GeDi | GeDi : Generative Discriminator Guided Sequence | Natural Language Processing library
kandi X-RAY | GeDi Summary
kandi X-RAY | GeDi Summary
GeDi is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch, Generative adversarial networks, Neural Network, Transformer applications. GeDi has no bugs, it has a Permissive License and it has low support. However GeDi has 1 vulnerabilities and it build file is not available. You can download it from GitHub.
GeDi is a method of using class-conditional language models (which we refer to as generative discriminators (GeDis)) to guide generation from other (potentially much larger) language models. This has several advantages over finetuning large language models directly including:. GeDi is a form of discriminator guided generation. A discriminator that can classify an attribute could be used to guide language model generation towards that attribute by classifying the sequences that result from candidate next tokens. However, using a normal discriminator (such as BERT) to do this would be very computationally expensive during generation, since it would require feeding in every candidate next token one-by-one to the discriminator to be classified. However, using generative discriminators, we can very efficiently classify candidate next tokens during generation using Bayes rule (see Section 3.1 of the paper). As an added bonus, generative discriminators can be used as zero shot classifiers, and can therefore be used to guide generation towards unseen topics.
GeDi is a method of using class-conditional language models (which we refer to as generative discriminators (GeDis)) to guide generation from other (potentially much larger) language models. This has several advantages over finetuning large language models directly including:. GeDi is a form of discriminator guided generation. A discriminator that can classify an attribute could be used to guide language model generation towards that attribute by classifying the sequences that result from candidate next tokens. However, using a normal discriminator (such as BERT) to do this would be very computationally expensive during generation, since it would require feeding in every candidate next token one-by-one to the discriminator to be classified. However, using generative discriminators, we can very efficiently classify candidate next tokens during generation using Bayes rule (see Section 3.1 of the paper). As an added bonus, generative discriminators can be used as zero shot classifiers, and can therefore be used to guide generation towards unseen topics.
Support
Quality
Security
License
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Support
GeDi has a low active ecosystem.
It has 102 star(s) with 23 fork(s). There are 11 watchers for this library.
It had no major release in the last 6 months.
There are 2 open issues and 7 have been closed. On average issues are closed in 21 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of GeDi is current.
Quality
GeDi has 0 bugs and 0 code smells.
Security
GeDi has 1 vulnerability issues reported (1 critical, 0 high, 0 medium, 0 low).
GeDi code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
GeDi is licensed under the BSD-3-Clause License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
Reuse
GeDi releases are not available. You will need to build from source code and install.
GeDi has no build file. You will be need to create the build yourself to 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 GeDi and discovered the below as its top functions. This is intended to give you an instant insight into GeDi implemented functionality, and help decide if they suit your requirements.
- Train the model
- Evaluate the model
- Load and cache examples
- Save the model weights to disk
- Add sep_id to batch
- Forward attention
- Merge the head of x
- Splits the input tensors
- Perform attention
- Resize token embedding
- Resize token embeddings
- Set embeddings
- Resized embeddings
- Load a pretrained model from pretrained model
- Takes input embeddings
- Tries to tie the weights
- Return the embeddings of this model
- Generate a trained model
- Calculate banned ngrams
- Generate a no beam search
- Prompts the user for GPT3 logits
- Process training and binary data
- Load and cache training examples
- Prune the given heads
- Saves the trained model weights
- Prune a layer
- Adjust length to model length
Get all kandi verified functions for this library.
GeDi Key Features
No Key Features are available at this moment for GeDi.
GeDi Examples and Code Snippets
No Code Snippets are available at this moment for GeDi.
Community Discussions
Trending Discussions on GeDi
QUESTION
How do I store a new array all checked data from a table in REACT?
Asked 2021-Jun-20 at 12:08
const students = [
{ _id: 1, name: "Ahmed Ali" },
{ _id: 2, name: "John Doe" },
{ _id: 3, name: "Jane Doe" },
{ _id: 4, name: "Farah Gedi" },
{ _id: 5, name: "Mustafa Jama" }
]
const attendCheck = ({ target: { name, value, id } }) => {
// logics will go here...
}
ANSWER
Answered 2021-Jun-20 at 12:08If you want store only id student, you can add a state is array to store selected id student:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install GeDi
You can download it from GitHub.
You can use GeDi 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 GeDi 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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