Accuracy is 0 when negative sampling is disabled

[ChristianAngel]

Problem description

When Word2Vec is trained on the text8 dataset with negative=0 (negative sampling disabled), the accuracy drops to 0 when evaluated on questions-words.txt.

Steps/code/corpus to reproduce

Minimal reproducible example:

import gensim.downloader
from gensim.models import Word2Vec

def evaluate(model):
    globalStats = model.wv.accuracy("questions-words.txt") 
    numberCorrect = len(globalStats[-1]['correct'])
    return numberCorrect

dataset = gensim.downloader.load("text8")
model1 = Word2Vec(dataset, size=300, workers=1, negative=5)
model2 = Word2Vec(dataset, size=300, workers=1, negative=0)

print("Number correct with negative sampling:", evaluate(model1))
print("Number correct without negative sampling:", evaluate(model2))

Output:

Number correct with negative sampling: 4031
Number correct without negative sampling: 0

questions-words.txt was downloaded from https://github.com/nicholas-leonard/word2vec/blob/master/questions-words.txt

Versions

Linux-3.10.0-862.2.3.el7.x86_64-x86_64-with-centos-7.5.1804-Core
Python 3.6.8 |Anaconda, Inc.| (default, Dec 30 2018, 01:22:34)
[GCC 7.3.0]
NumPy 1.16.4
SciPy 1.3.0
gensim 3.8.1
FAST_VERSION 1

[gojomo]

Duplicate of #1983 - but the only thing missing is a warning/error. This is a nonsensical configuration: if you disable negative without also enabling hs, then the model has no output-layer & source of backprop-training. (Either negative must be nonzero, or hs must be enabled, for anything useful to happen - as with the original word2vec.c code released by Google, there's no non-sparse training mode.) Training will complete instantly, logging output will be nonsense.