mat2vec | Supplementary Materials for Tshitoyan et al | Machine Learning library

by materialsintelligence Python Version: Current License: MIT

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kandi X-RAY | mat2vec Summary

mat2vec is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. mat2vec has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Supplementary Materials for Tshitoyan et al. "Unsupervised word embeddings capture latent knowledge from materials science literature", Nature (2019).

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mat2vec has a low active ecosystem.

It has 583 star(s) with 177 fork(s). There are 40 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 17 have been closed. On average issues are closed in 157 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of mat2vec is current.

Quality

mat2vec has 0 bugs and 0 code smells.

Security

mat2vec has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

mat2vec code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

mat2vec 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

mat2vec releases are not available. You will need to build from source code and install.

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

Installation instructions, examples and code snippets are available.

mat2vec saves you 240 person hours of effort in developing the same functionality from scratch.

It has 586 lines of code, 32 functions and 9 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed mat2vec and discovered the below as its top functions. This is intended to give you an instant insight into mat2vec implemented functionality, and help decide if they suit your requirements.

Download the models
Check to see if a file exists
Compute the accuracy for each epoch
Save object to pickle file
Build word gramgrams from a sentence
Exclude words in phrasegrams
Keep only the simple formula
Return True if text is a simple formula
Check if a string is an Element
Load a pickled object

Get all kandi verified functions for this library.

mat2vec Key Features

No Key Features are available at this moment for mat2vec.

mat2vec Examples and Code Snippets

No Code Snippets are available at this moment for mat2vec.

Community Discussions

Trending Discussions on mat2vec

How can I install the mat2vec Python package?

QUESTION

How can I install the mat2vec Python package?

Asked 2020-Apr-04 at 17:45

I'm trying to install the mat2vec Python package (more info here: https://github.com/materialsintelligence/mat2vec) typing the usual pip install mat2vec. But the output is

...

ANSWER

Answered 2020-Apr-04 at 16:31

I believe you need to do the following:

1- Download package root directory: https://github.com/materialsintelligence/mat2vec.

2- Open a python terminal and navigate to the downloaded package root directory path.

3- Run the command: pip install --ignore-installed -r requirements.txt.

4- Run the command: python setup.py instal.

5- Run the command: cde data download.

Source https://stackoverflow.com/questions/61030984

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

Vulnerabilities

No vulnerabilities reported

Install mat2vec

(['CoLiO2', 'is', 'a', 'battery', 'cathode', 'material', '.'], [('LiCoO2', 'CoLiO2')]). For the various methods and options see the docstrings in the code.
Make sure you have python3.6 and the pip module installed. We recommend using conda environments.
Navigate to the root folder of this repository (the same folder that contains this README file) and run pip install --ignore-installed -r requirements.txt. Note: If you are using a conda env and any packages fail to compile during this step, you may need to first install those packages separately with conda install package_name.
Wait for all the requirements to be downloaded and installed.
Run python setup.py install to install this module. This will also download the Word2vec model files. If the download fails, manually download the model, word embeddings and output embeddings and put them in mat2vec/training/models.
Finalize your chemdataextractor installation by executing cde data download (You may need to restart your virtual environment for the cde command line interface to be found).
You are ready to go!

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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