Extractive question-answering sentence transformer es.ipynb

{"cells":[{"cell_type":"markdown","id":"split-aluminum","metadata":{"id":"split-aluminum","papermill":{"duration":0.048394,"end_time":"2021-04-15T21:06:39.560571","exception":false,"start_time":"2021-04-15T21:06:39.512177","status":"completed"},"tags":[]},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pinecone-io/examples/blob/master/search/question-answering/extractive-question-answering.ipynb) [![Open nbviewer](https://raw.githubusercontent.com/pinecone-io/examples/master/assets/nbviewer-shield.svg)](https://nbviewer.org/github/pinecone-io/examples/blob/master/search/question-answering/extractive-question-answering.ipynb)\n","\n","# Extractive Question Answering with Sentence Transformers in Spanish"]},{"cell_type":"markdown","id":"prospective-turner","metadata":{"id":"prospective-turner","papermill":{"duration":0.045573,"end_time":"2021-04-15T21:06:39.651272","exception":false,"start_time":"2021-04-15T21:06:39.605699","status":"completed"},"tags":[]},"source":["This notebook demonstrates how you can build an extractive question-answering application in spanish. This demo components are:\n","\n","- A vector index to store and run semantic search in Pinecone vector db\n","- A retriever model for embedding our pieces of texts to become our context\n","- A reader model for QA tasks to extract answers for our questions and context.\n","\n","We will use the **SQuAD dataset**, which consists of **questions** and **context** paragraphs containing question **answers**. We generate embeddings for the context passages using the retriever, index them in the vector database, and query with semantic search to retrieve the top k most relevant contexts containing potential answers to our question. We then use the reader model to extract the answers from the returned contexts."]},{"cell_type":"markdown","id":"oC3GG-dWkZJ6","metadata":{"id":"oC3GG-dWkZJ6"},"source":["Let's get started by installing the packages needed for notebook to run:"]},{"cell_type":"markdown","id":"terminal-export","metadata":{"id":"terminal-export","papermill":{"duration":0.044413,"end_time":"2021-04-15T21:06:39.741951","exception":false,"start_time":"2021-04-15T21:06:39.697538","status":"completed"},"tags":[]},"source":["# Install Dependencies"]},{"cell_type":"code","execution_count":null,"id":"expressed-executive","metadata":{"execution":{"iopub.execute_input":"2021-04-15T21:06:39.845309Z","iopub.status.busy":"2021-04-15T21:06:39.842494Z","iopub.status.idle":"2021-04-15T21:08:22.163939Z","shell.execute_reply":"2021-04-15T21:08:22.164616Z"},"id":"expressed-executive","papermill":{"duration":102.376674,"end_time":"2021-04-15T21:08:22.165052","exception":false,"start_time":"2021-04-15T21:06:39.788378","status":"completed"},"tags":[],"executionInfo":{"status":"ok","timestamp":1688486110810,"user_tz":-120,"elapsed":18797,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"59fe7eff-501f-4477-94df-0016a39b17b8"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/486.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m481.3/486.2 kB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m486.2/486.2 kB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.1/179.1 kB\u001b[0m \u001b[31m20.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m23.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m25.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.0/60.0 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m283.7/283.7 kB\u001b[0m \u001b[31m28.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m95.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m77.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m83.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m43.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n"]}],"source":["!pip install -qU datasets pinecone-client sentence-transformers torch python-dotenv"]},{"cell_type":"markdown","source":["## Loading the libraries"],"metadata":{"id":"ZOqg2z3aq2Nn"},"id":"ZOqg2z3aq2Nn"},{"cell_type":"code","source":["from tqdm.auto import tqdm\n","from pprint import pprint\n","\n","import torch\n","from sentence_transformers import SentenceTransformer, util\n","from datasets import load_dataset\n","from transformers import pipeline\n","\n","import pinecone\n","\n","from dotenv import load_dotenv\n","import os"],"metadata":{"id":"H_QYNBfaqvu9"},"id":"H_QYNBfaqvu9","execution_count":null,"outputs":[]},{"cell_type":"markdown","id":"29ad3840","metadata":{"id":"29ad3840"},"source":["# Load Dataset"]},{"cell_type":"markdown","id":"hgIieQukgagu","metadata":{"id":"hgIieQukgagu"},"source":["For this demo we load the the SQUAD dataset in spanish from the HuggingFace Model Hub. We load the dataset into a pandas dataframe and filter the title, question, and context columns, and we drop any duplicate context passages."]},{"cell_type":"code","execution_count":null,"id":"J250IJeh7NIb","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":276,"referenced_widgets":["743eea8b679747019ab53bd315cc4571","5837768e6f5f4ce7a964641b7c5574d6","bf62a9eb7698491da90e9840962187d2","387cb9562abf4a61bf34f1ecf17b0c81","3c9edac2f965474aacd290462bc97dd9","19a46a339f71419d98974e70b6fb2072","de45926d07c34d2f91fb817c14d8a5c4","27c5438c11d4427fa1388fbc60794063","1bb910b40dfe4184970d6c9acf659b94","3d11dc395bc44dac8b210d280b8e14c8","84567b04bde24e5ba44a5372e1971548","d8c13849dbd84bcd8b8b1e145b4a81d6","b0b0a63c8a1b48ee90197bc51866dbfe","04063a6f73964140b2902a221207d641","120755c0cfbf4b66bcbabf968eb50088","baa0a844736d496ea36834a03574df11","6d35dac4e42d4c03b5eb7bc4bd6124a6","f0830e7b255846a0aa30cc994ac0b9a6","52f26c482106400bb0cb76da20a9c920","0f67da19cada4ce9ac0e7052af9e7ade","401ccd28297e4e51804c162a21a96103","5758db2fdc194789b9476d477a3855b5","a47da5a8732b44cab54be9c00f4be22e","b7a12aed620f4cfcaa4c1cba2232cb18","738057ce476c478ba04e9aaf8f58f981","b57cb9a82bd34a168877a5127571e50c","ba9202e1949349eb9087b86706d1d9ab","bf6f8a253edd4104a2f38ccffcde01d0","1f467984cd1d4d57a3f4bda9f77b202d","1036307a97bd4aa9b6669af367e98f9d","2543110e96ec486ba5ba76cd4c46638b","c475b2560285469eb5218948aa76b4fe","0ef575b505ab4261b457a248d2584293","6c7f0b6f6fe7444e842e60f8a9855d6d","c76f54e076f3485e94f4f242e524b980","feb900e6137e450f8d4c235446364157","24d947cb36cd46ef81fe70484650e0f2","a8ae1c6382f74d35ac37ee050f6acf08","fbc68b67b6564436b6e196dfa309a0b8","5d1c7b4a812a4942a2ca1c53842be5cd","e4bb25197c014d55a091b5b4a2e038ca","16663533e3fc439c8c5db9353e759b10","35ce0fb097e446c59b5d734b390758cc","83548b81e49449aba7164be4650c100f","b88688ad98c2448db38af11e4342019c","d8580e1210e7457186fa3965fe351687","cabb5d1de437489fa71830988c8f7d8f","8ab8d7b373714d72b8c30a48435c90ff","471ea2518a9c49059090b9c30a46eb58","d7a3baddd82e488594b4f4a8d0b4a805","a87c018c0eba47c29876d2b0a9b20815","19a942dba85f4524a4a2fbc1b8131258","ccc72c9a8bce49d5b02e20552ef95e67","4afa4f11c15d4ff280e721e6ab5f3afa","945b911fe6f54ebeb08ec03a181e2188","7707ab4363ce427a97da10a63da6b6a7","af52a41740334b21a6c0952a9f205584","b7ee99177d82465e8c51c9436111f772","4b217a1802874df2b47a29095d11a3b3","319c8c78f2014288b6e25668dcc75260","2d694361ef0f48aa845ebf606b79706e","826f3662cffa4af7866df9814cef6a4e","352fd2d78dfd43d3b96484abb9337cf6","b4bf8cbaa9024b8e880e0d56a74e66f5","e174163e5be346af91ed76f72784aa04","42b6a4e63b7644f79d87d40f3f44c330","cbd65d5cf0464105b8ef4d387bcf0a83","cc795c0624e74ef98ee605287325473d","2e0f173d601b4b73b985e0eae6e1fb50","f5415c8057604cd09642430907ef0e19","14020e1a6fa64874af67121550b09953","9a92f45baa1a46a3b03d976a89568198","55ddf4a71ec44bf59f780941eac436e3","3a2f55216539480380958a0c4a4ae753","c3ca99689dae488b88f227463e6f7da6","7a1ce2bb83b54bfc8747ec29aa7b3755","d4414f9e0852490a9184f06324aa17ed","ba338dc6b4da4d2b82365e8c8fcaf2fb","4d2f849e86f34a3b8f4c12a26b38ad7d","1b7164486b9e4c82b12caf1552ead44c","24dece10724441228d6c74f486c061ad","505099ffda34444dafec005a9aa5e600","28c0b4ce1579415ba0c8d4927d2f4271","b76bf6d4d85940b3bf5458c16b01b780","51bbf7ab12364de0825191c99a2d0659","50aedbab10354c94b9f2858d75ceebc7","8e94b61bf2194bc782f889ba0ea5e7d7","9e2b30b3877042d9a581e1844c6d0578","9bcd96ed03214f87805f44f318183768","9fe398dc284041f5ad25b93e83562a7d","8e8c9e370364477496b6aa5a7c896ca6","d055cb8b260341c190b4b5a3ae7c7a2f","0d8800d43c7740e7b820bd686a1aa95a","dc2f4ed65b564abc8d0f453328e0a8f6","5fd7d586614e4e6cae14c7c61099ea3e","f2a03e47f57a440b90c0f7e9a3a6f671","9e818768ba264e038713a6bc5efaed2a","c5bce72bd29a4fc19fb3d6f30de93cbf","68c8a08755344b05a35f73e5530d84da"]},"id":"J250IJeh7NIb","outputId":"0a26dc91-b3f4-4bb2-fb09-8a09ba891f29","executionInfo":{"status":"ok","timestamp":1688486298202,"user_tz":-120,"elapsed":20866,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/5.48k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"743eea8b679747019ab53bd315cc4571"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading metadata:   0%|          | 0.00/2.08k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d8c13849dbd84bcd8b8b1e145b4a81d6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/6.92k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a47da5a8732b44cab54be9c00f4be22e"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset squad_es/v2.0.0 to /root/.cache/huggingface/datasets/squad_es/v2.0.0/2.0.0/bcada4f600192451443b95e24f609325705c5185b8aad97bffa8bc3784a867ad...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6c7f0b6f6fe7444e842e60f8a9855d6d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/9.79M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b88688ad98c2448db38af11e4342019c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/812k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7707ab4363ce427a97da10a63da6b6a7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cbd65d5cf0464105b8ef4d387bcf0a83"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ba338dc6b4da4d2b82365e8c8fcaf2fb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9bcd96ed03214f87805f44f318183768"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset squad_es downloaded and prepared to /root/.cache/huggingface/datasets/squad_es/v2.0.0/2.0.0/bcada4f600192451443b95e24f609325705c5185b8aad97bffa8bc3784a867ad. Subsequent calls will reuse this data.\n"]}],"source":["# load the squad dataset into a pandas dataframe\n","df = load_dataset(\"squad_es\", 'v2.0.0', split=\"train\").to_pandas()"]},{"cell_type":"markdown","source":["Lets inspect our database, we always need to analyze and undestand what we are working with"],"metadata":{"id":"ZjqkcicOJRNs"},"id":"ZjqkcicOJRNs"},{"cell_type":"code","source":["df.head(5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"ZZ-7SURPJMt7","executionInfo":{"status":"ok","timestamp":1688486307064,"user_tz":-120,"elapsed":272,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"7d33d832-b469-411a-bc23-d34add6b4f18"},"id":"ZZ-7SURPJMt7","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                         id            title  \\\n","0  56be85543aeaaa14008c9063  Beyoncé Knowles   \n","1  56be85543aeaaa14008c9065  Beyoncé Knowles   \n","2  56be85543aeaaa14008c9066  Beyoncé Knowles   \n","3  56bf6b0f3aeaaa14008c9601  Beyoncé Knowles   \n","4  56bf6b0f3aeaaa14008c9602  Beyoncé Knowles   \n","\n","                                             context  \\\n","0  Beyoncé Giselle Knowles-Carter (nacida el 4 de...   \n","1  Beyoncé Giselle Knowles-Carter (nacida el 4 de...   \n","2  Beyoncé Giselle Knowles-Carter (nacida el 4 de...   \n","3  Beyoncé Giselle Knowles-Carter (nacida el 4 de...   \n","4  Beyoncé Giselle Knowles-Carter (nacida el 4 de...   \n","\n","                                            question  \\\n","0             ¿Cuándo Beyonce comenzó a ser popular?   \n","1    ¿En qué áreas compitió Beyonce cuando era niña?   \n","2  ¿Cuándo Beyonce dejó Destiny 's Child y se con...   \n","3            ¿En qué ciudad y estado creció Beyonce?   \n","4             ¿En qué década Beyonce se hizo famoso?   \n","\n","                                             answers  \n","0  {'text': ['a finales de 1990'], 'answer_start'...  \n","1       {'text': ['canto y'], 'answer_start': [197]}  \n","2          {'text': ['(200'], 'answer_start': [527]}  \n","3  {'text': ['Houston, Texas'], 'answer_start': [...  \n","4  {'text': ['finales de 1990'], 'answer_start': ...  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Sala","userId":"13317831924226771761"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["             title                                            context\n","0  Beyoncé Knowles  Beyoncé Giselle Knowles-Carter (nacida el 4 de...\n","1  Beyoncé Knowles  Después de la disolución de Destiny 's Child e...\n","2  Beyoncé Knowles  Una auto-descrita \"feminista moderna\", Beyoncé...\n","3  Beyoncé Knowles  Beyoncé Giselle Knowles nació en Houston, Texa...\n","4  Beyoncé Knowles  Beyoncé asistió a St. Mary 's Elementary Schoo...\n","5  Beyoncé Knowles  A los ocho años, Knowles y su amiga de la infa...\n","6  Beyoncé Knowles  El grupo cambió su nombre a Destiny 's Child e...\n","7  Beyoncé Knowles  LeToya Luckett y Roberson quedaron descontento...\n","8  Beyoncé Knowles  Los miembros restantes de la banda grabaron \"I...\n","9  Beyoncé Knowles  En julio de 2002, Beyoncé continuó su carrera ..."],"text/html":["\n","  <div id=\"df-0a434aa3-2ee1-44cd-bac7-77600d558c47\">\n"," 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The retriever will generate the embeddings for all context passages (context vectors/embeddings) and from our questions (query vector/embedding).\n","\n","The retriever will generate embeddings in a way that the questions and context passages containing answers to our questions are nearby in the vector space. We can use cosine similarity to calculate the similarity between the query and context embeddings to find the context passages that contain potential answers to our question.\n","\n","We will use a multilingual SentenceTransformer model named ``sentence-transformers/distiluse-base-multilingual-cased-v1`` trained on +50 languages."]},{"cell_type":"code","execution_count":null,"id":"31a85bb3","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":552,"referenced_widgets":["15a0bc7287cf4e3897c1d88ba6720b4a","9a87f174324f4a14b142b47cdd01d88a","4c3ed0ac01e44c918a68288685d392e4","99709ae92770416f8cbf635c36d73bad","8db34dde923e4139b235f40826819448","3a0601aaaf9f46fc8c9a118c0c75a3ff","af440b31c6e54c0c91de6fb4b8122a82","59ad44861e3a4025a41e64b54e9368b0","669b20884b384d57a732292f91edfbaa","7786dd0817d84e90905a376bc273a960","fe35214a05344fbab01d9107704e54f1","c20e8936210448f7b247138e48b642f3","446b83ca968c4bd7a5fd1452274dc571","bec0412b7eee4613b1392a64e3e85ecd","c2c9bf75bd754c0eb75bdb0dab9e72e3","79bb2098b8014c4b98e474838abd8c3f","d8e17016216b4e1187c30970837ad18b","02371f83306847c5902ba6726eb276da","23d3fcf022264e679187a057c766aa4d","dc4ed44a45db40e3bb6cf15006a11197","1a6019a534684ae18f1cc7f02a1fd73b","5a729f3022b74b78a4879559b0ef2972","e72f4411c0194981ad5954d94b3f32c1","3bae158daf0a4dd58cc208cb513bac9b","98c1cc2edbaf49a481f75411e96b765e","0670815de3544b4eafb679dc8894e5a7","3a597d97a8c8486ebde1007f83b2a635","56f8b9acb383499ea53876f6a9caab4b","f0a6e27bd83341669749119702ba3859","2ecdaec617b0474e8c54a69627f9d492","e8dd5beeb50c46deb6b0f3ea0a59b54c","66a81233ffe74ca98a8c7e845d13a55a","a38a805870be45baa857b47bc39f4750","cd3f8780a3f74472854ad78582314a4b","909f2bd11eba4e9ea44b46a62c5c3009","4692378fad0a455da61e11f5856b357a","4279c22731754776aa4453e322a39d19","606b4ce8a28f4608aff399b04112f3ae","654c5dce6ff140909dbe3e1dbf9ff4c0","06a853ec6cac43f7b60834c9a69c32b6","6893f6295e7b41318138df85d7197340","b6579162053046c9ac54ae365979c108","bc37ca061465412485f5252baa849a9a","ebe80fb5aa2e469f8f59d8fbd22a1556","7c5403d7bee94b8aa30c0581415e62f0","1cee0597371e43e4a52e223083e8037f","d65de8865b4b46b38613a130f0bf0ba1","6b0ff161bcc743c9a919a6982e2c641b","cd23c8bf3da043de80b1ac5e50ac0c3c","a6e8f7234d424543b3f18500b4b6e745","51eca4f9b38b419c86fabd6313bf40ed","030af79f1e4d418c94a11daf1e53872d","77a9179cff7149bbba1662c21a6e9ad6","e678eb815d3241d0874de997d3e4b836","d00925eb18234704b3ffc4c4c8e193b2","85b0e603dcd646a1abbbf92e957f3ff0","8cf647f41cb24039bca0d7d4161342eb","03796c14349f48b7a5c399c40f699178","f936a2191d1641b9beba7b0020a1bc91","4d5657218a284c65ba7e62c7383217b1","6a484109e286439697e0db4bc5e069ea","a0cc82af3ed8410d9b487ca57df52b04","71a77cb26e8a4709b10b36fb4ba43c7c","9f9a9771914140d2aa57850c46f76d9b","3b20f401f1ff4c87a62f514929d46b78","ff548f47a9aa424e95ac3749627ca282","2f4a2f9fbbd4440c839f30cbf72f897a","d88991eca3384171a60dbb44dee6f833","ebec3b955f184871a484fd5a7333a71e","825db0d416874b99b814706edbfda7a1","972509bfcae54761b5ea34724cbd0425","715a11e66e394ad5b959be0c5a5656d4","579fb12012364e7abb1312c2d507be8b","2bd2b79f75234f6b988bc8658ef447c4","51fad074d7f14266aa87f3985eae6b30","d62ffab82f414388b38f6003cd4f622e","d3dc84c90a244d43a8be77d3dc4cfb17","9f931737eec44058ab9d602ec2fad2b4","4f31fbd04da84989b70a4dd1bf7a82a6","cfd121d0560f445ba574179716dee2da","33c2d49e6e46444c9485c68f88f6ad18","bdda8ea50c434536996475e877256636","b9e52754c15e4a1aa95e5106686aa20c","9c9018daeee248a2bd3cbee1b7c29149","5320f40ceb4049ac9695f17774069258","439a0fdc3c7f4802ae119a526d53fec7","b1741729d3de437b8b7e11ee10c5210f","3384ed8493df401ba523a7989b955b26","143fd6f338404d22b568e1ef991a6636","2414698a3849418c99f0471bbb1a2326","2375fbc687b04e7fb90fdfa42ab20caf","fcc2e3902a2249fe82793659345347b1","bc40ae7d5b8b4e91988ce7e92227799b","de58dc334c3a47c882060004a00f4101","8378338670674b98adadfd95148c596b","10fa9cb4e4874bf6b3ddf50658aff377","0ed13a2093434613b1ac8e5c30740546","0e0e844e6c0e4b1aafdeefe0255ef7ed","b03799eed02444648d968d9c77ebd3ef","21e6ea7c8c5046bca3dcf6968bf6d822","7c47b7c178f9469188ab753b036014a2","5ec9402571e748c6b39ce16380070a0a","f5cb204600c242cb8daa1864e5faec1d","d243e31a7e12498caa49b3664f3d4ebf","f4afd02781734af3993908fd4a2826e9","f9dfd910fb4846168b7684588f66ac70","9820990b4b1048dfa3fa9684c8dd66a2","0c63e55a0fc84b27bd1ad6ce1515f9e0","3e98c07d813144b9a9b5591000eaa39b","06fb8243ffe5466191cc9a019e1160d2","3d7fb86e76904f9e8bfbaba0a55e01c4","1f26c600b0414551b377c5dd98076dba","1beac41a3f214efb9970974f8a2370e6","2c760f3d8a8a486c89bae80c2c91ed14","56c2c2808416493289983d4546769568","4027c89120994bcfa7c21a9b86be8508","d5415e7823914705b8eab24ef2f4e636","c9af4eecccdc43c48e9c608ffaeeba05","abb1a7cdea70428fad543d19d6c1aa3f","852240af553544b0a488b20f5114831c","b0873ef8a14a4bca963c7478ce58535c","82af05599e8a4e27bbfca0704e3e71f4","492f65b9fce640968f827ffe5daff8ea","ea6dd5b2a9c3403b8bf820cd977579c2","da460e90bcc9419d975e0e15a41b35a4","a2a274a9e2314e0b97bd8ed916894cba","70d0b4ad8e4745ff9d5c48d98f600fce","8ef943243bc848ada971e4001ee93ac7","8c3bf686df694b3cabff11c54c2ca89f","885c550754454a7ba9f6fe7704941b98","0a0a8356092841a48c386bdd6b170420","eb40c6ae72954d37887d7a40bb55e32a","ac2909678b2946c7afaa6be15f8a5833","d1e2c8daa4414dd6bbdf951077329695","0016427fd236490aa31ec04f05ae21c4","60861dddcf3440a291d342f50b212f5a","a3d154f9d2c54a85a2d855610ccd61fd","7ae3102b81eb487a8585037fb27021b8","fda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Muñoz Sala","userId":"13317831924226771761"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)5f450/.gitattributes:   0%|          | 0.00/690 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"15a0bc7287cf4e3897c1d88ba6720b4a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c20e8936210448f7b247138e48b642f3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/2_Dense/config.json:   0%|          | 0.00/114 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e72f4411c0194981ad5954d94b3f32c1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/1.58M 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?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ac2909678b2946c7afaa6be15f8a5833"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)465f450/modules.json:   0%|          | 0.00/341 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7fa1629c549f4381b6b1f058f930cef0"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["SentenceTransformer(\n","  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel \n","  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n","  (2): Dense({'in_features': 768, 'out_features': 512, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})\n",")"]},"metadata":{},"execution_count":6}],"source":["# set device to GPU if available\n","device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n","# load the retriever model from huggingface model hub\n","retriever = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v1', device=device)\n","retriever"]},{"cell_type":"markdown","source":["From the model final description we know that our embeddings will be 512 tokens length, we need this info to create our vector index in the vector database that we need to store the context passages that we will use to extract the answers."],"metadata":{"id":"dO__yFWwKda7"},"id":"dO__yFWwKda7"},{"cell_type":"code","source":["# Our sentences to encode\n","sentences = [\"This is an example sentence\", \"Esta es una sentencia de ejemplo\"]\n","# Create the embeddings for our sentences\n","embeddings = retriever.encode(sentences, convert_to_tensor=True)\n","# Show the final embeddings\n","print(embeddings)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"56UU_v82LBnG","executionInfo":{"status":"ok","timestamp":1688311984344,"user_tz":-120,"elapsed":12680,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"77784cc6-5b4d-488c-d832-0d2d407d717a"},"id":"56UU_v82LBnG","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[-0.0389,  0.0185, -0.0407,  ...,  0.0101, -0.0166, -0.0014],\n","        [-0.0206, -0.0221, -0.0081,  ...,  0.0643,  0.0062,  0.0091]],\n","       device='cuda:0')\n"]}]},{"cell_type":"code","source":["# Calculate distance between this two sentences\n","distance = util.pytorch_cos_sim(embeddings[0], embeddings[1])\n","print(distance)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"80MSl02lMR6S","executionInfo":{"status":"ok","timestamp":1688312013402,"user_tz":-120,"elapsed":225,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"d1113adf-6c4a-4e63-9292-c25786d78111"},"id":"80MSl02lMR6S","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[0.7632]], device='cuda:0')\n"]}]},{"cell_type":"markdown","id":"57bbcb57","metadata":{"id":"57bbcb57"},"source":["# Initialize Pinecone Index"]},{"cell_type":"markdown","id":"e24d904c","metadata":{"id":"e24d904c"},"source":["The Pinecone index stores vector representations of our context passages which we can retrieve using another vector (query vector). We first need to initialize our connection to Pinecone to create our vector index. For this, we need a free [API key](\"https://app.pinecone.io/\"), and then we initialize the connection like so:"]},{"cell_type":"code","execution_count":null,"id":"092d1e71","metadata":{"id":"092d1e71"},"outputs":[],"source":["# Load .env file with environment variables\n","load_dotenv()\n","\n","# connect to pinecone environment\n","pinecone.init(\n","    api_key=os.environ[\"PINECONE_API_KEY\"],\n","    environment=\"us-west4-gcp-free\"  # find next to API key in console\n",")"]},{"cell_type":"markdown","id":"58028e12","metadata":{"id":"58028e12"},"source":["Now we create a new index called \"extractive question-answering\". We specify the metric type as \"cosine\" and dimension as 512 because the retriever for these two parameters."]},{"cell_type":"code","execution_count":null,"id":"b3206184","metadata":{"id":"b3206184"},"outputs":[],"source":["# Set the index name\n","index_name = \"extractive-question-answering\"\n","\n","# check if the extractive-question-answering index exists\n","if index_name not in pinecone.list_indexes():\n","    # create the index if it does not exist\n","    pinecone.create_index(\n","        index_name,\n","        dimension=512,\n","        metric=\"cosine\"\n","    )\n","\n","# connect to extractive-question-answering index we created\n","index = pinecone.Index(index_name)"]},{"cell_type":"markdown","id":"8aaad0a2","metadata":{"id":"8aaad0a2"},"source":["# Generate Embeddings and Upsert"]},{"cell_type":"markdown","id":"Hgy7AagJtO_p","metadata":{"id":"Hgy7AagJtO_p"},"source":["Next, we need to generate embeddings for the context passages. We will do this in batches to help us more quickly generate embeddings and upload them to the Pinecone index. When passing the documents to Pinecone, we need an id (a unique value), context embedding, and metadata for each document representing context passages in the dataset. The metadata is a dictionary containing data relevant to our embeddings, such as the article title, context passage, etc."]},{"cell_type":"code","source":["# we will use batches of 64\n","batch_size = 64\n","# In order to minimize compute tim for this demo we limit the number of context passages we will work with\n","max_context = batch_size*50\n","print(\"Max number of context passages:\", max_context)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Hyc2ohfIPi-G","executionInfo":{"status":"ok","timestamp":1688312061428,"user_tz":-120,"elapsed":211,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"dacab6fa-529d-4137-e666-007276cb00ac"},"id":"Hyc2ohfIPi-G","execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Max number of context passages: 3200\n"]}]},{"cell_type":"code","execution_count":null,"id":"a17824ef","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a17824ef","outputId":"ffac9dee-5938-4b30-aef4-163f50a57298","executionInfo":{"status":"ok","timestamp":1688312068534,"user_tz":-120,"elapsed":535,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'dimension': 512,\n"," 'index_fullness': 0.0,\n"," 'namespaces': {'': {'vector_count': 3200}},\n"," 'total_vector_count': 3200}"]},"metadata":{},"execution_count":24}],"source":["# Check if index is empty\n","index_stats_response = index.describe_index_stats()\n","if index_stats_response['total_vector_count']<100:\n","  for i in tqdm(range(0, max_context, batch_size)):\n","      # find end of batch\n","      i_end = min(i+batch_size, max_context)\n","      # extract batch\n","      batch = df.iloc[i:i_end]\n","      # generate embeddings for batch\n","      emb = retriever.encode(batch['context'].tolist()).tolist()\n","      # get metadata\n","      meta = batch.to_dict(orient='records')\n","      # create unique IDs\n","      ids = [f\"{idx}\" for idx in range(i, i_end)]\n","      # add all to upsert list\n","      to_upsert = list(zip(ids, emb, meta))\n","      # upsert/insert these records to pinecone\n","      _ = index.upsert(vectors=to_upsert)\n","\n","# check that we have all vectors in index\n","index.describe_index_stats()"]},{"cell_type":"markdown","id":"YFyBYafuJ0y0","metadata":{"id":"YFyBYafuJ0y0"},"source":["# Initialize the Reader model"]},{"cell_type":"markdown","id":"HgdiLCz5ynOk","metadata":{"id":"HgdiLCz5ynOk"},"source":["We use the `timpal0l/mdeberta-v3-base-squad2` model from the HuggingFace model hub as our reader model. We load this model into a \"question-answering\" pipeline from HuggingFace transformers and feed it our questions and context passages individually. The model gives a prediction for each context we pass through the pipeline."]},{"cell_type":"code","execution_count":null,"id":"hg9XTDkIJzH_","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":226,"referenced_widgets":["fc24a24e35d842b885061f984e9e1f61","2ae38967c58843a28201490f1bf6f4a8","d7c6acc9932f4a9d9ff6ced308dbb7f8","bc0398b9a58c4ba59d638325fa24ea32","9f45a98167df4063b2f0b86c6a24c9b7","cb4edf08ab754b6c8163e72f73f73339","2c7c8dc44d56444cb7767374a712772a","9f7cb49589b94f498d03e8b416eb658c","ee1f80257aeb4b4baaab86d75c7e041c","8930ae5ca9e743e5bf172433cc42d111","01954747909749a9abd1d728bba39116","2b74e02900c74e8694a7e370b38b0b07","c0fb51b43ad1414990ba407c73432580","e24bc9e27b8a441c89fa4bb7098ba290","363376ad57c74ba385a974d237cb6b7c","42e8590172bd478e8a902fa734c16380","ffce47abc5cc4cb08644b9f7cc1a6779","8e8a268f479b4f239d265381954a8e84","2bbf5645a25b41debb1ea512d761f91c","86525c4158f247c886521c8d4d1d435c","b54c724fe77749c498523ddfb23bd4ab","3c2ce405e1b449c288b6cf5d0c2cf81d","cd6a3b84907c4d3cbe4f02ba0ccdd5e1","29dd2a899fa044e7870ebeeaa32a1410","24a310fe7c97409e9850a6c2b9cd4047","9828e245eb6c4b459caac586e040b896","fecd270813984c199e9ee8caef6b85fa","0ba81a56695e4336af41613cc6d34dc0","e0e3d689eece4b96b6e1822c92e696c2","ce9207d83b1e43d4b1e2dd3f03e180c2","91ae28bab443473f9c4464a6865b1941","a0219b67cf944f72944f5170a5871d1e","75f6f3a510b84ffc84b4c84104da5617","644eecbbaafa45ef8c4b29fd8f6b716d","b5849c8744f4471e94d829bea70d6e17","0e934d7ff7f54a44aceb36e7d3c5f987","71044f78653746538133c1cfd4e2246a","86e0a5dc786b4fa0b54d9b4e0f089e74","d5ade2d865734d09b27501fd3de3733f","1350ecdff3844116be23438c14ec7053","bb7974c51a604140a3bf863a655153b5","cacd6ae4c6b94ee58b4bac1dead85b58","d465b99ba97b408ea3b7c06c05629992","059e73a71782485fb6e13fc6f32f5b3a","650b53ac019b41bcbc2d6b83ec37a7ea","24cd9bdd1c1845dd8129c0fe091a1c8c","978d55cd595a4fd78b43dfec2c65977b","2201b14094f84c0ba90851a4810d269f","db203e596bd945e59d1cf40faabd18d8","e2dc132a0c274b799cae6da97f8bc647","25ad57b9e4764c0fae2d08aee48f1604","52d89a409a894fcd8d939b64f9c3218b","a0a6050b873a4af0b11dde4003cc492a","b7ddfb1e97bf447182c9109b623f6a19","eca03cbfe63745fd9a3e0a8828ed38ea","eae36dcf57dd4aa38ed6a8e4c2e11fdc","4536e6f5b3be4c2daa931b688a13808b","c9365f71b9264814a2a1c37865966ae8","03b0d331126d43c7a30a34097335171d","1466aaaf037a477f936e332a495376f2","c31c671434694978b8f7d23050566a0b","c63f50fc275648cfa0aa81aff88158b5","9a512e3449b74120bd8ed50b60bf54c7","4d4770d69bdf4d31b82a23c27c16957c","df9ffd4cbc8246aaaff91e2626985ff5","7d3686faf4dd4380a94cec633da98760"]},"id":"hg9XTDkIJzH_","outputId":"e74a0a7a-28dc-4f6b-c357-c32e7a8668d1","executionInfo":{"status":"ok","timestamp":1688312106242,"user_tz":-120,"elapsed":20593,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/879 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fc24a24e35d842b885061f984e9e1f61"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading model.safetensors:   0%|          | 0.00/1.11G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2b74e02900c74e8694a7e370b38b0b07"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/453 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cd6a3b84907c4d3cbe4f02ba0ccdd5e1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading tokenizer.json:   0%|          | 0.00/16.3M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"644eecbbaafa45ef8c4b29fd8f6b716d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)in/added_tokens.json:   0%|          | 0.00/23.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"650b53ac019b41bcbc2d6b83ec37a7ea"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)cial_tokens_map.json:   0%|          | 0.00/173 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"eae36dcf57dd4aa38ed6a8e4c2e11fdc"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["<transformers.pipelines.question_answering.QuestionAnsweringPipeline at 0x7f3e987d79d0>"]},"metadata":{},"execution_count":25}],"source":["# Set the reader model\n","model_name = 'timpal0l/mdeberta-v3-base-squad2'\n","# load the reader model into a question-answering pipeline\n","reader = pipeline(tokenizer=model_name, model=model_name, task='question-answering', device=device)\n","reader"]},{"cell_type":"markdown","id":"e14d89d6","metadata":{"id":"e14d89d6"},"source":["Let's write some helper functions to execute our queries. The `get_context` function retrieves the context embeddings containing answers to our question from the Pinecone index, and the `extract_answer` function extracts the answers from these context passages."]},{"cell_type":"code","execution_count":null,"id":"lyYaY3QEQiHZ","metadata":{"id":"lyYaY3QEQiHZ"},"outputs":[],"source":["# gets context passages from the pinecone index\n","def get_context(question, top_k):\n","    # generate embeddings for the question\n","    xq = retriever.encode([question]).tolist()\n","    # search pinecone index for context passage with the answer\n","    xc = index.query(xq, top_k=top_k, include_metadata=True)\n","    # extract the context passage from pinecone search result\n","    c = [x[\"metadata\"]['context'] for x in xc[\"matches\"]]\n","    return c"]},{"cell_type":"code","execution_count":null,"id":"Dc9VYOiUQA7B","metadata":{"id":"Dc9VYOiUQA7B"},"outputs":[],"source":["# extracts answer from the context passage\n","def extract_answer(question, context):\n","    results = []\n","    for c in context:\n","        # feed the reader the question and contexts to extract answers\n","        answer = reader(question=question, context=c)\n","        # add the context to answer dict for printing both together\n","        answer[\"context\"] = c\n","        results.append(answer)\n","    # sort the result based on the score from reader model\n","    sorted_result = pprint(sorted(results, key=lambda x: x['score'], reverse=True))\n","    return sorted_result"]},{"cell_type":"markdown","source":["It is time to test our question-answering model and how good our embeddings are, we need the appropiate context to solve the question"],"metadata":{"id":"VXsw4ynFRI-h"},"id":"VXsw4ynFRI-h"},{"cell_type":"code","source":["question = \"¿Donde nació Beyoncé?\"\n","context = get_context(question, top_k = 1)\n","context"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iapWo0gvLbvL","executionInfo":{"status":"ok","timestamp":1688312133521,"user_tz":-120,"elapsed":207,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"d053fce7-63cf-4525-80d6-41ac1bc22d03"},"id":"iapWo0gvLbvL","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Beyoncé Giselle Knowles nació en Houston, Texas, hija de Celestine Ann \"Tina\" Knowles, una peluquera y dueña de salón, y Mathew Knowles, un gerente de ventas de Xerox. El nombre de Beyoncé es un homenaje al apellido de soltera de su madre. La hermana menor de Beyoncé, Solange, también es cantante y ex miembro de Destiny \\'s Child. Mathew es afroamericano, mientras que Tina es de ascendencia criolla de Luisiana (con ascendencia africana, nativa americana, francesa, cajún, y distante irlandesa y española). A través de su madre, Beyoncé es descendiente del líder acadiano Joseph Broussard. Fue criada en un hogar metodista.']"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","source":["We can increase the top_k parameter, for this examples it does not look neccesary but lets do it."],"metadata":{"id":"-YSMlLHFRgRZ"},"id":"-YSMlLHFRgRZ"},{"cell_type":"code","source":["question = \"¿Donde nació Beyoncé?\"\n","context = get_context(question, top_k = 3)\n","context"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I8VirAjkRu4C","executionInfo":{"status":"ok","timestamp":1688312148399,"user_tz":-120,"elapsed":527,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}},"outputId":"0e5ff004-b2d2-45b3-ab0a-c830a63dca85"},"id":"I8VirAjkRu4C","execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['Beyoncé Giselle Knowles nació en Houston, Texas, hija de Celestine Ann \"Tina\" Knowles, una peluquera y dueña de salón, y Mathew Knowles, un gerente de ventas de Xerox. El nombre de Beyoncé es un homenaje al apellido de soltera de su madre. La hermana menor de Beyoncé, Solange, también es cantante y ex miembro de Destiny \\'s Child. Mathew es afroamericano, mientras que Tina es de ascendencia criolla de Luisiana (con ascendencia africana, nativa americana, francesa, cajún, y distante irlandesa y española). A través de su madre, Beyoncé es descendiente del líder acadiano Joseph Broussard. Fue criada en un hogar metodista.',\n"," 'Beyoncé asistió a St. Mary \\'s Elementary School en Fredericksburg, Texas, donde se matriculó en clases de baile. Su talento como cantante fue descubierto cuando la instructora de baile Darlette Johnson comenzó a tararear una canción y ella la terminó, capaz de tocar las notas agudas. El interés de Beyoncé en la música y la realización continuó después de ganar un concurso de talentos de la escuela a los siete años, cantando \"Imagine\" de John Lennon para vencer a los 15 / 16 años de edad. En el otoño de 1990, Knowles se matriculó en la Escuela Primaria Parker, una escuela de música imán en Houston, donde se presentaría con el coro de la escuela. También asistió a la High School for the Performing and Visual Arts y más tarde a la Alief Elsik High School. Beyoncé también fue miembro del coro de la Iglesia Metodista Unida de San Juan como solista durante dos años.',\n"," 'El 7 de enero de 2012, Beyoncé dio a luz a una hija, Blue Ivy Carter, en el Hospital Lenox Hill en Nueva York. Dos días después, Jay Z lanzó \"Glory\", una canción dedicada a su hijo, en su sitio web LifeandTimes.com. La canción detalla las luchas de embarazo de la pareja, incluyendo un aborto espontáneo que Beyoncé sufrió antes de quedar embarazada de Blue Ivy. Los gritos de Blue Ivy se incluyen al final de la canción, y ella fue oficialmente acreditada como \"B.I.C. En él. A los dos días de edad, se convirtió en la persona más joven en aparecer en una lista de Billboard cuando Glory debutó en el Hot R & B / Hip-Hop Songs.']"]},"metadata":{},"execution_count":29}]},{"cell_type":"markdown","id":"heKNVbWQ_LtC","metadata":{"id":"heKNVbWQ_LtC"},"source":["As we can see, the retiever is working fine and gets us the context passage that contains the answer to our question. Now let's use the reader to extract the exact answer from the context passage."]},{"cell_type":"code","execution_count":null,"id":"DQ4GWdbMSjPl","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DQ4GWdbMSjPl","outputId":"445ce1f0-5aaa-4539-ab68-2a5c64f2a577","executionInfo":{"status":"ok","timestamp":1688312154861,"user_tz":-120,"elapsed":525,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[{'answer': ' Houston, Texas,',\n","  'context': 'Beyoncé Giselle Knowles nació en Houston, Texas, hija de '\n","             'Celestine Ann \"Tina\" Knowles, una peluquera y dueña de salón, y '\n","             'Mathew Knowles, un gerente de ventas de Xerox. El nombre de '\n","             'Beyoncé es un homenaje al apellido de soltera de su madre. La '\n","             'hermana menor de Beyoncé, Solange, también es cantante y ex '\n","             \"miembro de Destiny 's Child. Mathew es afroamericano, mientras \"\n","             'que Tina es de ascendencia criolla de Luisiana (con ascendencia '\n","             'africana, nativa americana, francesa, cajún, y distante '\n","             'irlandesa y española). A través de su madre, Beyoncé es '\n","             'descendiente del líder acadiano Joseph Broussard. Fue criada en '\n","             'un hogar metodista.',\n","  'end': 48,\n","  'score': 0.9763014316558838,\n","  'start': 32},\n"," {'answer': ' Fredericksburg, Texas,',\n","  'context': \"Beyoncé asistió a St. Mary 's Elementary School en \"\n","             'Fredericksburg, Texas, donde se matriculó en clases de baile. Su '\n","             'talento como cantante fue descubierto cuando la instructora de '\n","             'baile Darlette Johnson comenzó a tararear una canción y ella la '\n","             'terminó, capaz de tocar las notas agudas. El interés de Beyoncé '\n","             'en la música y la realización continuó después de ganar un '\n","             'concurso de talentos de la escuela a los siete años, cantando '\n","             '\"Imagine\" de John Lennon para vencer a los 15 / 16 años de edad. '\n","             'En el otoño de 1990, Knowles se matriculó en la Escuela Primaria '\n","             'Parker, una escuela de música imán en Houston, donde se '\n","             'presentaría con el coro de la escuela. También asistió a la High '\n","             'School for the Performing and Visual Arts y más tarde a la Alief '\n","             'Elsik High School. Beyoncé también fue miembro del coro de la '\n","             'Iglesia Metodista Unida de San Juan como solista durante dos '\n","             'años.',\n","  'end': 73,\n","  'score': 0.6469601988792419,\n","  'start': 50},\n"," {'answer': ' Hospital Lenox Hill en Nueva York.',\n","  'context': 'El 7 de enero de 2012, Beyoncé dio a luz a una hija, Blue Ivy '\n","             'Carter, en el Hospital Lenox Hill en Nueva York. Dos días '\n","             'después, Jay Z lanzó \"Glory\", una canción dedicada a su hijo, en '\n","             'su sitio web LifeandTimes.com. La canción detalla las luchas de '\n","             'embarazo de la pareja, incluyendo un aborto espontáneo que '\n","             'Beyoncé sufrió antes de quedar embarazada de Blue Ivy. Los '\n","             'gritos de Blue Ivy se incluyen al final de la canción, y ella '\n","             'fue oficialmente acreditada como \"B.I.C. En él. A los dos días '\n","             'de edad, se convirtió en la persona más joven en aparecer en una '\n","             'lista de Billboard cuando Glory debutó en el Hot R & B / Hip-Hop '\n","             'Songs.',\n","  'end': 110,\n","  'score': 0.522406816482544,\n","  'start': 75}]\n"]}],"source":["extract_answer(question, context)"]},{"cell_type":"markdown","id":"fMD_ABuDAyhN","metadata":{"id":"fMD_ABuDAyhN"},"source":["Fine!!! The reader model predicted with 97% accuracy the correct answer *Houston, Texas* as seen from the context passage. Let's run few more queries."]},{"cell_type":"code","execution_count":null,"id":"_4NRgV4mGWoj","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_4NRgV4mGWoj","outputId":"a43638ce-16ad-4978-8635-677324be9d46","executionInfo":{"status":"ok","timestamp":1688312198716,"user_tz":-120,"elapsed":241,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[{'answer': ' Solange,',\n","  'context': 'Beyoncé Giselle Knowles nació en Houston, Texas, hija de '\n","             'Celestine Ann \"Tina\" Knowles, una peluquera y dueña de salón, y '\n","             'Mathew Knowles, un gerente de ventas de Xerox. El nombre de '\n","             'Beyoncé es un homenaje al apellido de soltera de su madre. La '\n","             'hermana menor de Beyoncé, Solange, también es cantante y ex '\n","             \"miembro de Destiny 's Child. Mathew es afroamericano, mientras \"\n","             'que Tina es de ascendencia criolla de Luisiana (con ascendencia '\n","             'africana, nativa americana, francesa, cajún, y distante '\n","             'irlandesa y española). A través de su madre, Beyoncé es '\n","             'descendiente del líder acadiano Joseph Broussard. Fue criada en '\n","             'un hogar metodista.',\n","  'end': 277,\n","  'score': 0.9883753061294556,\n","  'start': 268}]\n"]}],"source":["question = \"¿Cómo se llama la hermana menor de Beyoncé?\"\n","context = get_context(question, top_k=1)\n","extract_answer(question, context)"]},{"cell_type":"markdown","id":"OhCgeny_BVno","metadata":{"id":"OhCgeny_BVno"},"source":["Let's run another question. This time for top 3 context passages from the retriever."]},{"cell_type":"code","execution_count":null,"id":"iXACn71xmett","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iXACn71xmett","outputId":"bcd447e7-94f8-4d5b-ab82-7157f797782b","executionInfo":{"status":"ok","timestamp":1688312234363,"user_tz":-120,"elapsed":570,"user":{"displayName":"Eduardo Muñoz Sala","userId":"13317831924226771761"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["[{'answer': ' Sr. White,',\n","  'context': 'Bond desobedece la orden de M y viaja a Roma para asistir al '\n","             'funeral de Sciarra. Esa noche visita a la viuda de Sciarra, '\n","             'Lucía, quien le habla de Espectro, una organización criminal a '\n","             'la que pertenecía su marido. Bond se infiltra en una reunión del '\n","             'Espectro, donde identifica al líder, Franz Oberhauser. Cuando '\n","             'Oberhauser se dirige a Bond por su nombre, se escapa y es '\n","             'perseguido por el Sr. Hinx, un asesino del Espectro. Moneypenny '\n","             'informa a Bond que la información que recogió lleva al Sr. '\n","             'White, ex miembro de Quantum, una subsidiaria de Spectre. Bond '\n","             'le pide que investigue a Oberhauser, quien fue dado por muerto '\n","             'años antes.',\n","  'end': 498,\n","  'score': 0.3282511830329895,\n","  'start': 487},\n"," {'answer': ' Nine Eyes',\n","  'context': 'Bond y Swann regresan a Londres donde conocen a M, Bill Tanner, '\n","             'Q y Moneypenny; Tienen la intención de detener a C y detener a '\n","             'Nine Eyes de entrar en línea. Swann deja a Bond, diciéndole que '\n","             'no puede ser parte de una vida de espionaje, y posteriormente es '\n","             'secuestrada. En el camino, el grupo es emboscado y Bond es '\n","             'secuestrado, pero el resto sigue adelante con el plan. Después '\n","             'de que Q logra evitar que Nueve Ojos entre en línea, una breve '\n","             'lucha entre M y C termina con este último cayendo a su muerte. '\n","             'Mientras tanto, Bond es llevado al antiguo edificio del MI6, que '\n","             'está programado para su demolición, y se libera. Moviéndose por '\n","             'todo el laberinto en ruinas, se encuentra con un Blofeld '\n","             'desfigurado, que le dice que tiene tres minutos para escapar del '\n","             'edificio antes de que los explosivos sean detonados o mueran '\n","             'tratando de salvar a Swann. Bond encuentra a Swann y los dos '\n","             'escapan en barco cuando el edificio se derrumba. Bond derriba el '\n","             'helicóptero de Blofeld, que se estrella en el puente de '\n","             'Westminster. Mientras Blofeld se arrastra lejos de los restos, '\n","             'Bond se enfrenta a él, pero finalmente lo deja para ser '\n","             'arrestado por M. Bond deja el puente con Swann.',\n","  'end': 136,\n","  'score': 0.13803063333034515,\n","  'start': 126},\n"," {'answer': ' Spectre.',\n","  'context': 'Bond viaja a Austria para encontrar a White, que está muriendo '\n","             'de envenenamiento por talio. Admite estar desencantado con '\n","             'Quantum y le dice a Bond que encuentre y proteja a su hija, la '\n","             'Dra. Esto a su vez lo llevará a Spectre. White luego se suicida. '\n","             'Bond localiza a Swann en el Hoffler Klinik, pero es secuestrada '\n","             'por Hinx. Bond la rescata y los dos se encuentran con Q, quien '\n","             'descubre que el anillo de Sciarra vincula a Oberhauser con las '\n","             'misiones anteriores de Bond, identificando a Le Chiffre, Dominic '\n","             'Greene y Raoul Silva como agentes del Espectro. Swann revela que '\n","             \"L 'Américain es un hotel en Tánger.\",\n","  'end': 225,\n","  'score': 0.024815596640110016,\n","  'start': 216}]\n"]}],"source":["question = \"¿Quién investiga a Bond?\"\n","context = get_context(question, top_k=3)\n","extract_answer(question, context)"]},{"cell_type":"markdown","id":"c9oCWHy0tPpV","metadata":{"id":"c9oCWHy0tPpV"},"source":["The result looks pretty 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