USE Lite - Universal Sentence Encoder Lite

pip install vectorhub[encoders-text-tfhub]

Details

Example

#pip install vectorhub[encoders-text-tfhub]
from vectorhub.encoders.text.tfhub import USELite2Vec
model = USELite2Vec()
model.encode("I enjoy taking long walks along the beach with my dog.

Index and search vectors

Index and search your vectors easily on the cloud using 1 line of code!

username = '<your username>'
email = '<your email>'
# You can request an api_key using - type in your username and email.
api_key = model.request_api_key(username, email)

# Index in 1 line of code
items = ['chicken', 'toilet', 'paper', 'enjoy walking']
model.add_documents(user, api_key, items)

# Search in 1 line of code and get the most similar results.
model.search('basin')

# Add metadata to your search
metadata = [{'num_of_letters': 7, 'type': 'animal'}, {'num_of_letters': 6, 'type': 'household_items'}, {'num_of_letters': 5, 'type': 'household_items'}, {'num_of_letters': 12, 'type': 'emotion'}]
model.add_documents(user, api_key, items, metadata=metadata)

Description

The Universal Sentence Encoder Lite module is a lightweight version of Universal Sentence Encoder. This lite version is good for use cases when your computation resource is limited. For example, on-device inference. It's small and still gives good performance on various natural language understanding tasks.

Working in Colab

If you are using this in colab and want to save this so you don't have to reload, use:

import os 
os.environ['TFHUB_CACHE_DIR'] = "drive/MyDrive/"
os.environ["TFHUB_MODEL_LOAD_FORMAT"] = "COMPRESSED"