bert_classification | Using bert pre-trained Chinese model

by pengming617 Python Version: Current License: No License

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kandi X-RAY | bert_classification Summary

bert_classification is a Python library. bert_classification has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Using bert pre-trained Chinese model for text classification data set Chinese sentiment analysis corpus chnsenticorp

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bert_classification has a low active ecosystem.

It has 257 star(s) with 67 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 2 have been closed. On average issues are closed in 16 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of bert_classification is current.

Quality

bert_classification has 0 bugs and 57 code smells.

Security

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

bert_classification code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

bert_classification does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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bert_classification 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.

bert_classification saves you 1737 person hours of effort in developing the same functionality from scratch.

It has 3846 lines of code, 191 functions and 12 files.

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

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bert_classification Key Features

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bert_classification Examples and Code Snippets

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Community Discussions

Trending Discussions on bert_classification

bert + text and structur data

QUESTION

bert + text and structur data

Asked 2020-Apr-22 at 09:12

For each instance, I have the text and tabular data. I was wondering if there is any way I can use Bert_classification and combine the result to classify the whole dataset without overfitting. Is there any way to make two different classifications for text and tabular data and combine them together?

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ANSWER

Answered 2020-Apr-22 at 09:12

BERT can return a vector, so-called [CLS] vector that can be used as an input to any further model. So, the answer is yes: you can process your text with BERT and get the single-vector representation, get whatever representation/features you do for your tabular data, concatenate them and train a classifier on top of them.

Source https://stackoverflow.com/questions/61347507

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

Vulnerabilities

No vulnerabilities reported

Install bert_classification

You can download it from GitHub.
You can use bert_classification like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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