324 lines
9.4 KiB
Plaintext
324 lines
9.4 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": 15,
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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"
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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": 10,
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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": null,
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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_codexglue_to_beir(input_file):\n",
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" corpus, queries, qrels = {}, {}, {}\n",
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" with open(input_file, 'r') as f:\n",
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" for i, line in enumerate(f):\n",
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" data = json.loads(line)\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 nuestro documento (Corpus)\n",
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" corpus[docid] = {\"title\": \"\", \"text\": data['code']}\n",
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" # El docstring es nuestra consulta (Query)\n",
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" queries[queryid] = data['docstring']\n",
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" # En CodeXGLUE, la consulta i corresponde al 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\n",
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"\n",
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"# Carga tus datos (ejemplo con el set de test de AdvTest)\n",
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"corpus, queries, qrels = convert_codexglue_to_beir(\"test.jsonl\")\n"
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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": 11,
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"id": "230aae25",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "1915c67ec20f4806b30b48eff9a132e2",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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" 0%| | 0/5183 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"dataset=\"scifact\"\n",
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"url=f\"https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/{dataset}.zip\"\n",
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"data_path=util.download_and_unzip(url, out_dir=\"datasets\")\n",
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"corpus, queries, qrels=GenericDataLoader(data_path).load(split=\"test\")"
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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": 12,
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"id": "514540af",
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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': 0.56333, 'NDCG@3': 0.64367, 'NDCG@5': 0.66577, 'NDCG@10': 0.68551, 'NDCG@100': 0.71285}\n",
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"MAP: {'MAP@1': 0.52994, 'MAP@3': 0.6117, 'MAP@5': 0.62815, 'MAP@10': 0.6383, 'MAP@100': 0.64466}\n",
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"Recall: {'Recall@1': 0.52994, 'Recall@3': 0.7035, 'Recall@5': 0.75967, 'Recall@10': 0.81611, 'Recall@100': 0.94}\n",
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"Precision: {'P@1': 0.56333, 'P@3': 0.25889, 'P@5': 0.17067, 'P@10': 0.093, 'P@100': 0.0107}\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=\"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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"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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"results = evaluator.retrieve(corpus, queries)\n",
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"ndcg, _map, recall, precision = evaluator.evaluate(\n",
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" qrels, results, [1, 3, 5, 10, 100]\n",
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")\n",
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"\n",
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"print(\"NDCG:\", ndcg)\n",
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"print(\"MAP:\", _map)\n",
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"print(\"Recall:\", recall)\n",
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"print(\"Precision:\", precision)"
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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": 13,
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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_qwen2 = 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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"retriever_qwen_2 = DenseRetrievalExactSearch(model_qwen2, batch_size=64)\n",
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"evaluator_qwen_2 = EvaluateRetrieval(retriever_qwen_2, score_function=\"cos_sim\")\n",
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"\n",
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"results_qwen_2 = evaluator_qwen_2.retrieve(corpus, queries)\n",
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"ndcg_qwen_2, _map_qwen_2, recall_qwen_2, precision_qwen_2 = evaluator_qwen_2.evaluate(\n",
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" qrels, results_qwen_2, [1, 3, 5, 10, 100]\n",
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")\n",
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"\n",
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"print(\"NDCG:\", ndcg_qwen_2)\n",
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"print(\"MAP:\", _map_qwen_2)\n",
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"print(\"Recall:\", recall_qwen_2)\n",
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"print(\"Precision:\", precision_qwen_2)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b9402837",
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"metadata": {},
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"source": [
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"# Save 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": 16,
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"id": "c281d5e1",
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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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"Resultados guardados en /home/pseco/VsCodeProjects/assistance-engine/data/interim/beir_Scifact_results.json\n"
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]
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}
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],
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"source": [
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"results_data = {\n",
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" \"qwen3-0.6B-emb:latest\": {\n",
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" \"NDCG\": ndcg,\n",
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" \"MAP\": _map,\n",
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" \"Recall\": recall,\n",
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" \"Precision\": precision,\n",
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" },\n",
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" \"qwen2.5:1.5b\": {\n",
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" \"NDCG\": ndcg_qwen_2,\n",
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" \"MAP\": _map_qwen_2,\n",
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" \"Recall\": recall_qwen_2,\n",
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" \"Precision\": precision_qwen_2,\n",
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" }\n",
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"}\n",
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"\n",
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"output_file = \"/home/pseco/VsCodeProjects/assistance-engine/data/interim/beir_Scifact_results.json\"\n",
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"with open(output_file, \"w\") as f:\n",
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" json.dump(results_data, f, indent=2)\n",
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"\n",
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"print(f\"Resultados guardados en {output_file}\")"
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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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