Query word vectors (embeddings) very quickly with very little querying time overhead and far less memory usage than gensim or other equivalent solutions. This is made possible by Lightning Memory-Mapped Database.
Inspired by Delft. As explained in their readme, this approach permits us to have the pre-trained embeddings immediately "warm" (no load time), to free memory and to use any number of embeddings similtaneously with a very negligible impact on runtime when using SSD.
For instance, in a traditional approach glove-840B
takes around 2 minutes to load and 4GB in memory. Managed with LMDB, glove-840B
can be accessed immediately and takes only a couple MB in memory, for a negligible impact on runtime (around 1% slower).
from lmdb_embeddings.reader import LmdbEmbeddingsReader
from lmdb_embeddings.exceptions import MissingWordError
embeddings = LmdbEmbeddingsReader('/path/to/word/vectors/eg/GoogleNews-vectors-negative300')
try:
vector = embeddings.get_word_vector('google')
except MissingWordError:
# 'google' is not in the database.
pass
An example to write an LMDB vector file from a gensim model. As any iterator that yields word and vector pairs is supported, if you have the vectors in an alternative format then it is just a matter of altering the iter_embeddings
method below appropriately.
I will be writing a CLI interface to convert standard formats soon.
from gensim.models.keyedvectors import KeyedVectors
from lmdb_embeddings.writer import LmdbEmbeddingsWriter
GOOGLE_NEWS_PATH = 'GoogleNews-vectors-negative300.bin.gz'
OUTPUT_DATABASE_FOLDER = 'GoogleNews-vectors-negative300'
print('Loading gensim model...')
gensim_model = KeyedVectors.load_word2vec_format(GOOGLE_NEWS_PATH, binary = True)
def iter_embeddings():
for word in gensim_model.vocab.keys():
yield word, gensim_model[word]
print('Writing vectors to a LMDB database...')
writer = LmdbEmbeddingsWriter(
iter_embeddings()
).write(OUTPUT_DATABASE_FOLDER)
# These vectors can now be loaded with the LmdbEmbeddingsReader.
pytest
By default, LMDB Embeddings uses pickle to serialize the vectors to bytes (optimized and pickled with the highest available protocol). However, it is very easy to use an alternative approach such as msgpack. Simply inject the serializer and unserializer as callables into the LmdbEmbeddingsWriter
and LmdbEmbeddingsReader
.