Word-Similarity | task of measure similarity between two words | Natural Language Processing library

In your second example, you've created a training dataset with just a single text with the entire contents of the file. That's about 1.1 million word tokens, in a single list.

Word2Vec (& other related algorithms) in gensim have an internal implementation limitation, in their optimized paths, of 10,000 tokens per text item. All additional tokens are ignored.

So, in your 2nd case, 99% of your data is being discarded. Training may seem instant, but very little actual training will have occurred. (Word-vectors for words that only appear past the 1st 10,000 tokens won't have been trained at all, having only their initial randomly-set values.) If you enable logging at the INFO level, you'll see more details about each step of the process, and discrepancies like this may be easier to identify.

Yes, the api.load() variant takes extra steps to break the single-line-file into 10,000-token chunks. I believe it's using the LineSentence utility class for this purpose, whose source can be examined here:

https://github.com/RaRe-Technologies/gensim/blob/e859c11f6f57bf3c883a718a9ab7067ac0c2d4cf/gensim/models/word2vec.py#L1209

However, I recommend avoiding the api.load() functionality entirely. It doesn't just download data; it also downloads a shim of additional outside-of-version-control Python code for prepping that data for extra operations. Such code is harder to browse & less well-reviewed than official gensim release code as packaged for PyPI/etc, which also presents a security risk. Each load target (by name like 'text8') might do something different, leaving you with a different object type as the return value.

It's much better for understanding to directly download precisely the data files you need, to known local paths, and do the IO/prep yourself, from those paths, so you know what steps have been applied, and the only code you're running is the officially versioned & released code.