ConvBert | new architecture ConvBERT for pre-training based language | Machine Learning library
kandi X-RAY | ConvBert Summary
kandi X-RAY | ConvBert Summary
ConvBert is a Python library typically used in Artificial Intelligence, Machine Learning, Pytorch applications. ConvBert has no bugs, it has no vulnerabilities and it has low support. However ConvBert build file is not available and it has a Non-SPDX License. You can download it from GitHub.
In this repo, we introduce a new architecture ConvBERT for pre-training based language model. The code is tested on a V100 GPU. For detailed description and experimental results, please refer to our NeurIPS 2020 paper ConvBERT: Improving BERT with Span-based Dynamic Convolution.
In this repo, we introduce a new architecture ConvBERT for pre-training based language model. The code is tested on a V100 GPU. For detailed description and experimental results, please refer to our NeurIPS 2020 paper ConvBERT: Improving BERT with Span-based Dynamic Convolution.
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Quality
Security
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Support
ConvBert has a low active ecosystem.
It has 196 star(s) with 40 fork(s). There are 8 watchers for this library.
It had no major release in the last 6 months.
There are 2 open issues and 15 have been closed. On average issues are closed in 49 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of ConvBert is current.
Quality
ConvBert has 0 bugs and 0 code smells.
Security
ConvBert has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
ConvBert code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
ConvBert has a Non-SPDX License.
Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
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ConvBert releases are not available. You will need to build from source code and install.
ConvBert 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 ConvBert and discovered the below as its top functions. This is intended to give you an instant insight into ConvBert implemented functionality, and help decide if they suit your requirements.
- Transformer transformer model
- Get the shape of a tensor
- Apply dropout to input tensor
- Attention layer
- Feature features
- Improve the explanation of the answer span
- Check if the word spans with the given position
- Get prediction module
- Get the sequence output
- Create an optimizer
- Returns the masked lm output
- Write tf examples
- Process MRPC data
- Train or eval pretraining
- Fits the given example
- Construct a pretraining
- Creates attention_mask_from_tensor
- Get the discriminator output
- Get the prediction module
- Calculate precision - recall curve
- Embedding postprocessor
- Mask the input tensor
- Compute a word embedding table
- Run Finetuning
- Feature features_a
- Tokenize text
Get all kandi verified functions for this library.
ConvBert Key Features
No Key Features are available at this moment for ConvBert.
ConvBert Examples and Code Snippets
No Code Snippets are available at this moment for ConvBert.
Community Discussions
Trending Discussions on ConvBert
QUESTION
ValueError: Unrecognized model in ./MRPC/. Should have a `model_type` key in its config.json, or contain one of the following strings in its name
Asked 2022-Jan-13 at 14:10
Goal: Amend this Notebook to work with Albert and Distilbert models
Kernel: conda_pytorch_p36. I did Restart & Run All, and refreshed file view in working directory.
Error occurs in Section 1.2, only for these 2 new models.
For filenames etc., I've created a variable used everywhere:
...ANSWER
Answered 2022-Jan-13 at 14:10Explanation:
When instantiating AutoModel, you must specify a model_type parameter in ./MRPC/config.json file (downloaded during Notebook runtime).
List of model_types can be found here.
Code that appends model_type to config.json, in the same format:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install ConvBert
You can download it from GitHub.
You can use ConvBert 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 ConvBert 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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