wordVectors | R package for creating and exploring word2vec | Machine Learning library
kandi X-RAY | wordVectors Summary
kandi X-RAY | wordVectors Summary
This package does three major things to make it easier to work with word2vec and other vectorspace models of language.
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wordVectors Examples and Code Snippets
Community Discussions
Trending Discussions on wordVectors
QUESTION
I would like to download and load the pre-trained word2vec for analyzing Korean text.
I download the pre-trained word2vec here: https://drive.google.com/file/d/0B0ZXk88koS2KbDhXdWg1Q2RydlU/view?resourcekey=0-Dq9yyzwZxAqT3J02qvnFwg from the Github Pre-trained word vectors of 30+ languages: https://github.com/Kyubyong/wordvectors
My gensim version is 4.1.0, thus I used:
KeyedVectors.load_word2vec_format('./ko.bin', binary=False) to load the model. But there was an error that :
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte
I already tried many options including in stackoverflow and Github, but it still not work well. Would you mind letting me the suitable solution?
Thanks,
...ANSWER
Answered 2021-Dec-23 at 07:58While the page at https://github.com/Kyubyong/wordvectors isn't clear about the formats this author has chosen, by looking at their source code at...
https://github.com/Kyubyong/wordvectors/blob/master/make_wordvectors.py#L61
...shows it using the Gensim model .save() method.
Such saved models should be reloaded using the .load() class method of the same model class. For example, if a Word2Vec model was saved with...
QUESTION
I get always 100% training and validation accuracies. Here's how it looks:
...ANSWER
Answered 2020-Jun-10 at 12:39You initialize decoder_targets_one_hot as vectors of zeros, but do not set the index of true class as 1 anywhere. So, basically the target vectors are not one-hot vectors. The model tries to learn same target for all inputs, i.e. the vector of zeros.
QUESTION
We developed a Jupyter Notebook in a local machine to train models with the Python (V3) libraries sklearn and gensim.
As we set the random_state variable to a fixed integer, the results were always the same.
After this, we tried moving the notebook to a workspace in Azure Machine Learning Studio (classic), but the results differ even if we leave the random_state the same.
As suggested in the following links, we installed the same libraries versions and checked the MKL version was the same and the MKL_CBWR variable was set to AUTO.
t-SNE generates different results on different machines
Same Python code, same data, different results on different machines
Still, we are not able to get the same results.
What else should we check or why is this happening?
Update
If we generate a pkl file in the local machine and import it in AML, the results are the same (as the intention of the pkl file is).
Still, we are looking to get the same results (if possible) without importing the pkl file.
Library versions
...ANSWER
Answered 2020-Jun-07 at 01:37Definitely empathize with the issue you're having. Every data scientist has struggled with this at some point.
The hard truth I have for you is that Azure ML Studio (classic) isn't really capable of solving this "works on my machine" problem. However, the good news is that Azure ML Service is incredible at it. Studio classic doesn't let you define custom environments deterministically, only add and remove packages (and not so well even at that)
Because ML Service's execution is built on top of Docker containers and conda environments, you can feel more confident in repeated results. I highly recommend you take the time to learn it (and I'm also happy to debug any issues that come up). Azure's MachineLearningNotebooks repo has a lot of great tutorials for getting started.
I spent two hours making a proof of concept that demonstrate how ML Service solves the problem you're having by synthesizing:
- your code sample (before you shared your notebook),
- Jake Vanderplas's sklearn example, and
- this Azure ML tutorial on remote training.
I'm no T-SNE expert, but from the screenshot below, you can see that the t-sne outputs are the same when I run the script locally and remotely. This might be possible with Studio classic, but it would be hard to guarantee that it will always work.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install wordVectors
One of the major hurdles to running word2vec for ordinary people is that it requires compiling a C program. For many people, it may be easier to install it in R.
If you haven't already, install R and then install RStudio.
Open R, and get a command-line prompt (the thing with a > on the left hand side.) This is where you'll be copy-pasting commands.
Install (if you don't already have it) the package devtools by pasting the following install.packages("devtools")
Install the latest version of this package from Github by pasting in the following. devtools::install_github("bmschmidt/wordVectors") Windows users may need to install "Rtools" as well: if so, a message to this effect should appear in red on the screen. This may cycle through a very large number of warnings: so long as it says "warning" and not "error", you're probably OK.
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