLM **mais agrega valor**:\n", "- Tipos ambíguos com score baixo\n", "- Entidades que parecem genéricas mas escaparam do filtro\n", "- Casos com sobreposição parcial\n", "\n", "Tipos com alta precisão estrutural (URL, DATA via regex) **não** vão para revisão." ] }, { "cell_type": "code", "execution_count": null, "id": "1e7a3699", "metadata": { "colab": {}, "id": "1e7a3699" }, "outputs": [], "source": [ "# ============================================================\n", "# 14) Triagem da fila para revisão LLM\n", "# ============================================================\n", "\n", "if SKIP_LLM or len(df_entidades) == 0:\n", " logger.info(\"Pulando triagem LLM.\")\n", " df_queue = pd.DataFrame()\n", "else:\n", " df_q = df_entidades.copy()\n", " df_q[\"score_num\"] = pd.to_numeric(df_q[\"score\"], errors=\"coerce\").fillna(0.5)\n", "\n", " # Tipos que se beneficiam mais da revisão LLM (categorias semânticas\n", " # ambíguas em PT-BR, onde GLiNER pode confundir)\n", " TIPOS_AMBIGUOS = {\n", " \"PESSOA\", \"PESSOA_PUBLICA\", \"ATOR_POLITICO\",\n", " \"ORGAO_PUBLICO\", \"ORGAO_JUDICIARIO\", \"ORGAO_SEGURANCA\",\n", " \"ORGANIZACAO\", \"VEICULO_MIDIA\", \"FONTE_CITADA\",\n", " \"EVENTO_POLITICO\", \"OPERACAO_POLICIAL\",\n", " \"PROGRAMA_PUBLICO\", \"PARTIDO\",\n", " }\n", "\n", " # Tipos que NÃO precisam de revisão (regex de alta precisão)\n", " TIPOS_PULAR_REVISAO = {\"URL_DOMINIO\", \"DATA\", \"VALOR_MONETARIO\",\n", " \"ESTATISTICA\", \"LEI_NORMA\", \"PROCESSO_JUDICIAL\"}\n", "\n", " # Score de prioridade (maior = revisar primeiro)\n", " df_q[\"prio_baixa_conf\"] = (1.0 - df_q[\"score_num\"]).clip(0, 1)\n", " df_q[\"prio_tipo_ambiguo\"] = df_q[\"tipo_entidade\"].isin(TIPOS_AMBIGUOS).astype(float)\n", " df_q[\"prio_gliner\"] = (df_q[\"fonte_extracao\"] == \"gliner\").astype(float)\n", "\n", " # Score combinado (pesos diferentes por modo)\n", " if MODO_TRIAGEM == \"tipos_complexos_baixa_conf\":\n", " df_q[\"prioridade_revisao\"] = (\n", " df_q[\"prio_baixa_conf\"] * 2.0\n", " + df_q[\"prio_tipo_ambiguo\"] * 1.5\n", " + df_q[\"prio_gliner\"] * 0.5\n", " )\n", " df_q = df_q[df_q[\"tipo_entidade\"].isin(TIPOS_AMBIGUOS)]\n", " df_q = df_q[df_q[\"score_num\"] < 0.75] # foca em zona de incerteza\n", " elif MODO_TRIAGEM == \"todos_baixa_conf\":\n", " df_q[\"prioridade_revisao\"] = df_q[\"prio_baixa_conf\"] * 2.0 + df_q[\"prio_gliner\"]\n", " df_q = df_q[~df_q[\"tipo_entidade\"].isin(TIPOS_PULAR_REVISAO)]\n", " df_q = df_q[df_q[\"score_num\"] < 0.7]\n", " elif MODO_TRIAGEM == \"amostra_estratificada\":\n", " # 50 por tipo (ou todos se < 50)\n", " df_q = df_q[~df_q[\"tipo_entidade\"].isin(TIPOS_PULAR_REVISAO)]\n", " df_q = (\n", " df_q.groupby(\"tipo_entidade\", group_keys=False)\n", " .apply(lambda g: g.sample(min(len(g), 50), random_state=42))\n", " )\n", " df_q[\"prioridade_revisao\"] = 1.0\n", " else:\n", " raise ValueError(f\"MODO_TRIAGEM inválido: {MODO_TRIAGEM}\")\n", "\n", " df_q = df_q.sort_values(\"prioridade_revisao\", ascending=False)\n", " df_q = df_q.head(LIMITE_REVISOES_LLM).reset_index(drop=True)\n", " df_q[\"review_item_id\"] = [f\"rev_{i:07d}\" for i in range(len(df_q))]\n", "\n", " # Anexa texto da nota\n", " texto_map = df_work.set_index(\"row_id\")[TEXT_COL].to_dict()\n", " df_q[\"texto_nota\"] = df_q[\"row_id\"].map(texto_map)\n", "\n", " df_queue = df_q\n", " df_queue.to_parquet(REVIEW_DIR / \"fila_revisao.parquet\", index=False)\n", "\n", " logger.info(f\"Fila de revisão: {len(df_queue):,} entidades (modo={MODO_TRIAGEM})\")\n", " logger.info(f\"Tempo estimado em T4: {len(df_queue) * 1.5 / 60:.1f} min \"\n", " f\"(@ ~1.5s/revisão)\")\n", "\n", " if len(df_queue) > 0:\n", " print(\"\\nDistribuição por tipo na fila:\")\n", " display(df_queue[\"tipo_entidade\"].value_counts().rename(\"qtd\").to_frame())\n", " print(\"\\nAmostra (top prioridade):\")\n", " display(df_queue[[\"review_item_id\", \"texto_entidade\", \"tipo_entidade\",\n", " \"score_num\", \"prioridade_revisao\"]].head(10))\n" ] }, { "cell_type": "markdown", "id": "c1fa32b2", "metadata": { "id": "c1fa32b2" }, "source": [ "# Etapa C — Revisão pelo Marco Mini (LLM local)" ] }, { "cell_type": "code", "source": [ "# ============================================================\n", "# BLOCO DE RECUPERAÇÃO — retoma trabalho do Drive\n", "# ============================================================\n", "# Use em sessão NOVA quando você já tem df_entidades, fila_revisao,\n", "# taxonomia e dicionários salvos, e quer pular direto para a etapa LLM.\n", "#\n", "# Após este bloco:\n", "# 1. Rode as células 5, 6, 7, 8, 9 (definições de funções: regex,\n", "# utilitários, entidades_regex, overlaps, pós-processamento).\n", "# Pule 1, 2, 3, 4, 10–14, 16 — já feitas ou desnecessárias.\n", "# 2. Pule para a célula 15 (Setup LLM) e siga até a 27.\n", "\n", "import os, sys, gc, json, time, logging\n", "from pathlib import Path\n", "import pandas as pd\n", "import yaml\n", "import torch # usado pelas células LLM para gerenciar VRAM\n", "from unidecode import unidecode\n", "\n", "logging.basicConfig(\n", " level=logging.INFO,\n", " format=\"%(asctime)s | %(levelname)s | %(message)s\",\n", " datefmt=\"%H:%M:%S\",\n", " force=True,\n", ")\n", "logger = logging.getLogger(\"ner_pipeline\")\n", "\n", "# ------------------------------------------------------------\n", "# 1) Monta o Drive\n", "# ------------------------------------------------------------\n", "if not Path(\"/content/drive/MyDrive\").exists():\n", " from google.colab import drive\n", " drive.mount(\"/content/drive\")\n", "\n", "SAVED_DIR = Path(\"/content/drive/MyDrive/Compartilhados/CommunityNotesNER_SavedWork\")\n", "assert SAVED_DIR.exists(), f\"Diretório não existe: {SAVED_DIR}\"\n", "\n", "# ------------------------------------------------------------\n", "# 2) Inspeciona o que tem no Drive\n", "# ------------------------------------------------------------\n", "logger.info(f\"Conteúdo de {SAVED_DIR}:\")\n", "for p in sorted(SAVED_DIR.rglob(\"*\")):\n", " if p.is_file():\n", " logger.info(f\" {p.relative_to(SAVED_DIR)} ({p.stat().st_size/1e6:.1f} MB)\")\n", "\n", "# ------------------------------------------------------------\n", "# 3) Localiza arquivos (estrutura plana OU aninhada)\n", "# ------------------------------------------------------------\n", "def _find_one(*patterns, where=SAVED_DIR):\n", " for pat in patterns:\n", " matches = sorted(where.rglob(pat))\n", " if matches:\n", " return matches[0]\n", " return None\n", "\n", "path_taxonomy = _find_one(\"taxonomy_v*.json\", \"taxonomy*.json\")\n", "path_dicts = _find_one(\"dicionarios.yaml\", \"dicionarios.yml\")\n", "path_entidades = _find_one(\"df_entidades_gliner.parquet\", \"df_entidades*.parquet\")\n", "path_fila = _find_one(\"fila_revisao.parquet\", \"fila*.parquet\")\n", "\n", "assert path_taxonomy, f\"Não achei taxonomy*.json em {SAVED_DIR}\"\n", "assert path_dicts, f\"Não achei dicionarios.yaml em {SAVED_DIR}\"\n", "assert path_entidades, f\"Não achei df_entidades*.parquet em {SAVED_DIR}\"\n", "assert path_fila, f\"Não achei fila*.parquet em {SAVED_DIR}\"\n", "\n", "# ------------------------------------------------------------\n", "# 4) Aponta TODOS os paths para o Drive (escritas vão pra lá)\n", "# ------------------------------------------------------------\n", "ARTIFACT_DIR = SAVED_DIR\n", "EXTRACT_DIR = path_entidades.parent\n", "REVIEW_DIR = path_fila.parent\n", "CONFIG_DIR = path_taxonomy.parent\n", "FINAL_DIR = ARTIFACT_DIR / \"final\"\n", "LOG_DIR = ARTIFACT_DIR / \"logs\"\n", "MODEL_DIR = ARTIFACT_DIR / \"models\"\n", "\n", "for p in [REVIEW_DIR, CONFIG_DIR, FINAL_DIR, LOG_DIR, MODEL_DIR]:\n", " p.mkdir(parents=True, exist_ok=True)\n", "\n", "fh = logging.FileHandler(LOG_DIR / \"pipeline.log\", encoding=\"utf-8\")\n", "fh.setFormatter(logging.Formatter(\"%(asctime)s | %(levelname)s | %(message)s\"))\n", "logger.addHandler(fh)\n", "\n", "# ------------------------------------------------------------\n", "# 5) Recarrega taxonomia (substitui as vars da célula 3)\n", "# ------------------------------------------------------------\n", "with open(path_taxonomy, \"r\", encoding=\"utf-8\") as f:\n", " taxonomy_data = json.load(f)\n", "\n", "TAXONOMY = [\n", " (t[\"label_en\"], t[\"label_pt\"], t[\"threshold\"], t[\"papel_padrao\"])\n", " for t in taxonomy_data[\"taxonomy\"]\n", "]\n", "LABEL_TO_PT = {a: b for a, b, _, _ in TAXONOMY}\n", "PT_TO_LABEL = {b: a for a, b, _, _ in TAXONOMY}\n", "LABEL_TO_THRESHOLD = {a: t for a, _, t, _ in TAXONOMY}\n", "PT_TO_PAPEL = {b: p for _, b, _, p in TAXONOMY}\n", "GLINER_LABELS = [a for a, _, _, _ in TAXONOMY]\n", "TIPOS_EVIDENCIA = set(taxonomy_data[\"tipos_evidencia\"])\n", "TIPOS_REGEX_EXTRAS = set(taxonomy_data.get(\"tipos_regex_extras\",\n", " [\"URL_DOMINIO\",\"DATA\",\"VALOR_MONETARIO\",\n", " \"ESTATISTICA\",\"LEI_NORMA\",\"PROCESSO_JUDICIAL\"]))\n", "TIPOS_VALIDOS = set(taxonomy_data.get(\n", " \"tipos_validos\",\n", " list(LABEL_TO_PT.values()) + list(TIPOS_REGEX_EXTRAS)))\n", "THRESHOLD_DEFAULT = taxonomy_data.get(\"threshold_default\", 0.40)\n", "THRESHOLD_GLINER_FLOOR = taxonomy_data.get(\n", " \"threshold_floor\",\n", " min(LABEL_TO_THRESHOLD.values()) - 0.05)\n", "GLINER_MODEL_ID = taxonomy_data.get(\"model_id\", \"urchade/gliner_multi-v2.1\")\n", "\n", "logger.info(f\"Taxonomia: {len(TAXONOMY)} tipos | TIPOS_VALIDOS={len(TIPOS_VALIDOS)}\")\n", "\n", "# ------------------------------------------------------------\n", "# 6) Recarrega dicionários (substitui as vars da célula 4)\n", "# ------------------------------------------------------------\n", "with open(path_dicts, \"r\", encoding=\"utf-8\") as f:\n", " DICTS = yaml.safe_load(f)\n", "\n", "ENTIDADES_GENERICAS = set(DICTS[\"entidades_genericas\"])\n", "\n", "def _build_lookup(canonical_dict):\n", " return {\n", " unidecode(var.lower().strip()): canonical\n", " for canonical, vars_ in canonical_dict.items()\n", " for var in vars_\n", " }\n", "\n", "PROGRAMAS_LOOKUP = _build_lookup(DICTS[\"programas_publicos\"])\n", "ORGAOS_LOOKUP = _build_lookup(DICTS[\"siglas_orgaos\"])\n", "PARTIDOS_LOOKUP = _build_lookup(DICTS[\"partidos_br\"])\n", "\n", "logger.info(\n", " f\"Dicionários: {len(ENTIDADES_GENERICAS)} genéricas | \"\n", " f\"{len(PROGRAMAS_LOOKUP)} programas | {len(ORGAOS_LOOKUP)} órgãos | \"\n", " f\"{len(PARTIDOS_LOOKUP)} partidos\"\n", ")\n", "\n", "# ------------------------------------------------------------\n", "# 7) Recarrega dataframes\n", "# ------------------------------------------------------------\n", "df_entidades = pd.read_parquet(path_entidades)\n", "df_queue = pd.read_parquet(path_fila)\n", "\n", "logger.info(f\"df_entidades: {len(df_entidades):,} linhas | {df_entidades.shape[1]} colunas\")\n", "logger.info(f\"df_queue: {len(df_queue):,} linhas (fila para LLM)\")\n", "\n", "# Verificações sanitárias\n", "required_cols_ent = {\"entity_id\", \"row_id\", \"texto_entidade\", \"tipo_entidade\",\n", " \"score\", \"fonte_extracao\"}\n", "required_cols_fila = {\"review_item_id\", \"row_id\", \"entity_id\", \"texto_entidade\",\n", " \"tipo_entidade\", \"texto_nota\"}\n", "\n", "missing_ent = required_cols_ent - set(df_entidades.columns)\n", "missing_fila = required_cols_fila - set(df_queue.columns)\n", "\n", "if missing_ent:\n", " logger.warning(f\"⚠ df_entidades sem colunas: {missing_ent}\")\n", "if missing_fila:\n", " logger.warning(f\"⚠ df_queue sem colunas: {missing_fila} \"\n", " f\"(o loop LLM precisa de 'texto_nota')\")\n", "if not missing_ent and not missing_fila:\n", " logger.info(\"✓ Esquemas válidos.\")\n", "\n", "# Constantes esperadas pelos próximos passos\n", "SKIP_LLM = False\n", "TEXT_COL = \"texto_nota\"\n", "ID_COL = \"noteId\" if \"noteId\" in df_queue.columns else \"row_id\"\n", "\n", "# Visão geral da fila\n", "print(\"\\nDistribuição da fila por tipo:\")\n", "display(df_queue[\"tipo_entidade\"].value_counts().rename(\"qtd\").to_frame().head(20))\n", "\n", "cols_show = [c for c in [\"review_item_id\", \"texto_entidade\", \"tipo_entidade\",\n", " \"score_num\", \"prioridade_revisao\"]\n", " if c in df_queue.columns]\n", "print(\"\\nTopo da fila (maior prioridade):\")\n", "display(df_queue[cols_show].head(10))\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", "print(\"✓ ESTADO RECUPERADO\")\n", "print(\"=\" * 60)\n", "print(f\"Paths agora apontam para: {ARTIFACT_DIR}\")\n", "print(f\"Revisões LLM serão salvas em: {REVIEW_DIR}\")\n", "print(f\"\\nPróximo passo: rode células 5–9 (defs de funções) e depois 15+.\")" ], "metadata": { "colab": {}, "id": "OTp6q65CYo3H" }, "id": "OTp6q65CYo3H", "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": null, "id": "63b1fef7", "metadata": { "colab": {}, "id": "63b1fef7" }, "outputs": [], "source": [ "# ============================================================\n", "# CÉLULA 11 — Setup do LLM (Marco Mini local) + sanity checks\n", "# ============================================================\n", "\n", "from google.colab import drive\n", "drive.mount('/content/drive')\n", "\n", "# Descomente na primeira execução de uma sessão nova:\n", "# !pip -q install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124\n", "# !pip -q install huggingface_hub hf_transfer\n", "\n", "import os\n", "import gc\n", "import json\n", "import time\n", "import torch\n", "from pathlib import Path\n", "from llama_cpp import Llama\n", "from huggingface_hub import hf_hub_download\n", "\n", "os.environ[\"HF_HUB_ENABLE_HF_TRANSFER\"] = \"1\"\n", "\n", "# ------------------------------------------------------------\n", "# Configuração\n", "# ------------------------------------------------------------\n", "GGUF_LOCAL_PATH = \"/content/drive/MyDrive/Compartilhados/community_notes_pt/Marco-Mini-Instruct.i1-Q6_K.gguf\"\n", "GGUF_REPO = \"mradermacher/Marco-Mini-Instruct-i1-GGUF\"\n", "GGUF_FALLBACK_FILE = \"Marco-Mini-Instruct.i1-Q6_K.gguf\"\n", "\n", "LLM_CTX = 8192\n", "LLM_MAX_TOKENS = 320\n", "LLM_TEMPERATURE = 0.0\n", "LLM_REPEAT_PENALTY = 1.08\n", "\n", "# ------------------------------------------------------------\n", "# Localiza o GGUF (path manual > busca recursiva > download)\n", "# ------------------------------------------------------------\n", "def find_marco_gguf():\n", " if GGUF_LOCAL_PATH is not None and Path(GGUF_LOCAL_PATH).exists():\n", " return Path(GGUF_LOCAL_PATH)\n", "\n", " roots = [Path(\"/content/drive/MyDrive\"), Path(\"/content\")]\n", " found = []\n", " for root in roots:\n", " if root.exists():\n", " for f in root.rglob(\"*.gguf\"):\n", " if \"Marco-Mini-Instruct\" in f.name:\n", " found.append(f)\n", "\n", " for pref in [\"Q6_K\", \"Q5_K\", \"Q4_K_M\", \"Q4_K\"]:\n", " for f in found:\n", " if pref in f.name:\n", " return f\n", "\n", " return found[0] if found else None\n", "\n", "local_gguf = find_marco_gguf()\n", "\n", "if local_gguf is not None:\n", " MODEL_PATH = str(local_gguf)\n", " size_gb = local_gguf.stat().st_size / 1e9\n", " print(f\"Usando GGUF local: {MODEL_PATH} ({size_gb:.2f} GB)\")\n", "else:\n", " print(\"GGUF local não encontrado. Baixando fallback...\")\n", " if \"MODEL_DIR\" not in globals():\n", " MODEL_DIR = Path(\"/content/community_notes_ner_pipeline/models\")\n", " MODEL_DIR.mkdir(parents=True, exist_ok=True)\n", " MODEL_PATH = hf_hub_download(\n", " repo_id=GGUF_REPO,\n", " filename=GGUF_FALLBACK_FILE,\n", " local_dir=str(MODEL_DIR / \"marco_mini_i1\"),\n", " )\n", "\n", "# ------------------------------------------------------------\n", "# Limpa instância anterior, libera VRAM\n", "# ------------------------------------------------------------\n", "if \"llm\" in globals():\n", " try:\n", " del llm\n", " except Exception:\n", " pass\n", " gc.collect()\n", " if torch.cuda.is_available():\n", " torch.cuda.empty_cache()\n", "\n", "if torch.cuda.is_available():\n", " free_gb, total_gb = (x / 1e9 for x in torch.cuda.mem_get_info())\n", " print(f\"VRAM antes do LLM: livre={free_gb:.1f}GB / total={total_gb:.1f}GB\")\n", "\n", "# ------------------------------------------------------------\n", "# Carrega o modelo\n", "# ------------------------------------------------------------\n", "llm = Llama(\n", " model_path=MODEL_PATH,\n", " n_gpu_layers=-1,\n", " n_ctx=LLM_CTX,\n", " verbose=False,\n", " seed=42,\n", ")\n", "print(f\"Marco Mini carregado: {MODEL_PATH}\")\n", "\n", "if torch.cuda.is_available():\n", " free_gb, total_gb = (x / 1e9 for x in torch.cuda.mem_get_info())\n", " print(f\"VRAM após o LLM: livre={free_gb:.1f}GB / total={total_gb:.1f}GB\")\n", "\n", "# ------------------------------------------------------------\n", "# Flags consumidas por revisar_entidade (célula 12)\n", "# ------------------------------------------------------------\n", "HAS_CHAT_API = hasattr(llm, \"create_chat_completion\")\n", "HAS_JSON_FORMAT = HAS_CHAT_API\n", "\n", "# ------------------------------------------------------------\n", "# Sanity check 1: geração mínima\n", "# ------------------------------------------------------------\n", "print(\"\\n\" + \"-\" * 60)\n", "print(\"Sanity check 1: geração básica\")\n", "print(\"-\" * 60)\n", "t0 = time.time()\n", "try:\n", " _r = llm.create_chat_completion(\n", " messages=[{\"role\": \"user\", \"content\": \"Responda apenas: ok\"}],\n", " max_tokens=5,\n", " temperature=LLM_TEMPERATURE,\n", " )\n", " dt1 = time.time() - t0\n", " saida = _r[\"choices\"][0][\"message\"][\"content\"]\n", " print(f\"OK ({dt1:.2f}s): {saida!r}\")\n", "except Exception as e:\n", " raise RuntimeError(f\"Geração básica falhou: {e}\")\n", "\n", "# ------------------------------------------------------------\n", "# Sanity check 2: forçamento de JSON\n", "# ------------------------------------------------------------\n", "print(\"\\n\" + \"-\" * 60)\n", "print(\"Sanity check 2: response_format=json_object\")\n", "print(\"-\" * 60)\n", "try:\n", " t0 = time.time()\n", " _r = llm.create_chat_completion(\n", " messages=[\n", " {\"role\": \"system\", \"content\": \"Responda apenas com JSON válido.\"},\n", " {\"role\": \"user\", \"content\": 'Devolva: {\"status\": \"ok\", \"n\": 1}'},\n", " ],\n", " response_format={\"type\": \"json_object\"},\n", " max_tokens=30,\n", " temperature=LLM_TEMPERATURE,\n", " )\n", " raw = _r[\"choices\"][0][\"message\"][\"content\"]\n", " parsed = json.loads(raw)\n", " print(f\"OK ({time.time()-t0:.2f}s): {parsed}\")\n", " HAS_JSON_FORMAT = True\n", "except Exception as e:\n", " print(f\"Falhou ({type(e).__name__}: {e})\")\n", " print(\" → cairá para completion raw na revisar_entidade (também funciona)\")\n", " HAS_JSON_FORMAT = False\n", "\n", "# ------------------------------------------------------------\n", "# Estimativa de tempo da fila\n", "# ------------------------------------------------------------\n", "print(\"\\n\" + \"-\" * 60)\n", "print(\"Estimativa\")\n", "print(\"-\" * 60)\n", "print(f\"Flags: HAS_CHAT_API={HAS_CHAT_API} | HAS_JSON_FORMAT={HAS_JSON_FORMAT}\")\n", "\n", "if \"df_queue\" in globals() and len(df_queue) > 0:\n", " # Heurística: revisão real demora ~10x o sanity 1 (prompt maior + JSON estruturado)\n", " est_por_item = max(dt1 * 10, 1.0)\n", " est_total_min = len(df_queue) * est_por_item / 60\n", " print(f\"Fila atual: {len(df_queue):,} entidades\")\n", " print(f\"Estimativa: ~{est_por_item:.1f}s/item → ~{est_total_min:.0f} min total\")\n", " print(f\" (a estimativa real virá no smoke test da revisar_entidade)\")\n", "else:\n", " print(\"df_queue não está em memória — recarregue antes de rodar a fila.\")\n", "\n", "print(\"\\nPróxima célula: defina revisar_entidade e rode o smoke test real.\")" ] }, { "cell_type": "code", "execution_count": null, "id": "8c37810d", "metadata": { "colab": {}, "id": "8c37810d" }, "outputs": [], "source": [ "# ============================================================\n", "# 16) Função de revisão LLM (com retry, JSON validation, few-shot)\n", "# ============================================================\n", "\n", "# Tipos válidos para resposta do LLM (inclui NENHUM e tipos regex)\n", "TIPOS_RESPOSTA = sorted(TIPOS_VALIDOS | {\"NENHUM\"})\n", "\n", "SYSTEM_PROMPT = \"\"\"Você é um revisor especialista em entidades nomeadas de Community Notes em português brasileiro.\n", "\n", "Sua tarefa: revisar UMA entidade extraída automaticamente e decidir o que fazer com ela.\n", "\n", "DECISÕES POSSÍVEIS:\n", "- \"manter\": entidade e tipo corretos\n", "- \"corrigir_tipo\": entidade real, mas tipo errado\n", "- \"corrigir_trecho\": tipo correto, mas texto da entidade está mal delimitado\n", "- \"remover\": falso positivo, termo genérico isolado, ou não é entidade\n", "\n", "REGRAS:\n", "- Termos genéricos isolados (\"governo\", \"imprensa\", \"site\", \"rede social\", \"presidente\" sem nome) → REMOVER\n", "- Programas como Auxílio Brasil, Bolsa Família, SUS, Prouni → PROGRAMA_PUBLICO\n", "- STF, TSE, STJ → ORGAO_JUDICIARIO; PF, PRF → ORGAO_SEGURANCA\n", "- IBGE, Ipea, Anvisa, INSS → ORGAO_PUBLICO\n", "- Veículos específicos (Folha, G1, UOL, CNN Brasil) → VEICULO_MIDIA\n", "- Pessoas com cargo público (presidente Lula, ministro Haddad) → ATOR_POLITICO\n", "- Pessoas mencionadas sem cargo claro → PESSOA_PUBLICA ou PESSOA\n", "- Em caso de dúvida entre PESSOA e PESSOA_PUBLICA, prefira PESSOA_PUBLICA se houver fama nacional\n", "\n", "Responda APENAS com JSON válido no formato:\n", "{\"decisao\": \"...\", \"tipo_final\": \"...\", \"texto_final\": \"...\", \"papel_final\": \"mencao|fonte_ou_evidencia\", \"confianca\": \"alta|media|baixa\", \"justificativa\": \"...\"}\n", "\n", "Se decisao=\"remover\", coloque tipo_final=\"NENHUM\".\"\"\"\n", "\n", "\n", "FEW_SHOT_EXAMPLES = [\n", " {\n", " \"texto\": \"O governo Lula reformulou o Auxilio Brasil para Bolsa Família em 2023.\",\n", " \"entidade\": {\"texto\": \"Auxilio Brasil\", \"tipo\": \"PESSOA\", \"score\": 0.55},\n", " \"resposta\": {\n", " \"decisao\": \"corrigir_tipo\",\n", " \"tipo_final\": \"PROGRAMA_PUBLICO\",\n", " \"texto_final\": \"Auxilio Brasil\",\n", " \"papel_final\": \"mencao\",\n", " \"confianca\": \"alta\",\n", " \"justificativa\": \"É um programa social, não pessoa.\",\n", " },\n", " },\n", " {\n", " \"texto\": \"O governo prometeu mas não cumpriu.\",\n", " \"entidade\": {\"texto\": \"governo\", \"tipo\": \"ORGAO_PUBLICO\", \"score\": 0.42},\n", " \"resposta\": {\n", " \"decisao\": \"remover\",\n", " \"tipo_final\": \"NENHUM\",\n", " \"texto_final\": \"\",\n", " \"papel_final\": \"mencao\",\n", " \"confianca\": \"alta\",\n", " \"justificativa\": \"Termo genérico isolado, sem identificação específica.\",\n", " },\n", " },\n", " {\n", " \"texto\": \"Segundo a Polícia Federal, o esquema movimentou milhões.\",\n", " \"entidade\": {\"texto\": \"Polícia Federal\", \"tipo\": \"ORGANIZACAO\", \"score\": 0.58},\n", " \"resposta\": {\n", " \"decisao\": \"corrigir_tipo\",\n", " \"tipo_final\": \"ORGAO_SEGURANCA\",\n", " \"texto_final\": \"Polícia Federal\",\n", " \"papel_final\": \"mencao\",\n", " \"confianca\": \"alta\",\n", " \"justificativa\": \"É órgão de segurança pública específico.\",\n", " },\n", " },\n", " {\n", " \"texto\": \"Lula afirmou em entrevista coletiva que não houve corrupção.\",\n", " \"entidade\": {\"texto\": \"Lula\", \"tipo\": \"ATOR_POLITICO\", \"score\": 0.62},\n", " \"resposta\": {\n", " \"decisao\": \"manter\",\n", " \"tipo_final\": \"ATOR_POLITICO\",\n", " \"texto_final\": \"Lula\",\n", " \"papel_final\": \"mencao\",\n", " \"confianca\": \"alta\",\n", " \"justificativa\": \"Presidente da República, ator político central.\",\n", " },\n", " },\n", "]\n", "\n", "\n", "def montar_user_prompt(texto, entidade_dict):\n", " blocos = []\n", " for ex in FEW_SHOT_EXAMPLES:\n", " blocos.append(\n", " f\"Texto: {ex['texto']}\\n\"\n", " f\"Entidade: {json.dumps(ex['entidade'], ensure_ascii=False)}\\n\"\n", " f\"Resposta: {json.dumps(ex['resposta'], ensure_ascii=False)}\"\n", " )\n", " blocos.append(\n", " f\"Texto: {texto}\\n\"\n", " f\"Entidade: {json.dumps(entidade_dict, ensure_ascii=False)}\\n\"\n", " f\"Resposta:\"\n", " )\n", " return \"\\n\\n---\\n\\n\".join(blocos)\n", "\n", "\n", "def _call_llm_chat(system_prompt, user_prompt, max_tokens=300):\n", " \"\"\"Tenta usar chat completion com response_format JSON.\"\"\"\n", " return llm.create_chat_completion(\n", " messages=[\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\": user_prompt},\n", " ],\n", " response_format={\"type\": \"json_object\"},\n", " temperature=0.0,\n", " max_tokens=max_tokens,\n", " )\n", "\n", "\n", "def _call_llm_completion(prompt, max_tokens=300):\n", " \"\"\"Fallback: completion raw.\"\"\"\n", " return llm(\n", " prompt=prompt,\n", " temperature=0.0,\n", " max_tokens=max_tokens,\n", " stop=[\"\\n\\n---\", \"\\nTexto:\"],\n", " )\n", "\n", "\n", "def _extrair_json(raw_text):\n", " \"\"\"Extrai primeiro JSON válido de texto bruto.\"\"\"\n", " raw_text = raw_text.strip()\n", " # Tenta parse direto\n", " try:\n", " return json.loads(raw_text)\n", " except Exception:\n", " pass\n", " # Procura primeiro objeto JSON balanceado\n", " start = raw_text.find(\"{\")\n", " if start == -1:\n", " return None\n", " depth = 0\n", " for i in range(start, len(raw_text)):\n", " if raw_text[i] == \"{\":\n", " depth += 1\n", " elif raw_text[i] == \"}\":\n", " depth -= 1\n", " if depth == 0:\n", " try:\n", " return json.loads(raw_text[start:i+1])\n", " except Exception:\n", " return None\n", " return None\n", "\n", "\n", "def _validar_resposta(parsed):\n", " \"\"\"Garante que a resposta tem campos válidos.\"\"\"\n", " if not isinstance(parsed, dict):\n", " return None\n", " decisao = parsed.get(\"decisao\", \"\").lower().strip()\n", " if decisao not in {\"manter\", \"corrigir_tipo\", \"corrigir_trecho\", \"remover\"}:\n", " return None\n", " tipo_final = (parsed.get(\"tipo_final\") or \"NENHUM\").strip().upper()\n", " if decisao == \"remover\":\n", " tipo_final = \"NENHUM\"\n", " elif tipo_final not in TIPOS_RESPOSTA:\n", " return None\n", " papel = (parsed.get(\"papel_final\") or \"mencao\").strip().lower()\n", " if papel not in {\"mencao\", \"fonte_ou_evidencia\"}:\n", " papel = \"mencao\"\n", " confianca = (parsed.get(\"confianca\") or \"media\").strip().lower()\n", " if confianca not in {\"alta\", \"media\", \"baixa\"}:\n", " confianca = \"media\"\n", " return {\n", " \"decisao\": decisao,\n", " \"tipo_final\": tipo_final,\n", " \"texto_final\": str(parsed.get(\"texto_final\") or \"\").strip(),\n", " \"papel_final\": papel,\n", " \"confianca\": confianca,\n", " \"justificativa\": str(parsed.get(\"justificativa\") or \"\").strip()[:200],\n", " }\n", "\n", "\n", "def revisar_entidade(texto_nota, entidade_dict, max_retries=2):\n", " \"\"\"Revisa uma entidade. Retorna (resultado, raw, modo, ok).\"\"\"\n", " # Trunca texto para caber no contexto (deixa folga para few-shots)\n", " texto_truncado = str(texto_nota or \"\")[:1200]\n", "\n", " user_prompt = montar_user_prompt(texto_truncado, entidade_dict)\n", "\n", " for tentativa in range(max_retries + 1):\n", " modo = \"?\"\n", " raw = \"\"\n", " try:\n", " if HAS_JSON_FORMAT:\n", " resp = _call_llm_chat(SYSTEM_PROMPT, user_prompt)\n", " raw = resp[\"choices\"][0][\"message\"][\"content\"]\n", " modo = \"chat_json\"\n", " else:\n", " full_prompt = f\"{SYSTEM_PROMPT}\\n\\n{user_prompt}\"\n", " resp = _call_llm_completion(full_prompt)\n", " raw = resp[\"choices\"][0][\"text\"]\n", " modo = \"completion\"\n", "\n", " parsed = _extrair_json(raw)\n", " validado = _validar_resposta(parsed) if parsed else None\n", "\n", " if validado:\n", " return validado, raw, modo, True\n", "\n", " except Exception as e:\n", " logger.warning(f\"LLM falhou (tentativa {tentativa+1}): {e}\")\n", " raw = f\"ERROR: {e}\"\n", "\n", " # Falhou todas as tentativas - retorna fallback \"manter\"\n", " return (\n", " {\"decisao\": \"manter\", \"tipo_final\": entidade_dict.get(\"tipo\", \"NENHUM\"),\n", " \"texto_final\": entidade_dict.get(\"texto\", \"\"),\n", " \"papel_final\": \"mencao\", \"confianca\": \"baixa\",\n", " \"justificativa\": \"fallback - parse falhou\"},\n", " raw, modo, False,\n", " )\n", "\n", "\n", "# Smoke test do LLM\n", "if not SKIP_LLM and llm is not None and len(df_queue) > 0:\n", " teste_ent = {\"texto\": \"governo\", \"tipo\": \"ORGAO_PUBLICO\", \"score\": 0.4}\n", " teste_texto = \"O governo prometeu reduzir impostos.\"\n", " t0 = time.time()\n", " resultado, raw, modo, ok = revisar_entidade(teste_texto, teste_ent)\n", " dt = time.time() - t0\n", " logger.info(f\"Smoke test LLM ({dt:.2f}s, modo={modo}, ok={ok}):\")\n", " logger.info(f\" Resultado: {resultado}\")\n", " logger.info(f\"Tempo estimado fila completa: {len(df_queue) * dt / 60:.1f} min\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "b971f439", "metadata": { "colab": {}, "id": "b971f439" }, "outputs": [], "source": [ "# ============================================================\n", "# 17) Loop de revisão (incremental, retomável)\n", "# ============================================================\n", "\n", "if not SKIP_LLM and llm is not None and len(df_queue) > 0:\n", " jsonl_path = REVIEW_DIR / \"revisoes_marco.jsonl\"\n", "\n", " # Retoma de onde parou\n", " done = set()\n", " if jsonl_path.exists():\n", " with open(jsonl_path, \"r\", encoding=\"utf-8\") as f:\n", " for line in f:\n", " try:\n", " done.add(json.loads(line)[\"review_item_id\"])\n", " except Exception:\n", " pass\n", " logger.info(f\"Retomando: {len(done)} já revisadas.\")\n", "\n", " pendentes = [r for r in df_queue.to_dict(orient=\"records\")\n", " if r[\"review_item_id\"] not in done]\n", " logger.info(f\"Pendentes: {len(pendentes):,}\")\n", "\n", " if pendentes:\n", " t0 = time.time()\n", " n_ok = n_fail = 0\n", "\n", " with open(jsonl_path, \"a\", encoding=\"utf-8\") as f:\n", " for row in tqdm(pendentes, desc=\"Revisão LLM\"):\n", " entidade = {\n", " \"texto\": row[\"texto_entidade\"],\n", " \"tipo\": row[\"tipo_entidade\"],\n", " \"score\": float(row.get(\"score_num\", 0.0)),\n", " }\n", " texto_nota = row.get(\"texto_nota\") or \"\"\n", "\n", " resultado, raw, modo, ok = revisar_entidade(texto_nota, entidade)\n", "\n", " registro = {\n", " \"review_item_id\": row[\"review_item_id\"],\n", " \"row_id\": int(row[\"row_id\"]),\n", " \"entity_id\": row.get(\"entity_id\"),\n", " ID_COL: str(row.get(ID_COL, \"\")),\n", " \"texto_entidade_original\": row[\"texto_entidade\"],\n", " \"tipo_entidade_original\": row[\"tipo_entidade\"],\n", " \"score_original\": float(row.get(\"score_num\", 0.0)),\n", " \"modo_llm\": modo,\n", " \"parse_ok\": ok,\n", " \"raw_llm\": raw[:1000], # trunca para parquet\n", " **resultado,\n", " }\n", " f.write(json.dumps(registro, ensure_ascii=False) + \"\\n\")\n", " f.flush()\n", "\n", " if ok:\n", " n_ok += 1\n", " else:\n", " n_fail += 1\n", "\n", " dt = time.time() - t0\n", " logger.info(f\"Revisão concluída: {len(pendentes)} em {dt/60:.1f} min \"\n", " f\"({dt/len(pendentes):.2f}s/item)\")\n", " logger.info(f\"Sucessos: {n_ok} | Falhas parse: {n_fail}\")\n", " else:\n", " logger.info(\"Nada pendente.\")\n", "else:\n", " logger.info(\"Pulando loop de revisão.\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "c78220ea", "metadata": { "id": "c78220ea" }, "outputs": [], "source": [ "# ============================================================\n", "# 18) Consolidar revisões em parquet\n", "# ============================================================\n", "\n", "if not SKIP_LLM and (REVIEW_DIR / \"revisoes_marco.jsonl\").exists():\n", " jsonl_path = REVIEW_DIR / \"revisoes_marco.jsonl\"\n", " revs = []\n", " with open(jsonl_path, \"r\", encoding=\"utf-8\") as f:\n", " for line in f:\n", " try:\n", " revs.append(json.loads(line))\n", " except Exception:\n", " pass\n", "\n", " df_revisoes = pd.DataFrame(revs)\n", " if len(df_revisoes) > 0:\n", " # Dedup mantendo a última revisão por item\n", " df_revisoes = df_revisoes.drop_duplicates(\n", " subset=[\"review_item_id\"], keep=\"last\"\n", " ).reset_index(drop=True)\n", "\n", " df_revisoes.to_parquet(REVIEW_DIR / \"revisoes_marco.parquet\", index=False)\n", " df_revisoes.to_csv(REVIEW_DIR / \"revisoes_marco.csv\",\n", " index=False, encoding=\"utf-8-sig\")\n", "\n", " logger.info(f\"Revisões consolidadas: {len(df_revisoes):,}\")\n", "\n", " if len(df_revisoes) > 0 and \"decisao\" in df_revisoes.columns:\n", " print(\"\\nDecisões do LLM:\")\n", " display(df_revisoes[\"decisao\"].value_counts(dropna=False)\n", " .rename(\"qtd\").to_frame())\n", "\n", " print(\"\\nConfiança das revisões:\")\n", " display(df_revisoes[\"confianca\"].value_counts(dropna=False)\n", " .rename(\"qtd\").to_frame())\n", "\n", " print(\"\\nTaxa de parse OK:\")\n", " display(df_revisoes[\"parse_ok\"].value_counts(dropna=False)\n", " .rename(\"qtd\").to_frame())\n", "else:\n", " df_revisoes = pd.DataFrame()\n", " logger.info(\"Sem revisões para consolidar.\")\n" ] }, { "cell_type": "markdown", "id": "87fe608e", "metadata": { "id": "87fe608e" }, "source": [ "# Etapa D — Aplicar decisões do LLM\n", "\n", "Cria `df_entidades_final` aplicando as revisões:\n", "- `manter` → entrada inalterada\n", "- `corrigir_tipo` → atualiza `tipo_entidade` (e recomputa `texto_canonico`)\n", "- `corrigir_trecho` → atualiza `texto_entidade`\n", "- `remover` → entrada descartada\n", "- Entidades não revisadas → mantidas como vieram do GLiNER" ] }, { "cell_type": "code", "execution_count": null, "id": "5f2785fd", "metadata": { "id": "5f2785fd" }, "outputs": [], "source": [ "# ============================================================\n", "# 19) Aplicação das decisões do LLM\n", "# ============================================================\n", "\n", "df_final = df_entidades.copy()\n", "\n", "# Marca origem da decisão\n", "df_final[\"revisado_llm\"] = False\n", "df_final[\"decisao_llm\"] = \"nao_revisado\"\n", "df_final[\"confianca_llm\"] = None\n", "df_final[\"tipo_entidade_pre_llm\"] = df_final[\"tipo_entidade\"]\n", "df_final[\"texto_entidade_pre_llm\"] = df_final[\"texto_entidade\"]\n", "\n", "if len(df_revisoes) > 0:\n", " # Indexa revisões por entity_id (a coluna que liga ao df_entidades)\n", " rev_map = df_revisoes.set_index(\"entity_id\").to_dict(orient=\"index\")\n", "\n", " n_manter = n_corrigir_tipo = n_corrigir_trecho = n_remover = 0\n", " drop_indices = []\n", "\n", " for idx, row in df_final.iterrows():\n", " rev = rev_map.get(row[\"entity_id\"])\n", " if rev is None:\n", " continue\n", "\n", " df_final.at[idx, \"revisado_llm\"] = True\n", " df_final.at[idx, \"decisao_llm\"] = rev[\"decisao\"]\n", " df_final.at[idx, \"confianca_llm\"] = rev.get(\"confianca\")\n", "\n", " decisao = rev[\"decisao\"]\n", " if decisao == \"manter\":\n", " n_manter += 1\n", " elif decisao == \"corrigir_tipo\":\n", " tipo_novo = rev.get(\"tipo_final\")\n", " if tipo_novo and tipo_novo in TIPOS_VALIDOS:\n", " df_final.at[idx, \"tipo_entidade\"] = tipo_novo\n", " df_final.at[idx, \"papel_no_texto\"] = rev.get(\"papel_final\", \"mencao\")\n", " # Recomputa canonical com novo tipo\n", " df_final.at[idx, \"texto_canonico\"] = texto_canonico(\n", " row[\"texto_entidade\"], tipo_novo\n", " )\n", " n_corrigir_tipo += 1\n", " elif decisao == \"corrigir_trecho\":\n", " texto_novo = (rev.get(\"texto_final\") or \"\").strip()\n", " if texto_novo:\n", " df_final.at[idx, \"texto_entidade\"] = texto_novo\n", " df_final.at[idx, \"texto_canonico\"] = texto_canonico(\n", " texto_novo, row[\"tipo_entidade\"]\n", " )\n", " n_corrigir_trecho += 1\n", " elif decisao == \"remover\":\n", " drop_indices.append(idx)\n", " n_remover += 1\n", "\n", " if drop_indices:\n", " df_final = df_final.drop(index=drop_indices).reset_index(drop=True)\n", "\n", " logger.info(\n", " f\"Aplicação: manter={n_manter} | corrigir_tipo={n_corrigir_tipo} | \"\n", " f\"corrigir_trecho={n_corrigir_trecho} | remover={n_remover}\"\n", " )\n", " taxa_manter = n_manter / max(len(df_revisoes), 1) * 100\n", " logger.info(f\"Concordância LLM-GLiNER (taxa de 'manter'): {taxa_manter:.1f}%\")\n", "\n", "# Salva versão final\n", "path_final = FINAL_DIR / \"df_entidades_final.parquet\"\n", "path_final_csv = FINAL_DIR / \"df_entidades_final.csv\"\n", "df_final.to_parquet(path_final, index=False)\n", "df_final.to_csv(path_final_csv, index=False, encoding=\"utf-8-sig\")\n", "\n", "logger.info(f\"Antes do LLM: {len(df_entidades):,} entidades\")\n", "logger.info(f\"Depois do LLM: {len(df_final):,} entidades\")\n", "logger.info(f\"Salvo: {path_final}\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "21a6b82a", "metadata": { "id": "21a6b82a" }, "outputs": [], "source": [ "# ============================================================\n", "# 20) Master enriquecida (final)\n", "# ============================================================\n", "\n", "df_master_final = df_work.copy()\n", "\n", "if len(df_final) > 0:\n", " agg = (\n", " df_final.groupby(\"row_id\")\n", " .agg(\n", " qtd_entidades=(\"entity_id\", \"count\"),\n", " tipos_unicos=(\"tipo_entidade\", lambda s: sorted(set(s))),\n", " entidades_canonicas=(\"texto_canonico\", lambda s: sorted(set(s))),\n", " qtd_evidencias=(\"papel_no_texto\",\n", " lambda s: int((s == \"fonte_ou_evidencia\").sum())),\n", " qtd_revisadas_llm=(\"revisado_llm\", \"sum\"),\n", " dominios=(\"dominio\",\n", " lambda s: sorted(set(x for x in s if pd.notna(x) and x))),\n", " )\n", " .reset_index()\n", " )\n", " df_master_final = df_master_final.merge(agg, on=\"row_id\", how=\"left\")\n", "\n", "# Default values para notas sem entidades\n", "for col, default in [\n", " (\"qtd_entidades\", 0), (\"qtd_evidencias\", 0), (\"qtd_revisadas_llm\", 0),\n", "]:\n", " if col in df_master_final.columns:\n", " df_master_final[col] = df_master_final[col].fillna(0).astype(int)\n", " else:\n", " df_master_final[col] = 0\n", "\n", "for col in [\"tipos_unicos\", \"entidades_canonicas\", \"dominios\"]:\n", " if col in df_master_final.columns:\n", " df_master_final[col] = df_master_final[col].apply(\n", " lambda x: x if isinstance(x, list) else []\n", " )\n", " else:\n", " df_master_final[col] = [[] for _ in range(len(df_master_final))]\n", "\n", "path_master = FINAL_DIR / \"df_master_final.parquet\"\n", "df_master_final.to_parquet(path_master, index=False)\n", "logger.info(f\"Master final salva: {path_master}\")\n" ] }, { "cell_type": "markdown", "id": "0df0af5a", "metadata": { "id": "0df0af5a" }, "source": [ "# Etapa E — Análises finais" ] }, { "cell_type": "code", "execution_count": null, "id": "c6236350", "metadata": { "id": "c6236350" }, "outputs": [], "source": [ "# ============================================================\n", "# 21) Análises consolidadas\n", "# ============================================================\n", "\n", "if len(df_final) == 0:\n", " print(\"Sem entidades para analisar.\")\n", "else:\n", " print(\"=\" * 60)\n", " print(\"DISTRIBUIÇÃO POR TIPO (final, pós-LLM)\")\n", " print(\"=\" * 60)\n", " dist = df_final[\"tipo_entidade\"].value_counts().rename(\"qtd\").to_frame()\n", " dist[\"pct\"] = (dist[\"qtd\"] / dist[\"qtd\"].sum() * 100).round(2)\n", " display(dist)\n", "\n", " print(\"\\n\" + \"=\" * 60)\n", " print(\"TOP 30 ENTIDADES CANÔNICAS\")\n", " print(\"=\" * 60)\n", " top_canonico = (\n", " df_final.groupby([\"tipo_entidade\", \"texto_canonico\"])\n", " .size().reset_index(name=\"mencoes\")\n", " .sort_values(\"mencoes\", ascending=False).head(30)\n", " )\n", " display(top_canonico)\n", "\n", " print(\"\\n\" + \"=\" * 60)\n", " print(\"TOP 30 DOMÍNIOS\")\n", " print(\"=\" * 60)\n", " if \"dominio\" in df_final.columns:\n", " display(\n", " df_final.query(\"tipo_entidade == 'URL_DOMINIO'\")\n", " [\"dominio\"].dropna().value_counts().head(30)\n", " .rename(\"qtd\").to_frame()\n", " )\n", "\n", " if len(df_revisoes) > 0:\n", " print(\"\\n\" + \"=\" * 60)\n", " print(\"IMPACTO DA REVISÃO LLM\")\n", " print(\"=\" * 60)\n", "\n", " total_rev = len(df_revisoes)\n", " total_ent = len(df_entidades)\n", " print(f\"\\nEntidades revisadas: {total_rev:,} de {total_ent:,} \"\n", " f\"({total_rev/max(total_ent,1)*100:.1f}%)\")\n", "\n", " print(\"\\nDecisões:\")\n", " display(\n", " df_revisoes[\"decisao\"].value_counts()\n", " .rename(\"qtd\").to_frame()\n", " .assign(pct=lambda d: (d[\"qtd\"]/d[\"qtd\"].sum()*100).round(1))\n", " )\n", "\n", " print(\"\\nTipos mais corrigidos (correções de tipo):\")\n", " corrigidos = df_revisoes[df_revisoes[\"decisao\"] == \"corrigir_tipo\"]\n", " if len(corrigidos) > 0:\n", " display(\n", " corrigidos.groupby([\"tipo_entidade_original\", \"tipo_final\"])\n", " .size().reset_index(name=\"qtd\")\n", " .sort_values(\"qtd\", ascending=False).head(20)\n", " )\n", "\n", " print(\"\\nTipos mais removidos:\")\n", " removidos = df_revisoes[df_revisoes[\"decisao\"] == \"remover\"]\n", " if len(removidos) > 0:\n", " display(\n", " removidos[\"tipo_entidade_original\"]\n", " .value_counts().head(15).rename(\"qtd\").to_frame()\n", " )\n", "\n", " if \"label_3classes\" in df_final.columns:\n", " print(\"\\n\" + \"=\" * 60)\n", " print(\"ENTIDADES POR CLASSE DA NOTA\")\n", " print(\"=\" * 60)\n", " ct = pd.crosstab(\n", " df_final[\"tipo_entidade\"], df_final[\"label_3classes\"], margins=True\n", " )\n", " display(ct)\n", " ct_norm = pd.crosstab(\n", " df_final[\"tipo_entidade\"], df_final[\"label_3classes\"],\n", " normalize=\"columns\",\n", " ).round(3)\n", " display(ct_norm)\n", "\n", " if \"macrotheme_label\" in df_final.columns:\n", " print(\"\\n\" + \"=\" * 60)\n", " print(\"ENTIDADES POR MACROTEMA (top 50)\")\n", " print(\"=\" * 60)\n", " display(\n", " df_final.groupby([\"macrotheme_label\", \"tipo_entidade\"])\n", " .size().reset_index(name=\"qtd\")\n", " .sort_values(\"qtd\", ascending=False).head(50)\n", " )\n", "\n", " print(\"\\n\" + \"=\" * 60)\n", " print(\"NOTAS COM MAIS ENTIDADES (top 10)\")\n", " print(\"=\" * 60)\n", " cols = [c for c in [ID_COL, TEXT_COL, \"qtd_entidades\", \"qtd_evidencias\",\n", " \"qtd_revisadas_llm\", \"tipos_unicos\"]\n", " if c in df_master_final.columns]\n", " display(\n", " df_master_final.sort_values(\"qtd_entidades\", ascending=False)\n", " [cols].head(10)\n", " )\n" ] }, { "cell_type": "code", "execution_count": null, "id": "a7eb22dc", "metadata": { "id": "a7eb22dc" }, "outputs": [], "source": [ "# ============================================================\n", "# 22) Manifesto e pacote final\n", "# ============================================================\n", "\n", "manifest = {\n", " \"project\": \"community_notes_ner_ptbr\",\n", " \"version\": \"pipeline_v4\",\n", " \"text_col\": TEXT_COL,\n", " \"id_col\": ID_COL,\n", " \"limit_rows\": LIMIT_ROWS,\n", 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str(CONFIG_DIR),\n", " \"log_dir\": str(LOG_DIR),\n", " },\n", " \"created_at\": pd.Timestamp.utcnow().isoformat(),\n", "}\n", "\n", "manifest_path = ARTIFACT_DIR / \"manifest_v4.json\"\n", "with open(manifest_path, \"w\", encoding=\"utf-8\") as f:\n", " json.dump(manifest, f, ensure_ascii=False, indent=2, default=str)\n", "\n", "# Pacote final (exclui modelo .gguf que é grande)\n", "import tarfile\n", "archive_path = str(ARTIFACT_DIR) + \"_v4.tar.gz\"\n", "with tarfile.open(archive_path, \"w:gz\") as tar:\n", " for item in ARTIFACT_DIR.rglob(\"*\"):\n", " if item.is_file() and \"models/\" not in str(item):\n", " tar.add(item, arcname=item.relative_to(ARTIFACT_DIR.parent))\n", "\n", "logger.info(f\"Manifesto: {manifest_path}\")\n", "logger.info(f\"Pacote (sem modelo): {archive_path}\")\n", "logger.info(f\"Tamanho do pacote: {Path(archive_path).stat().st_size / 1e6:.1f} MB\")\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", "print(\"PIPELINE COMPLETO ✓\")\n", "print(\"=\" * 60)\n", 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