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

Setup Environment

Setup common imports and functions

2023-12-08 13:25:49.787832: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2023-12-08 13:25:50.524521: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2023-12-08 13:25:50.524623: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2023-12-08 13:25:50.524633: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
[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 split 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

10455 sentences, 10552 questions extracted from SQuAD https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json

Example sentence and context:

sentence:

('The input string for a computational problem is referred to as a problem '
 'instance, and should not be confused with the problem itself.')

context:

('A computational problem can be viewed as an infinite collection of instances '
 'together with a solution for every instance. The input string for a '
 'computational problem is referred to as a problem instance, and should not '
 'be confused with the problem itself. In computational complexity theory, a '
 'problem refers to the abstract question to be solved. In contrast, an '
 'instance of this problem is a rather concrete utterance, which can serve as '
 'the input for a decision problem. For example, consider the problem of '
 'primality testing. The instance is a number (e.g. 15) and the solution is '
 '"yes" if the number is prime and "no" otherwise (in this case "no"). Stated '
 'another way, the instance is a particular input to the problem, and the '
 'solution is the output corresponding to the given input.')

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

2023-12-08 13:25:57.835736: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:267] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected

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