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This is a demo for using Univeral Encoder Multilingual Q&A model for question-answer retrieval of text, illustrating the use of question_encoder and response_encoder of the model. We use sentences from SQuAD paragraphs as the demo dataset, each sentence and its context (the text surrounding the sentence) is encoded into high dimension embeddings with the response_encoder. These embeddings are stored in an index built using the simpleneighbors library for question-answer retrieval.
On retrieval a random question is selected from the SQuAD dataset and encoded into high dimension embedding with the question_encoder and query the simpleneighbors index returning a list of approximate nearest neighbors in semantic space.
%%capture # Install the latest Tensorflow version. !pip install -q tensorflow_text !pip install -q simpleneighbors[annoy] !pip install -q nltk !pip install -q tqdm
import json import nltk import os import pprint import random import simpleneighbors import urllib from IPython.display import HTML, display from tqdm.notebook import tqdm import tensorflow.compat.v2 as tf import tensorflow_hub as hub from tensorflow_text import SentencepieceTokenizer nltk.download('punkt') def download_squad(url): return json.load(urllib.request.urlopen(url)) def extract_sentences_from_squad_json(squad): all_sentences =  for data in squad['data']: for paragraph in data['paragraphs']: sentences = nltk.tokenize.sent_tokenize(paragraph['context']) all_sentences.extend(zip(sentences, [paragraph['context']] * len(sentences))) return list(set(all_sentences)) # remove duplicates def extract_questions_from_squad_json(squad): questions =  for data in squad['data']: for paragraph in data['paragraphs']: for qas in paragraph['qas']: if qas['answers']: questions.append((qas['question'], qas['answers']['text'])) return list(set(questions)) def output_with_highlight(text, highlight): output = "<li> " i = text.find(highlight) while True: if i == -1: output += text break output += text[0:i] output += '<b>'+text[i:i+len(highlight)]+'</b>' text = text[i+len(highlight):] i = text.find(highlight) return output + "</li>\n" def display_nearest_neighbors(query_text, answer_text=None): query_embedding = model.signatures['question_encoder'](tf.constant([query_text]))['outputs'] search_results = index.nearest(query_embedding, n=num_results) if answer_text: result_md = ''' <p>Random Question from SQuAD:</p> <p> <b>%s</b></p> <p>Answer:</p> <p> <b>%s</b></p> ''' % (query_text , answer_text) else: result_md = ''' <p>Question:</p> <p> <b>%s</b></p> ''' % query_text result_md += ''' <p>Retrieved sentences : <ol> ''' if answer_text: for s in search_results: result_md += output_with_highlight(s, answer_text) else: for s in search_results: result_md += '<li>' + s + '</li>\n' result_md += "</ol>" display(HTML(result_md))
[nltk_data] Downloading package punkt to /home/kbuilder/nltk_data... [nltk_data] Unzipping tokenizers/punkt.zip.
Run the following code block to download and extract the SQuAD dataset into:
- sentences is a list of (text, context) tuples - each paragraph from the SQuAD dataset are splitted into sentences using nltk library and the sentence and paragraph text forms the (text, context) tuple.
- questions is a list of (question, answer) tuples.
squad_url = 'https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json' squad_json = download_squad(squad_url) sentences = extract_sentences_from_squad_json(squad_json) questions = extract_questions_from_squad_json(squad_json) print("%s sentences, %s questions extracted from SQuAD %s" % (len(sentences), len(questions), squad_url)) print("\nExample sentence and context:\n") sentence = random.choice(sentences) print("sentence:\n") pprint.pprint(sentence) print("\ncontext:\n") pprint.pprint(sentence) print()
10455 sentences, 10552 questions extracted from SQuAD https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json Example sentence and context: sentence: ('The plan that the delegates agreed to was never ratified by the colonial ' 'legislatures nor approved of by the crown.') context: ('An early important political response to the opening of hostilities was the ' 'convening of the Albany Congress in June and July, 1754. The goal of the ' 'congress was to formalize a unified front in trade and negotiations with ' 'various Indians, since allegiance of the various tribes and nations was seen ' 'to be pivotal in the success in the war that was unfolding. The plan that ' 'the delegates agreed to was never ratified by the colonial legislatures nor ' 'approved of by the crown. Nevertheless, the format of the congress and many ' 'specifics of the plan became the prototype for confederation during the War ' 'of Independence.')
The following code block setup the tensorflow graph g and session with the Univeral Encoder Multilingual Q&A model's question_encoder and response_encoder signatures.
module_url = "https://tfhub.dev/google/universal-sentence-encoder-multilingual-qa/3" model = hub.load(module_url)
The following code block compute the embeddings for all the text, context tuples and store them in a simpleneighbors index using the response_encoder.
batch_size = 100 encodings = model.signatures['response_encoder']( input=tf.constant([sentences]), context=tf.constant([sentences])) index = simpleneighbors.SimpleNeighbors( len(encodings['outputs']), metric='angular') print('Computing embeddings for %s sentences' % len(sentences)) slices = zip(*(iter(sentences),) * batch_size) num_batches = int(len(sentences) / batch_size) for s in tqdm(slices, total=num_batches): response_batch = list([r for r, c in s]) context_batch = list([c for r, c in s]) encodings = model.signatures['response_encoder']( input=tf.constant(response_batch), context=tf.constant(context_batch) ) for batch_index, batch in enumerate(response_batch): index.add_one(batch, encodings['outputs'][batch_index]) index.build() print('simpleneighbors index for %s sentences built.' % len(sentences))
Computing embeddings for 10455 sentences HBox(children=(FloatProgress(value=0.0, max=104.0), HTML(value=''))) simpleneighbors index for 10455 sentences built.
On retrieval, the question is encoded using the question_encoder and the question embedding is used to query the simpleneighbors index.
num_results = 25 query = random.choice(questions) display_nearest_neighbors(query, query)