Index out of bounds error with Gensim 4.0.1 Word2Vec model

Question

Error image1:
Error image2:

I am new to study word2vec model. Here is the code, I want to use sentences list to finetune word2vec model (From Gensim 4.1.0)

from gensim.models import Word2Vec, KeyedVectors
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '2'

sentences = [['this', 'is', 'the', 'first', 'sentence', 'for', 'word2vec'],
            ['this', 'is', 'the', 'second', 'sentence'],
            ['yet', 'another', 'sentence'],
            ['one', 'more', 'sentence'],
            ['and', 'the', 'final', 'sentence']]

# load GoogleNews-vectors-negative300.bin
model = Word2Vec(sentences, vector_size=300, min_count=1, epochs=10)
model.build_vocab(sentences)
total_examples = model.corpus_count
print('total_examples:', total_examples)

model.wv.intersect_word2vec_format("GoogleNews-vectors**strong text**-negative300.bin", binary=True, lockf=1.0)

print('success')
model.train(sentences, total_examples=total_examples, epochs=model.epochs)
model.save("word2vec_model1")

I first got error below:

model.wv.intersect_word2vec_format("GoogleNews-vectors-negative300.bin", binary=True, lockf=1.0) self.vectors_lockf[self.get_index(word)] = lockf # lock-factor: 0.0=no changes IndexError: index 12 is out of bounds for axis 0 with size 1

Then I look into resource code in keyedvectores.py, code as below:

if word in self.key_to_index:
     overlap_count += 1
     self.vectors[self.get_index(word)] = weights
     self.vectors_lockf[self.get_index(word)] = lockf  # lock-factor: 0.0=no changes

I use ctrl+right click "vectors_lockf" to see this variance's declaration, but it shows "cannot find declaration to go"...

After that, when debugging, I find vectors_lockf is ndarray{ (1,)}[1.], but I don't know how it's generated... which code generates it.

Answer 1

This issue was not in gensim 3.8, vectors_lockf array was initialized internally but after update in gensim 4 they expect users to initialize this array manually when needed(you can see the comment in code).

After that, when debuging, I find vectors_lockf is ndarray{(1,)}[1.], but I don't know how it's generated...which code generate it..

It is initialized in init () method of Word2Vec class (word2vec.py)

if not hasattr(self, 'wv'):  # set unless subclass already set (eg: FastText)
    self.wv = KeyedVectors(vector_size)
    # EXPERIMENTAL lockf feature; create minimal no-op lockf arrays (1 element of 1.0)
    # advanced users should directly resize/adjust as desired after any vocab growth
    self.wv.vectors_lockf = np.ones(1, dtype=REAL)  # 0.0 values suppress word-backprop-updates; 1.0 allows

You can initialize this numpy array whenever you build vocab

model.build_vocab(sentences)
model.wv.vectors_lockf = np.ones(len(model.wv), dtype=REAL)

you can ignore dtype since default is float in numpy or you can import REAL dtype from gensim.