GoogleNews | Script for GoogleNews | Dataset library

The 'current working directory' that the Python process will consider active, and thus will use as the expected location for your plain relative filename GoogleNews-vectors-negative300.bin, will depend on how you launched Flask.

You could print out the directory to be sure – see some ways at How do you properly determine the current script directory? – but I suspect it may just be the /Users/Ile-Maurice/Desktop/Flask/flaskapp/ directory.

If so, you could relatively-reference your file with the path relative to the above directory...

You are currently having a sparse tensor for your y-values:

Try with requests package:

You're thinking too hard. All I mean is something like this:

We can do it in pure Python using a context manager and the csv module :

You can do that using find method as follows:

The .intersect_word2vec_format() method still exists, but as an operation on a set of word-vectors, has moved to KeyedVectors. So in some cases, older code that had called the method on a Word2Vec model itself will need to call it on the model's .wv property, holding a KeyedVectors object, instead. EG:

I think you mean

I don't think that code would've ever have worked in Gensim versions before 4.0. A plain list-of-word-vectors, like GoogleNews-vectors-negative300.bin, does not (& never has) had enough info to continue training.

It's missing the hidden-to-output layer weights & word-frequency info essential for training.

Looking at past source code, as of release 1.0.0 (February 2017), that code wouldn've already given a deprecation-error with a pointer to the method for loading a plain set-of-word-vectors - to address people with the mistaken notion that could work – and raised other errors on any attempts to train() such a model. (Pre-1.0.0, docs also warned that this would not work, & would have failed with a less-helpful error.)

As one of those errors mentioned, there has at times been experimental support for loading some of a prior set-of-word-vectors to clobber any words in an existing model's already-initialized vocabulary, via .intersect_word2vec_format(). But by default that both (1) locks the imported vectors against further change; (2) brings in no new words. That's unlike what people most often want from "fine-tuning", so it's not a ready-made help for that goal.

I believe some people have cobbled together custom code to achieve various kinds of fine-tuning in their projects – but I don't know of anyone who's published a reliable recipe or strong results. (And I suspect some of the people who think they're doing this well just haven't rigorously evaluated the steps they are taking.)

If you have any recipe you know worked pre-Gensim-4.0.0, it should be adaptable - 4.0 changes to the Word2Vec-related classes were mainly refactorings, optimizations, & new options (with little-to-none removal of functionality). But a reliable description of what used-to-work, or which particular fine-tuning strategy is being pursued for what specific benefits, to make more specific recommendations.