256 lines
7.2 KiB
Plaintext
256 lines
7.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "66cbbaf8",
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"metadata": {},
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"source": [
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"# Libraries"
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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": "c01c19dc",
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Dict, List, Union\n",
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"import numpy as np\n",
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"from langchain_ollama import OllamaEmbeddings\n",
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"from beir.datasets.data_loader import GenericDataLoader\n",
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"from beir.retrieval.search.dense import DenseRetrievalExactSearch\n",
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"from beir.retrieval.evaluation import EvaluateRetrieval\n",
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"from beir import util\n",
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"import json\n",
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"from datasets import load_dataset"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ac011c1c",
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"metadata": {},
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"source": [
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"# Utils"
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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": "b83e7900",
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"metadata": {},
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"outputs": [],
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"source": [
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"class BEIROllamaEmbeddings:\n",
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" \"\"\"\n",
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" Adapter that makes LangChain's OllamaEmbeddings compatible with BEIR.\n",
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" \"\"\"\n",
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"\n",
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" def __init__(\n",
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" self,\n",
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" base_url: str,\n",
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" model: str,\n",
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" batch_size: int = 64,\n",
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" ) -> None:\n",
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" self.batch_size = batch_size\n",
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" self.embeddings = OllamaEmbeddings(\n",
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" base_url=base_url,\n",
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" model=model,\n",
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" )\n",
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"\n",
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" def _batch_embed(self, texts: List[str]) -> np.ndarray:\n",
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" vectors = []\n",
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"\n",
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" for i in range(0, len(texts), self.batch_size):\n",
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" batch = texts[i : i + self.batch_size]\n",
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" batch_vectors = self.embeddings.embed_documents(batch)\n",
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" vectors.extend(batch_vectors)\n",
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"\n",
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" return np.asarray(vectors, dtype=np.float32)\n",
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"\n",
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" def encode_queries(self, queries: List[str], **kwargs) -> np.ndarray:\n",
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" \"\"\"\n",
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" BEIR query encoder\n",
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" \"\"\"\n",
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" return self._batch_embed(queries)\n",
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"\n",
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" def encode_corpus(\n",
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" self,\n",
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" corpus: Union[List[Dict[str, str]], Dict[str, Dict[str, str]]],\n",
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" **kwargs,\n",
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" ) -> np.ndarray:\n",
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" \"\"\"\n",
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" BEIR corpus encoder\n",
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" \"\"\"\n",
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" if isinstance(corpus, dict):\n",
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" corpus = list(corpus.values())\n",
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"\n",
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" texts = []\n",
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" for doc in corpus:\n",
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" title = (doc.get(\"title\") or \"\").strip()\n",
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" text = (doc.get(\"text\") or \"\").strip()\n",
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"\n",
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" if title:\n",
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" texts.append(f\"{title}\\n{text}\")\n",
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" else:\n",
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" texts.append(text)\n",
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"\n",
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" return self._batch_embed(texts)"
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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": 3,
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"id": "af3eb66d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def convert_hf_to_beir(hf_dataset):\n",
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" corpus, queries, qrels = {}, {}, {}\n",
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" \n",
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" for i, data in enumerate(hf_dataset):\n",
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" docid = f\"doc_{i}\"\n",
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" queryid = f\"q_{i}\"\n",
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" \n",
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" # El código es el documento (lo que el agente debe recuperar)\n",
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" corpus[docid] = {\"title\": data.get(\"func_name\", \"\"), \"text\": data['code']}\n",
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" \n",
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" # El docstring es la consulta (lo que el usuario pide)\n",
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" queries[queryid] = data['docstring']\n",
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" \n",
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" # Relación 1 a 1: la query i busca el código i\n",
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" qrels[queryid] = {docid: 1}\n",
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" \n",
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" return corpus, queries, qrels"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c9528fb6",
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"metadata": {},
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"source": [
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"# Data"
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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": 4,
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"id": "230aae25",
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"metadata": {},
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"outputs": [],
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"source": [
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"raw_dataset = load_dataset(\"google/code_x_glue_tc_nl_code_search_adv\", split=\"test\")\n",
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"corpus, queries, qrels = convert_hf_to_beir(raw_dataset)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "13050d31",
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"metadata": {},
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"source": [
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"# Test qwen3-0.6B-emb:latest"
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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": null,
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"id": "514540af",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = BEIROllamaEmbeddings(\n",
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" base_url=\"http://localhost:11434\",\n",
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" model=\"qwen3-0.6B-emb:latest\",\n",
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" batch_size=64,\n",
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")\n",
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"\n",
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"# Inicializar buscador y evaluador\n",
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"retriever = DenseRetrievalExactSearch(model, batch_size=64)\n",
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"evaluator = EvaluateRetrieval(retriever, score_function=\"cos_sim\")\n",
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"\n",
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"# Ejecutar recuperación\n",
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"results = evaluator.retrieve(corpus, queries)\n",
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"\n",
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"# Evaluar métricas (NDCG, MAP, Recall, Precision)\n",
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"ndcg, _map, recall, precision = evaluator.evaluate(\n",
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" qrels, results, [1, 3, 5, 10]\n",
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")\n",
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"\n",
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"print(f\"Resultados para CodeXGLUE:\")\n",
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"print(\"NDCG@10:\", ndcg[\"NDCG@10\"])\n",
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"print(\"Recall@10:\", recall[\"Recall@10\"])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c4e643ca",
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"metadata": {},
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"source": [
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"# Test qwen2.5:1.5b"
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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": null,
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"id": "5ced1c25",
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"metadata": {},
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"outputs": [
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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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"NDCG: {'NDCG@1': 0.02333, 'NDCG@3': 0.03498, 'NDCG@5': 0.0404, 'NDCG@10': 0.04619, 'NDCG@100': 0.07768}\n",
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"MAP: {'MAP@1': 0.02083, 'MAP@3': 0.03083, 'MAP@5': 0.03375, 'MAP@10': 0.03632, 'MAP@100': 0.04123}\n",
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"Recall: {'Recall@1': 0.02083, 'Recall@3': 0.04417, 'Recall@5': 0.0575, 'Recall@10': 0.07417, 'Recall@100': 0.23144}\n",
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"Precision: {'P@1': 0.02333, 'P@3': 0.01556, 'P@5': 0.01267, 'P@10': 0.00833, 'P@100': 0.00277}\n"
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]
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}
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],
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"source": [
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"model = BEIROllamaEmbeddings(\n",
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" base_url=\"http://localhost:11434\",\n",
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" model=\"qwen2.5:1.5b\",\n",
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" batch_size=64,\n",
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")\n",
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"\n",
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"# Inicializar buscador y evaluador\n",
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"retriever = DenseRetrievalExactSearch(model, batch_size=64)\n",
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"evaluator = EvaluateRetrieval(retriever, score_function=\"cos_sim\")\n",
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"\n",
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"# Ejecutar recuperación\n",
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"results = evaluator.retrieve(corpus, queries)\n",
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"\n",
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"# Evaluar métricas (NDCG, MAP, Recall, Precision)\n",
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"ndcg, _map, recall, precision = evaluator.evaluate(\n",
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" qrels, results, [1, 3, 5, 10]\n",
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")\n",
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"\n",
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"print(f\"Resultados para CodeXGLUE:\")\n",
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"print(\"NDCG@10:\", ndcg[\"NDCG@10\"])\n",
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"print(\"Recall@10:\", recall[\"Recall@10\"])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "assistance-engine",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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