bert-japanese | BERT models for Japanese text | Natural Language Processing library
kandi X-RAY | bert-japanese Summary
kandi X-RAY | bert-japanese Summary
bert-japanese is a Python library typically used in Artificial Intelligence, Natural Language Processing, Pytorch, Bert applications. bert-japanese 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.
BERT models for Japanese text.
BERT models for Japanese text.
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Quality
Security
License
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Support
bert-japanese has a low active ecosystem.
It has 429 star(s) with 48 fork(s). There are 19 watchers for this library.
It had no major release in the last 6 months.
There are 13 open issues and 18 have been closed. On average issues are closed in 57 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of bert-japanese is v3.0
Quality
bert-japanese has 0 bugs and 0 code smells.
Security
bert-japanese has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
bert-japanese code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
bert-japanese is licensed under the Apache-2.0 License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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bert-japanese 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 are not available. Examples and code snippets are available.
Top functions reviewed by kandi - BETA
kandi has reviewed bert-japanese and discovered the below as its top functions. This is intended to give you an instant insight into bert-japanese implemented functionality, and help decide if they suit your requirements.
- Create TrainingInstances
- Creates a sequence of tokens from a document
- Creates the predicted LM predictions for the given tokens
- Tokenize text
- Truncate a sequence pair
- Write examples to examples
- Create a TF feature feature
- Creates a feature feature
- Convert tf2 checkpoint to PyTorch model
- Load weights in Tensorflow
- Convert a checkpoint to TensorFlow
- Preprocess text
- Return True if text contains equations
Get all kandi verified functions for this library.
bert-japanese Key Features
No Key Features are available at this moment for bert-japanese.
bert-japanese Examples and Code Snippets
No Code Snippets are available at this moment for bert-japanese.
Community Discussions
Trending Discussions on bert-japanese
QUESTION
HuggingFace for Japanese tokenizer
Asked 2020-Jul-17 at 08:28
I recently tested on the below code based on the source: https://github.com/cl-tohoku/bert-japanese/blob/master/masked_lm_example.ipynb
...ANSWER
Answered 2020-Jul-17 at 08:28With a quick check, no errors for me, maybe there are some version issues in your case?
From what it looks like, the error occurs with the BertJapaneseTokenizer, so possibly the version of the tokenizer (mecab?) that you have is incompatible with your environment.
The mecab-python in my environment:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install bert-japanese
You can download it from GitHub.
You can use bert-japanese 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.
You can use bert-japanese 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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