wordvec | Word2Vec / GloVe implementation in Go | Natural Language Processing library

by alexalemi Go Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | wordvec Summary

wordvec is a Go library typically used in Artificial Intelligence, Natural Language Processing applications. wordvec has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Word2Vec / GloVe implementation in Go.

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Support

wordvec has a low active ecosystem.

It has 3 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

wordvec has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of wordvec is current.

Quality

wordvec has 0 bugs and 0 code smells.

Security

wordvec has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

wordvec code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

wordvec is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

wordvec releases are not available. You will need to build from source code and install.

It has 234 lines of code, 16 functions and 4 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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wordvec Key Features

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wordvec Examples and Code Snippets

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Community Discussions

Trending Discussions on wordvec

Pytorch C++ RuntimeError: Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _th_index_select

QUESTION

cosine similarity doc vectors and word vectors for topical prevalence using doc2vec

Asked 2021-Sep-30 at 07:11

I have a corpus of 250k Dutch news articles 2010-2020 to which I've applied word2vec models to uncover relationships between sets of neutral words and dimensions (e.g. good-bad). Since my aim is also to analyze the prevalence of certain topics over time, I was thinking of using doc2vec instead so as to simultaneously learn word and document embeddings. The 'prevalence' of topics in a document could then be calculated as the cosine similarities between doc vectors and word embeddings (or combinations of word vectors). In this way, I can calculate the annual topical prevalence in the corpus and see whether there's any changes over time. An example of such an approach can be found here.

My issue is that the avg. yearly cosine similarities yield really strange results. As an example, the cosine similarities between document vectors and a mixture of keywords related to covid-19/coronavirus show a decrease in topical prevalence since 2016 (which obviously cannot be the case).

My question is whether the approach that I'm following is actually valid. Or that maybe there's something that I'm missing. A 250k documents and 100k + vocabulary should be sufficient enough?

Below is the code that I've written:

...

ANSWER

Answered 2021-Sep-30 at 07:11

Turns out that setting parameters to dm=0, dbow_words=1 allows for training documents and words in the same space, now yielding valid results.

Source https://stackoverflow.com/questions/69361669

QUESTION

Pytorch C++ RuntimeError: Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _th_index_select

Asked 2020-Jul-24 at 13:18

I am calculating word similarity using torch::Embedding module by pretrained wordvector (glove.300d) on Ubuntu 18.04LTS PyTorch C++ (1.5.1, CUDA 10.1). I believe I have moved everything I can to the GPU, but when I execute it, it still says (full error log on the end of the question):

...

ANSWER

Answered 2020-Jul-24 at 13:18

Based on the error message, one of the two following Tensors are not in the GPU when you're running SimilarityModel::forward():

this->embedding->weight
x

Given that the error points to the argument #1, I'd say that weight is the one on the CPU.

Here's the call for index.select:

Source https://stackoverflow.com/questions/63057577

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

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Install wordvec

You can download it from GitHub.

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