Chinese-clinical-NER
kandi X-RAY | Chinese-clinical-NER Summary
kandi X-RAY | Chinese-clinical-NER Summary
Chinese-clinical-NER is a Python library. Chinese-clinical-NER has no vulnerabilities and it has low support. However Chinese-clinical-NER has 4 bugs and it build file is not available. You can download it from GitHub.
CCKS2019中文命名实体识别任务。从医疗文本中识别疾病和诊断、解剖部位、影像检查、实验室检验、手术和药物6种命名实体。现已实现基于jieba和AC自动机的baseline构建、基于BiLSTM和CRF的序列标住模型构建。bert的部分代码主要源于感谢作者。 模型最终测试集得分0.81，还有较大改进空间。可以当做一个baseline。
CCKS2019中文命名实体识别任务。从医疗文本中识别疾病和诊断、解剖部位、影像检查、实验室检验、手术和药物6种命名实体。现已实现基于jieba和AC自动机的baseline构建、基于BiLSTM和CRF的序列标住模型构建。bert的部分代码主要源于感谢作者。 模型最终测试集得分0.81，还有较大改进空间。可以当做一个baseline。
Support
Quality
Security
License
Reuse
Support
Chinese-clinical-NER has a low active ecosystem.
It has 273 star(s) with 63 fork(s). There are 9 watchers for this library.
It had no major release in the last 6 months.
There are 14 open issues and 2 have been closed. On average issues are closed in 11 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Chinese-clinical-NER is current.
Quality
Chinese-clinical-NER has 4 bugs (0 blocker, 0 critical, 0 major, 4 minor) and 301 code smells.
Security
Chinese-clinical-NER has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Chinese-clinical-NER code analysis shows 0 unresolved vulnerabilities.
There are 9 security hotspots that need review.
License
Chinese-clinical-NER 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.
Reuse
Chinese-clinical-NER releases are not available. You will need to build from source code and install.
Chinese-clinical-NER has no build file. You will be need to create the build yourself to build the component from source.
Chinese-clinical-NER saves you 199862 person hours of effort in developing the same functionality from scratch.
It has 200388 lines of code, 614 functions and 100 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed Chinese-clinical-NER and discovered the below as its top functions. This is intended to give you an instant insight into Chinese-clinical-NER implemented functionality, and help decide if they suit your requirements.
- Fit the model
- Train the model
- Convert a model
- Finetune optimizer
- Load a pre - trained model
- Construct an instance from a json file
- Create a model from a pretrained pretrained model
- Tokenize text
- Split text into tokens
- Compute the overlap between two terms
- Load X data from file
- Tokenize a Unicode string
- Get character tag data
- Evaluate the model
- Computes soft confusion matrix
- Runs prediction on data
- Revise index
- Computes the confusion matrix
- Extracts the distance from a document
- Evaluate the category
- Print prediction with pred_filename
- Load a pre - trained model from a pre - trained model
- Rank a sentence
- Get the entity index
- Load word dictionary from file
- Concatenate data into a single entity
Get all kandi verified functions for this library.
Chinese-clinical-NER Key Features
No Key Features are available at this moment for Chinese-clinical-NER.
Chinese-clinical-NER Examples and Code Snippets
No Code Snippets are available at this moment for Chinese-clinical-NER.
Community Discussions
No Community Discussions are available at this moment for Chinese-clinical-NER.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Chinese-clinical-NER
You can download it from GitHub.
You can use Chinese-clinical-NER 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 Chinese-clinical-NER 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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