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Multilingual Universal Sentence Encoder Q&A Retrieval

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This is a demo for using Universal 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.

More models

You can find all currently hosted text embedding models here and all models that have been trained on SQuAD as well here.


Setup Environment

# Install the latest Tensorflow version.
!pip install -q tensorflow_text
!pip install -q simpleneighbors[annoy]
!pip install -q nltk
!pip install -q tqdm

Setup common imports and functions

[nltk_data] Downloading package punkt to /home/kbuilder/nltk_data...
[nltk_data]   Unzipping tokenizers/

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.

Download and extract SQuAD data

squad_url = ''

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)
10455 sentences, 10552 questions extracted from SQuAD

Example sentence and context:


('The Mongol Emperors had built large palaces and pavilions, but some still '
 'continued to live as nomads at times.')


("Since its invention in 1269, the 'Phags-pa script, a unified script for "
 'spelling Mongolian, Tibetan, and Chinese languages, was preserved in the '
 'court until the end of the dynasty. Most of the Emperors could not master '
 'written Chinese, but they could generally converse well in the language. The '
 'Mongol custom of long standing quda/marriage alliance with Mongol clans, the '
 'Onggirat, and the Ikeres, kept the imperial blood purely Mongol until the '
 'reign of Tugh Temur, whose mother was a Tangut concubine. The Mongol '
 'Emperors had built large palaces and pavilions, but some still continued to '
 'live as nomads at times. Nevertheless, a few other Yuan emperors actively '
 'sponsored cultural activities; an example is Tugh Temur (Emperor Wenzong), '
 'who wrote poetry, painted, read Chinese classical texts, and ordered the '
 'compilation of books.')

The following code block setup the tensorflow graph g and session with the Universal Encoder Multilingual Q&A model's question_encoder and response_encoder signatures.

Load model from tensorflow hub

The following code block compute the embeddings for all the text, context tuples and store them in a simpleneighbors index using the response_encoder.

Compute embeddings and build simpleneighbors index

Computing embeddings for 10455 sentences
0%|          | 0/104 [00:00<?, ?it/s]
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.

Retrieve nearest neighbors for a random question from SQuAD

num_results = 25

query = random.choice(questions)
display_nearest_neighbors(query[0], query[1])