SentenceGraph


NameSentenceGraph JSON
Version 0.0.5 PyPI version JSON
download
home_pagehttps://github.com/Hevia/SentenceGraph
SummaryEasily create semantic graphs from text using SentenceTransformers
upload_time2022-12-18 08:07:42
maintainer
docs_urlNone
authorHevia
requires_python>=3.8
licenseApache Software License 2.0
keywords nbdev jupyter notebook python
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            SentenceGraph
================

<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

## Install

``` sh
pip install SentenceGraph
```

## How to use

``` python
# from SentenceGraph.core import SentenceGraph, Format, TextNodeType
# from SentenceGraph.functional import create_text_nodes
```

``` python
# sentenceGraph = SentenceGraph()
```

``` python
# SentenceGraph requires all sentences to be passed as TextNode, which is just a namedtuple containing an id and text.
# There are several ways to prepare your sentence data for SentenceGraph.

# Use the builtin helper function which will just assign sequential ids for the data. Useful for experimentation.
# sentences = ['This framework generates embeddings for each input sentence',
#     'Sentences are passed as a list of string.', 
#     'The quick brown fox jumps over the lazy dog.']

# sentences = create_text_nodes(sentences)

# # 
# sentences = [TextNode(1, 'This framework generates embeddings for each input sentence'),
#     TextNode(2, 'Sentences are passed as a list of string.'), 
#     TextNode(3,'The quick brown fox jumps over the lazy dog.')]
```

``` python
# sim_graph = sentenceGraph.createGraph(sentences)
# sim_graph
```

You can also return a graph matrix in different formats.

``` python
# sim_graph = sentenceGraph.createGraph(sentences, format=Format.Numpy)
# sim_graph
```



            

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