sent2vec | encode sentences in a high-dimensional vector space | Natural Language Processing library

by pdrm83 Python Version: 0.3.0 License: MIT

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

kandi X-RAY | sent2vec Summary

sent2vec is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. sent2vec has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install sent2vec' or download it from GitHub, PyPI.

How to encode sentences in a high-dimensional vector space, a.k.a., sentence embedding.

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Quality

Security

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Support

sent2vec has a low active ecosystem.

It has 56 star(s) with 7 fork(s). There are 5 watchers for this library.

It had no major release in the last 12 months.

There are 8 open issues and 1 have been closed. On average issues are closed in 22 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of sent2vec is 0.3.0

Quality

sent2vec has 0 bugs and 12 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 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

sent2vec releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

It has 355 lines of code, 30 functions and 9 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed sent2vec and discovered the below as its top functions. This is intended to give you an instant insight into sent2vec implemented functionality, and help decide if they suit your requirements.

Execute the model
Convert sentences to words

Get all kandi verified functions for this library.

sent2vec Key Features

No Key Features are available at this moment for sent2vec.

sent2vec Examples and Code Snippets

Sent2Vec,Usage

Python

Lines of Code : 32

License : Permissive (MIT)

Copy

from sent2vec.vectorizer import Vectorizer

sentences = [
    "This is an awesome book to learn NLP.",
    "DistilBERT is an amazing NLP model.",
    "We can interchangeably use embedding, encoding, or vectorizing.",
]
vectorizer = Vectorizer()
vecto

Sent2Vec,Install

Python

Lines of Code : 1

License : Permissive (MIT)

Copy

pip3 install sent2vec

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

The sent2vec is developed to help you prototype faster. That is why it has many dependencies on other libraries. The module requires the following libraries:.
gensim
numpy
spacy
transformers
torch

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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