Different embeddings with different length

I faced issue that while encoding same sentence but in lists of different length i receive slightly different embeddings.

Here is a code to describe what I meant:

from laserembeddings import Laser

import numpy as np

laser = Laser()
a = laser.embed_sentences(["apple", "banana", "clementina"], lang='en')
b = laser.embed_sentences(["apple"], lang='en')
c = laser.embed_sentences(["apple", "potato", "strawberry"], lang='en')
(a[0]==b[0]).all() # check if all elemnts same
#False
(a[0]==c[0]).all()
#True
np.linalg.norm(a[0]-b[0])
#1.3968409e-07
np.linalg.norm(a[0]-c[0])
#0.0

My goal is to get same embedding of word sentence apple, no matter which size of text list I use - but it seems to be unreal with current version of laserembeddings. I would like to know if such behavior is intentional or it’s a bug?

[yannvgn]

Ok, but your batch is relatively small (3 elements). If you try with more sentences, you should see that the batched version is faster:

import timeit
from laserembeddings import Laser

laser = Laser()
laser_1 = Laser(embedding_options={'max_sentences': 1})

# 300 sentences
sentences = ['apple', 'potato', 'strawberry'] * 100

def init():
    laser.embed_sentences(['init'], lang='en')

def test_with_batch():
    laser.embed_sentences(sentences, lang='en')

def test_without_batch():
    laser_1.embed_sentences(sentences, lang='en')

# make sure everything is initialized
init()

print(timeit.timeit(test_with_batch, number=10))
# 4.908349000004819

print(timeit.timeit(test_without_batch, number=10))
# 66.42792240000563

[vchulski]

Ok, but your batch is relatively small (3 elements).

That’s a valid point. Thanks for example you provided and all of your answers.