word2vec | Implementation of word2vec from scratch using Numpy | Machine Learning library

Remove the .vocab here in model_glove.vocab, this is not supported in the current version of gensim any more: Edit: also needs split() to iterate over words and not characters here.

Import GensimWord2VecVectorizer in your Flask Web app python file.

Word2Vec expects a list of lists as input, where the corpus (main list) is composed of individual documents. The individual documents are composed of individual words (tokens). Word2Vec iterates over all documents and all tokens. In your example you have passed a single list to Word2Vec, therefore Word2Vec interprets each word as an individual document and iterates over each word character which is interpreted as a token. Therefore you have built a vocabulary of characters not words. To build a vocabulary of words you can pass a nested list to Word2Vec as in the example below.

In plotly-python, I don't think there's an easy way of retrieving the location of the cursor. You can attempt to use go.FigureWidget to highlight a trace as described in this answer, but i think you're going to be limited with with plotly-python and i'm not sure if highlighting the closest n points will be possible.

However, I believe that you can accomplish what you want in plotly-dash since callbacks are supported - meaning you would be able to retrieve location of your cursor and then calculate the n closest data points to your cursor and highlight the data points as needed.

Below is an example of such a solution. If you haven't seen it before, it looks complicated, but what is happening is that I am taking the point where you clicked as an input. plotly is plotly.js under the hood so it comes us in the form of a dictionary (and not some kind of plotly-python object). Then I calculate the closest three data points to the clicked input point by comparing the coordinates of every other point in the dataframe, add the information from the three closest points as traces to the input with the color teal (or any color of your choosing), and send this modified input back as the output, and update the figure.

I am using click instead of hover because hover would cause the highlighted points to flicker too much as you drag your mouse through the points.

Also the dash app doesn't work perfectly as I believe there is some issue when you double click on points (you can see me click once in the gif below before getting it to start working), but this basic framework is hopefully close enough to what you want. Cheers!

If a .save() is creating any files with the word trainables in it, you're using a older version fo Gensim. Any new training should definitely prefer using a current version. As of now (January 2022), that's gensim-4.1.2, released 2021-09.

If an attempt at a .load() generated that particular error, then there should've been that file, alongside the others you mention, created when the .save() had been done. (In fact, the only way that the main file you named with path_filename should be able to know that other filename is if that other file was written successfully, allowing the main file to complete writing.)

Are you sure that file wasn't written, but then somehow left behind, perhaps getting deleted or not moving alongside the other few files to some new filesystem path?

In general, I would suggest:

• using latest Gensim for any new training
• always enable Python logging at the INFO level, & watch the logging/console output of training/saving processes closely to see confirmation of expected activity/steps
• keep all files from a .save() that begin with the same main filename (in your examples above, w2v_US.model) together - & keep in mind that for larger models it may be a larger roster of files than for a small test model

You will probably have to re-train the model, but you might be able to re-generate a compatible lockf file via steps like the following:

• save aside all files of any potential use
• from the exact same configuration as your original .save() – including the same outdated Gensim version, exact same model parameters, & exact same training corpus – repeat all the model-building steps you did before up through the .build_vocab() step. (That is: no extra need to .train().) This will create an untrained dummy model that should exactly match the vocabulary 'shape' of your broken model.
• use .save() to save that dummy model again - watching the logs/output for errors. There should be, alongside the other files, a file with a name like dummy.model.trainables.vectors_lockf.npy. If so, you might be able to copy that away, rename it to tbe the file expected by the original model whose load failed, then leave it alongside that original model - and the .load() might then succeed, or fail in a different way.

(If there were other problems/corruption at the time of the original model creation, this might not work. In particular, I wonder if when you talk about retraining the model, you didn't start with a fresh Word2Vec instance, but somehow expanded the older one, which might've added other problems/complications. In that case, a full retraining, ideally in the latest Gensim, would be necessary, and also a better basis for going forward.)

Using an example from the package:

Gensim's classes generally only log if you specifically turn it on, in your code, by setting either a global or module/class-specific logging level.

So, are you sure you didn't turn on more logging that you want?

Search your code for anything that sets an INFO or DEBUG level of logging - and either delete or adjust/narrow that line to either not enable, or to set a more restrictie level, on the word2vec module or Word2Vec class.

At one level, when a word-token isn't in a fixed set of word-vectors, the creators of that set of word-vectors chose not to train/model that word. So, anything you do will only be a crude workaround for its absence.

Note, though, that when Google prepared those vectors – based on a dataset of news articles from before 2012 – they also ran some statistical multigram-combinations on it, creating multigrams with connecting _ characters. So, first check if a vector for 'artificial_intelligence' might be present.

If it isn't, you could try other rough workarounds like averaging together the vectors for 'artificial' and 'intelligence' – though of course that won't really be what people mean by the distinct combination of those words, just meanings suggested by the independent words.

The Gensim .most_similar() method can take either a raw vectors you've created by operations such as averaging, or even a list of multiple words which it will average for you, as arguments via its explicit keyword positive parameter. For example:

The model outputs the predicted probabilities, you need to transform them back to class labels before calculating the classification metrics, see below.