nmt-chatbot | chatbot using NMT - Neural Machine Translation | Chat library
kandi X-RAY | nmt-chatbot Summary
kandi X-RAY | nmt-chatbot Summary
nmt-chatbot is a Python library typically used in Messaging, Chat, Tensorflow, Neural Network, Transformer applications. nmt-chatbot has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.
NMT Chatbot
NMT Chatbot
Support
Quality
Security
License
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Support
nmt-chatbot has a low active ecosystem.
It has 378 star(s) with 221 fork(s). There are 35 watchers for this library.
It had no major release in the last 6 months.
There are 67 open issues and 107 have been closed. On average issues are closed in 163 days. There are 1 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of nmt-chatbot is v0.2
Quality
nmt-chatbot has 0 bugs and 0 code smells.
Security
nmt-chatbot has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
nmt-chatbot code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
nmt-chatbot is licensed under the GPL-3.0 License. This license is Strong Copyleft.
Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.
Reuse
nmt-chatbot 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.
nmt-chatbot saves you 582 person hours of effort in developing the same functionality from scratch.
It has 1359 lines of code, 36 functions and 19 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed nmt-chatbot and discovered the below as its top functions. This is intended to give you an instant insight into nmt-chatbot implemented functionality, and help decide if they suit your requirements.
- Process questions
- Generate a score for each answer
- Normalize newlines
- Return the best answer based on score
- Replace all occurrences in answers
- Start inference
- Apply preprocessing
- Perform inference
- Prepare files for training
- Replace characters in the entity
- Read lines from a file
- Train the model
- Train TF
- Replace all occurrences in the answer string
- Detokenize the response
- Return a score for the given answers
- Copy a file or folder
- Tokenize a sentence
- Replace characters in an entity
- Internal inference method
Get all kandi verified functions for this library.
nmt-chatbot Key Features
No Key Features are available at this moment for nmt-chatbot.
nmt-chatbot Examples and Code Snippets
No Code Snippets are available at this moment for nmt-chatbot.
Community Discussions
Trending Discussions on nmt-chatbot
QUESTION
Failed to load native tensorflow runtime- Paperspace
Asked 2018-Jun-25 at 20:49
I was using Paperspace's machine(has all the ml stuff built in) to train a model. Everything seemed fine until I ran the training file. Error message shown below:
...ANSWER
Answered 2018-Jun-25 at 20:49Turns out it's Tensorflow version problem. The following steps solved the problem
- pip uninstall tensorflow
- pip install tensorflow==1.4 #yourdesiredTFversion
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install nmt-chatbot
You can download it from GitHub.
You can use nmt-chatbot 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 nmt-chatbot 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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