Distilled Roberta QA

pip install vectorhub[encoders-text-sentence-transformers]


Release date: 2019-08-27

Vector length: 768 (default)

Repo: https://github.com/UKPLab/sentence-transformers

Paper: https://arxiv.org/abs/1908.10084


#pip install vectorhub[encoders-text-sentence-transformers]
from vectorhub.encoders.text_text.sentence_transformers import DistilRobertaQA2Vec
model = DistilRobertaQA2Vec('bert-base-uncased')
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! If you require metadata to not be stored on the cloud, simply attach with an ID for personal referral.

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.

# 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)


These are Distilled Roberta QA trained on MSMACRO dataset from sbert.net by UKPLab.