sent2vec | General purpose unsupervised sentence representations | Natural Language Processing library

by epfml C++ Version: v1 License: Non-SPDX

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

kandi X-RAY | sent2vec Summary

sent2vec is a C++ library typically used in Artificial Intelligence, Natural Language Processing applications. sent2vec has no bugs, it has no vulnerabilities and it has medium support. However sent2vec has a Non-SPDX License. You can download it from GitHub.

TLDR: This library provides numerical representations (features) for words, short texts, or sentences, which can be used as input to any machine learning task.

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sent2vec has a medium active ecosystem.

It has 1150 star(s) with 252 fork(s). There are 39 watchers for this library.

It had no major release in the last 12 months.

There are 23 open issues and 83 have been closed. On average issues are closed in 220 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of sent2vec is v1

Quality

sent2vec has 0 bugs and 0 code smells.

Security

sent2vec has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

sent2vec code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

sent2vec has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

sent2vec releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 95 lines of code, 6 functions and 2 files.

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

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sent2vec Key Features

No Key Features are available at this moment for sent2vec.

sent2vec Examples and Code Snippets

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Community Discussions

Trending Discussions on sent2vec

Which document embedding model for document similarity

Access server running on docker container

QUESTION

Which document embedding model for document similarity

Asked 2020-Nov-26 at 20:36

First, I want to explain my task. I have a dataset of 300k documents with an average of 560 words (no stop word removal yet) 75% in German, 15% in English and the rest in different languages. The goal is to recommend similar documents based on an existing one. At the beginning I want to focus on the German and English documents.

To achieve this goal I looked into several methods on feature extraction for document similarity, especially the word embedding methods have impressed me because they are context aware in contrast to simple TF-IDF feature extraction and the calculation of cosine similarity.

I'm overwhelmed by the amount of methods I could use and I haven't found a proper evaluation of those methods yet. I know for sure that the size of my documents are too big for BERT, but there is FastText, Sent2Vec, Doc2Vec and the Universal Sentence Encoder from Google. My favorite method based on my research is Doc2Vec even though there aren't any or old pre-trained models which means I have to do the training on my own.

Now that you know my task and goal, I have the following questions:

Which method should I use for feature extraction based on the rough overview of my data?
My dataset is too small to train Doc2Vec on it. Do I achieve good results if I train the model on English / German Wikipedia?

...

ANSWER

Answered 2020-Nov-26 at 20:36

You really have to try the different methods on your data, with your specific user tasks, with your time/resources budget to know which makes sense.

You 225K German documents and 45k English documents are each plausibly large enough to use Doc2Vec - as they match or exceed some published results. So you wouldn't necessarily need to add training on something else (like Wikipedia) instead, and whether adding that to your data would help or hurt is another thing you'd need to determine experimentally.

(There might be special challenges in German given compound words using common-enough roots but being individually rare, I'm not sure. FastText-based approaches that use word-fragments might be helpful, but I don't know a Doc2Vec-like algorithm that necessarily uses that same char-ngrams trick. The closest that might be possible is to use Facebook FastText's supervised mode, with a rich set of meaningful known-labels to bootstrap better text vectors - but that's highly speculative and that mode isn't supported in Gensim.)

Source https://stackoverflow.com/questions/65027694

QUESTION

Access server running on docker container

Asked 2020-Oct-07 at 08:08

I am running the StanfordCoreNLP server through my docker container. Now I want to access it through my python script.

Github repo I'm trying to run: https://github.com/swisscom/ai-research-keyphrase-extraction

I ran the command which gave me the following output:

...

ANSWER

Answered 2020-Oct-07 at 08:08

As seen in the log, your service is listening to port 9000 inside the container. However, from outside you need further information to be able to access it. Two pieces of information that you need:

The IP address of the container
The external port that docker exports this 9000 to the outside (by default docker does not export locally open ports).

To get the IP address you need to use docker inspect, for example via

Source https://stackoverflow.com/questions/64238613

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

Vulnerabilities

No vulnerabilities reported

Install sent2vec

Our code builds upon Facebook's FastText library, see also their nice documentation and python interfaces. To compile the library, simply run a make command.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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