embedding-as-service

One-Stop Solution to encode sentence to fixed length vectors from various embedding techniques
• Inspired from bert-as-service

What is it • Installation • Getting Started • Supported Embeddings • API •

What is it

Encoding/Embedding is a upstream task of encoding any inputs in the form of text, image, audio, video, transactional data to fixed length vector. Embeddings are quite popular in the field of NLP, there has been various Embeddings models being proposed in recent years by researchers, some of the famous one are bert, xlnet, word2vec etc. The goal of this repo is to build one stop solution for all embeddings techniques available, here we are starting with popular text embeddings for now and later on we aim to add as much technique for image, audio, video inputs also.

embedding-as-service help you to encode any given text to fixed length vector from supported embeddings and models.

💾 Installation

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Here we have given the capability to use embedding-as-service like a module or you can run it as a server and handle queries by installing client package embedding-as-service-client

Using embedding-as-service as module

Install the embedding-as-servive via pip.

$ pip install embedding-as-service

Note that the code MUST be running on Python >= 3.6. Again module does not support Python 2!

Using embedding-as-service as a server

Here you also need to install a client module embedding-as-service-client

$ pip install embedding-as-service # server
$ pip install embedding-as-service-client # client

Client module need not to be on Python 3.6, it supports both Python2 and Python3

⚡ ️Getting Started

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1. Intialise encoder using supported embedding and models from here

If using embedding-as-service as a module

>>> from embedding_as_service.text.encode import Encoder  
>>> en = Encoder(embedding='bert', model='bert_base_cased', max_seq_length=256)  

If using embedding-as-service as a server

# start the server by proving embedding, model, port, max_seq_length[default=256], num_workers[default=4]
$ embedding-as-service-start --embedding bert --model bert_base_cased --port 8080 --max_seq_length 256

>>> from embedding_as_service_client import EmbeddingClient
>>> en = EmbeddingClient(host=<host_server_ip>, port=<host_port>)

2. Get sentences tokens embedding

>>> vecs = en.encode(texts=['hello aman', 'how are you?'])  
>>> vecs  
array([[[ 1.7049843 ,  0.        ,  1.3486509 , ..., -1.3647075 ,  
 0.6958289 ,  1.8013777 ], ... [ 0.4913215 ,  0.60877025,  0.73050433, ..., -0.64490885, 0.8525057 ,  0.3080206 ]]], dtype=float32)  
>>> vecs.shape  
(2, 128, 768) # batch x max_sequence_length x embedding_size  

3. Using pooling strategy, click here for more.

>>> vecs = en.encode(texts=['hello aman', 'how are you?'], pooling='reduce_mean')  
>>> vecs  
array([[-0.33547154,  0.34566957,  1.1954105 , ...,  0.33702594,  
 1.0317835 , -0.785943  ], [-0.3439088 ,  0.36881036,  1.0612687 , ...,  0.28851607, 1.1107115 , -0.6253736 ]], dtype=float32)  

>>> vecs.shape  
(2, 768) # batch x embedding_size  

4. Show embedding Tokens

>>> en.tokenize(texts=['hello aman', 'how are you?'])  
[['_hello', '_aman'], ['_how', '_are', '_you', '?']]  

5. Using your own tokenizer

>>> texts = ['hello aman!', 'how are you']  

# a naive whitespace tokenizer  
>>> tokens = [s.split() for s in texts]  
>>> vecs = en.encode(tokens, is_tokenized=True)  

📋 API

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1. class embedding_as_service.text.encoder.Encoder

2. def embedding_as_service.text.encoder.Encoder.encode

3. def embedding_as_service.text.encoder.Encoder.tokenize

✅ Supported Embeddings and Models

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Here are the list of supported embeddings and their respective models.

Credits

This software uses the following open source packages:

• XLnet
• tensorflow-hub

Contributors ✨

Thanks goes to these wonderful people (emoji key):

MrPranav101 💻 📖 🚇

Aman Srivastava 💻 📖 🚇

Chirag Jain 💻 📖 🚇

Ashutosh Singh 💻 📖 🚇

Dhaval Taunk 💻 📖 🚇

Alec Koumjian 🐛

Pradeesh 🐛

This project follows the all-contributors specification. Contributions of any kind welcome!

Please read the contribution guidelines first.

Citing

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If you use embedding-as-service in a scientific publication, we would appreciate references to the following BibTex entry:

@misc{aman2019embeddingservice,
  title={embedding-as-service},
  author={Srivastava, Aman},
  howpublished={\url{https://github.com/amansrivastava17/embedding-as-service}},
  year={2019}
}