https://github.com/cedergrouphub/matentityrecognition

Extract materials from paragraphs and recognize the targets and precursors
https://github.com/cedergrouphub/matentityrecognition

Last synced: 6 months ago
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Extract materials from paragraphs and recognize the targets and precursors

• Host: GitHub
• URL: https://github.com/cedergrouphub/matentityrecognition
• Owner: CederGroupHub
• Created: 2018-04-08T07:16:03.000Z (over 8 years ago)
• Default Branch: v3
• Last Pushed: 2024-03-13T21:54:41.000Z (over 2 years ago)
• Last Synced: 2025-09-09T23:38:56.000Z (10 months ago)
• Language: Python
• Homepage:
• Size: 63.8 MB
• Stars: 7
• Watchers: 3
• Forks: 4
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

          
# Synthesis Materials Recognizer (SMR) for Materials Entity Recognition (MER)

Extract materials from a paragraph, and recognize the targets and precursors in those materials  

Install:  

	If Git Large File Storage (lfs) is not installed on your computer, please install it fistly following the instruction on

		https://help.github.com/articles/installing-git-large-file-storage/.

	Then

        git clone git@github.com:CederGroupHub/MatEntityRecognition.git 

        cd MatEntityRecognition

        pip install -e .

	

	Spacy is used. If there is an error saying: 

	    "Can't find model 'en-core-web-sm'..." 

	It is because the spacy data is not downloaded. Please use:

	    python -m spacy download en-core-web-sm

	    

    MaterialParser is used. Please find it here:

        https://github.com/CederGroupHub/MaterialParser

Use:

    # An example is in test/example.py

	from materials_entity_recognition import MatRecognition   

	model = MatRecognition()  

	result = model.mat_recognize(input_paras)  

Parameters:

	Input: list of plain text of paragraphs or plian text of a paragraph. 

	Note: input a list of paragraphs (recommended) is much faster than inputting them one by one in a loop!  

	Output: a list of (list of) dict objects, containing all materials, precursors, targets, and other materials for each sentence in the input paragraphs.  

It is also possible to use pre-defined tokens:

    # An example is in test/pre_tokens.py

    # pre_tokens is a list of list of tokens.

    # The element in the first-level list corresponds to each paragraph

    # The element in the second-level list corresponds to each sentence in each paragraph

    # Each token is dict such as {'start': 0, 'end': 4, 'text': 'text'} or 

    # an object with attributes of 'start', 'end', and 'text'. 

    result = model.mat_recognize(input_paras, pre_tokens=pre_tokens)