Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses
kandi X-RAY | Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses Summary
kandi X-RAY | Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses Summary
Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses is a Python library. Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses has no bugs, it has no vulnerabilities and it has low support. However Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses build file is not available. You can download it from GitHub.
Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses
Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses
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Support
Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses has a low active ecosystem.
It has 8 star(s) with 3 fork(s). There are no watchers for this library.
It had no major release in the last 6 months.
Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses is current.
Quality
Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses has no bugs reported.
Security
Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses 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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Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses releases are not available. You will need to build from source code and install.
Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses has no build file. You will be need to create the build yourself to build the component from source.
Top functions reviewed by kandi - BETA
kandi has reviewed Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses and discovered the below as its top functions. This is intended to give you an instant insight into Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses implemented functionality, and help decide if they suit your requirements.
- Reads examples from file
- Build vocabulary
- Add a word to the corpus
- Add a word
- Convert set of sets to index
- Get the value for a word
- Return the value of the index w
- Reads examples from a test file
- Train model
- Train a training epoch
- Evaluate an epoch
- Return the word corresponding to the given index
- Extract the allowed transitions from the id2label
- Check if a transition is allowed
- Loads a word embedding
- Forward attention on query
- Compute the attention
- Compute the embedding
- Perform viterbi decoding
- Calculate the loss of the logit
- Evaluate the encoder
- Compute the score of the given features
- Compute negative log - likelihood loss
- Compute the best path for the given features
- Decorator that checks the built vocabulary
- Updates the word list
Get all kandi verified functions for this library.
Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses Key Features
No Key Features are available at this moment for Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses.
Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses Examples and Code Snippets
No Code Snippets are available at this moment for Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses.
Community Discussions
No Community Discussions are available at this moment for Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses
You can download it from GitHub.
You can use Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses 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 Multi-Task-Learning-using-Uncertainty-to-Weigh-Losses 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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