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).
pip install lmdb-embeddingsfrom 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.
passAn 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.A reader with an LRU (Least Recently Used) cache is included. This will save the embeddings for the 50,000 most recently queried words and return the same object instead of querying the database each time. Its interface is the same as the standard reader. See functools.lru_cache in the standard library.
from lmdb_embeddings.reader import LruCachedLmdbEmbeddingsReader
from lmdb_embeddings.exceptions import MissingWordError
embeddings = LruCachedLmdbEmbeddingsReader('/path/to/word/vectors/eg/GoogleNews-vectors-negative300')
try:
vector = embeddings.get_word_vector('google')
except MissingWordError:
# 'google' is not in the database.
passBy 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 - simply inject the serializer and unserializer as callables into the LmdbEmbeddingsWriter and LmdbEmbeddingsReader.
A msgpack serializer is included and can be used in the same way.
from lmdb_embeddings.writer import LmdbEmbeddingsWriter
from lmdb_embeddings.serializers import MsgpackSerializer
writer = LmdbEmbeddingsWriter(
iter_embeddings(),
serializer=MsgpackSerializer().serialize
).write(OUTPUT_DATABASE_FOLDER)from lmdb_embeddings.reader import LmdbEmbeddingsReader
from lmdb_embeddings.serializers import MsgpackSerializer
reader = LmdbEmbeddingsReader(
OUTPUT_DATABASE_FOLDER,
unserializer=MsgpackSerializer().unserialize
)pytest
- Github: DomHudson
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This project is GPL-3.0 licensed.