flyvec 0.3.0

A biologically inspired method to create sparse, binary word vectors

FlyVec

Sparse Binary Word Embeddings Inspired by the Fruit Fly Brain

Code based on the ICLR 2021 paper Can a Fruit Fly Learn Word Embeddings?.

In this work we use a well-established neurobiological network motif from the mushroom body of the fruit fly brain to learn sparse binary word embeddings from raw unstructured text. This package allows the user to access pre-trained word embeddings and generate sparse binary hash codes for individual words.

Interactive demos of the learned concepts available at flyvec.org.

How to use

Install from Pip (recommended)

pip install flyvec

Installing from Source

After cloning:

conda env create -f environment-dev.yml
conda activate flyvec
pip install -e .

Basic Usage

An example below illustrates how one can access the binary word embedding for individual tokens for a default hash length k=50.

import numpy as np
from flyvec import FlyVec

model = FlyVec.load()
embed_info = model.get_sparse_embedding("market"); embed_info

{'token': 'market',
 'id': 1180,
 'embedding': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
        1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,
        0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
        1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
        1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
        1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
        0, 0, 0, 0], dtype=int8)}

Changing the Hash Length

The user can obtain the FlyVec embeddings for any hash length using the following example.

small_embed = model.get_sparse_embedding("market", 4); np.sum(small_embed['embedding'])

4

Handling "unknown" tokens

FlyVec uses a simple, word-based tokenizer. The provided model uses a vocabulary with about 20,000 words, all lower-cased, with special tokens for numbers (<NUM>) and unknown words (<UNK>). Unknown tokens have the token id of 0, which can be used to filter unknown tokens.

unk_embed = model.get_sparse_embedding("DefNotAWord")
if unk_embed['id'] == 0:
    print("I AM THE UNKNOWN TOKEN DON'T USE ME FOR ANYTHING IMPORTANT")

I AM THE UNKNOWN TOKEN DON'T USE ME FOR ANYTHING IMPORTANT

Batch generating word embeddings

Embeddings for individual words in a sentence can be obtained using this snippet.

sentence = "Supreme Court dismissed the criminal charges."
tokens = model.tokenize(sentence)
embedding_info = [model.get_sparse_embedding(t) for t in tokens]
embeddings = np.array([e['embedding'] for e in embedding_info])
print("TOKENS: ", [e['token'] for e in embedding_info])
print("EMBEDDINGS: ", embeddings)

TOKENS:  ['supreme', 'court', 'dismissed', 'the', 'criminal', 'charges']
EMBEDDINGS:  [[0 1 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 1 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 1 0]
 [0 0 0 ... 0 1 0]]

FlyVec vocabulary

The vocabulary under the hood uses the gensim Dictionary and can be accessed by either IDs (ints) or Tokens (strs).

# The tokens in the vocabulary
print(model.token_vocab[:5])

# The IDs that correspond to those tokens
print(model.vocab[:5])

# The dictionary object itself
model.dictionary;

['properties', 'a', 'among', 'and', 'any']
[2, 3, 4, 5, 6]

Training

Please note that the training code is included, though code for processing the inputs.

Prerequisites

You need a python environment with numpy installed, a system that supports CUDA, nvcc, and g++.

Building the Source Files

flyvec_compile

(Or, if using from source, you can also run make training)

Note that you will see some warnings. This is expected.

Training

flyvec_train path/to/encodings.npy path/to/offsets.npy -o save/checkpoints/in/this/directory

Description of Inputs

• encodings.npy -- An np.int32 array representing the tokenized vocabulary-IDs of the input corpus, of shape (N,) where N is the number of tokens in the corpus
• offsets.npy -- An np.uint64 array of shape (C,) where C is the number of chunks in the corpus. Each each value represents the index that starts a new chunk within encodings.npy. (Chunks can be thought of as sentences or paragraphs within the corpus; boundaries over which the sliding window does not cross.)

Description of Outputs

• model_X.npy -- Stores checkpoints after every epoch within the specified output directory

See flyvec_train --help for more options.

Citation

If you use this in your work, please cite:

@article{liang2021can,
  title={Can a Fruit Fly Learn Word Embeddings?},
  author={Liang, Yuchen and Ryali, Chaitanya K and Hoover, Benjamin and Grinberg, Leopold and Navlakha, Saket and Zaki, Mohammed J and Krotov, Dmitry},
  journal={arXiv preprint arXiv:2101.06887},
  year={2021}
  url={https://arxiv.org/abs/2101.06887}
}