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https://github.com/ThoughtRiver/lmdb-embeddings
Fast word vectors with little memory usage in Python
https://github.com/ThoughtRiver/lmdb-embeddings
embeddings fasttext gensim glove lmdb magnitude memory speed text vectors word word2vec
Last synced: about 2 months ago
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Fast word vectors with little memory usage in Python
- Host: GitHub
- URL: https://github.com/ThoughtRiver/lmdb-embeddings
- Owner: ThoughtRiver
- License: gpl-3.0
- Created: 2018-09-03T16:31:18.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-06-26T13:12:01.000Z (about 3 years ago)
- Last Synced: 2024-05-06T12:21:49.731Z (2 months ago)
- Topics: embeddings, fasttext, gensim, glove, lmdb, magnitude, memory, speed, text, vectors, word, word2vec
- Language: Python
- Homepage:
- Size: 67.4 KB
- Stars: 414
- Watchers: 19
- Forks: 30
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- my-awesome-stars - ThoughtRiver/lmdb-embeddings - Fast word vectors with little memory usage in Python (Python)
- awesome - lmdb-embeddings - Fast word vectors with little memory usage in Python (Python)
README
[](https://travis-ci.org/ThoughtRiver/lmdb-embeddings)
# LMDB Embeddings
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](https://en.wikipedia.org/wiki/Lightning_Memory-Mapped_Database).
Inspired by [Delft](https://github.com/kermitt2/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).
## Installation
```bash
pip install lmdb-embeddings
```
## Reading vectors
```python
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
```
## Writing vectors
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.
```python
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.
```
## LRU Cache
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](https://docs.python.org/3/library/functools.html#functools.lru_cache) in the standard library.
```python
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.
pass
```
## Customisation
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 - simply inject the serializer and unserializer as callables into the `LmdbEmbeddingsWriter` and `LmdbEmbeddingsReader`.
A [msgpack](https://msgpack.org/index.html) serializer is included and can be used in the same way.
```python
from lmdb_embeddings.writer import LmdbEmbeddingsWriter
from lmdb_embeddings.serializers import MsgpackSerializer
writer = LmdbEmbeddingsWriter(
iter_embeddings(),
serializer=MsgpackSerializer().serialize
).write(OUTPUT_DATABASE_FOLDER)
```
```python
from lmdb_embeddings.reader import LmdbEmbeddingsReader
from lmdb_embeddings.serializers import MsgpackSerializer
reader = LmdbEmbeddingsReader(
OUTPUT_DATABASE_FOLDER,
unserializer=MsgpackSerializer().unserialize
)
```
## Running tests
```
pytest
```
## Author
- Github: [DomHudson](https://github.com/DomHudson)
## Contributing
Contributions, issues and feature requests are welcome!
## Show your support
Give a ⭐️ if this project helped you!
## License
Copyright © 2019 [ThoughtRiver](https://github.com/thoughtriver).
This project is [GPL-3.0](https://github.com/ThoughtRiver/lmdb-embeddings/blob/master/LICENSE) licensed.