I am using word2vec (gensim 4.3.3) on word embedding, results of the word vectors from the saved file ‘wv.vectors.npy’ shows that all word vectors are small, min of the entire array is -0.003 and max is 0.003, that each word is embedded with a very small vector, which is not expected.
What seems to be the problems, is my corpus or word not good for the application of the word2vec model, or the somethings about the training settings?

I am using word2vec (gensim 4.3.3) on word embedding, results of the word vectors from the saved file ‘wv.vectors.npy’ shows that all word vectors are small, min of the entire array is -0.003 and max is 0.003, that each word is embedded with a very small vector, which is not expected.
What seems to be the problems, is my corpus or word not good for the application of the word2vec model, or the somethings about the training settings?

I am using word2vec (gensim 4.3.3) on word embedding, results of the word vectors from the saved file ‘wv.vectors.npy’ shows that all word vectors are small, min of the entire array is -0.003 and max is 0.003, that each word is embedded with a very small vector, which is not expected.
What seems to be the problems, is my corpus or word not good for the application of the word2vec model, or the somethings about the training settings?

I am using word2vec (gensim 4.3.3) on word embedding, results of the word vectors from the saved file ‘wv.vectors.npy’ shows that all word vectors are small, min of the entire array is -0.003 and max is 0.003, that each word is embedded with a very small vector, which is not expected.
What seems to be the problems, is my corpus or word not good for the application of the word2vec model, or the somethings about the training settings?

I am using word2vec (gensim 4.3.3) on word embedding, results of the word vectors from the saved file ‘wv.vectors.npy’ shows that all word vectors are small, min of the entire array is -0.003 and max is 0.003, that each word is embedded with a very small vector, which is not expected.
What seems to be the problems, is my corpus or word not good for the application of the word2vec model, or the somethings about the training settings?

I am using word2vec (gensim 4.3.3) on word embedding, results of the word vectors from the saved file ‘wv.vectors.npy’ shows that all word vectors are small, min of the entire array is -0.003 and max is 0.003, that each word is embedded with a very small vector, which is not expected.
What seems to be the problems, is my corpus or word not good for the application of the word2vec model, or the somethings about the training settings?

I’m implementing a Skip-Gram model for Word2Vec using Python. However, my model doesn’t seem to be working correctly, as indicated by the resulting embeddings and their visualization. Here is an example of the 3D plot of the embeddings, which shows words clustered together and overlapping, making it difficult to distinguish between them: