nlp-benchmarks

by ArdalanM Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | nlp-benchmarks Summary

nlp-benchmarks is a Python library. nlp-benchmarks has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

nlp-benchmarks

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nlp-benchmarks has a low active ecosystem.

It has 113 star(s) with 20 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 3 have been closed. On average issues are closed in 106 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of nlp-benchmarks is current.

Quality

nlp-benchmarks has 0 bugs and 0 code smells.

Security

nlp-benchmarks has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

nlp-benchmarks code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

nlp-benchmarks 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

nlp-benchmarks 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.

nlp-benchmarks saves you 858 person hours of effort in developing the same functionality from scratch.

It has 1964 lines of code, 168 functions and 15 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed nlp-benchmarks and discovered the below as its top functions. This is intended to give you an instant insight into nlp-benchmarks implemented functionality, and help decide if they suit your requirements.

Test for training
Step the optimizer
Data generator
Generate a matrix where each element in the given size
Download a file from origin
Validate a file
Apply softmax
Scaled softmax
Perform a forward projection
Compute the attention layer
Predict sentences with attention
Reorder sent data
Train the model
Calculate the metrics
Loads available datasets
Transform sentences into sequences
Get argument parser
Transform a list of sentences
Greedy decoding
Concatenate the embedding
Colorize words and values
Save a tensor to a file
Make the standard mask of the given padding
Test the train tranformer

Get all kandi verified functions for this library.

nlp-benchmarks Key Features

No Key Features are available at this moment for nlp-benchmarks.

nlp-benchmarks Examples and Code Snippets

No Code Snippets are available at this moment for nlp-benchmarks.

Community Discussions

Trending Discussions on nlp-benchmarks

Understanding nn.Sequential in convolutional layers

QUESTION

Understanding nn.Sequential in convolutional layers

Asked 2020-Jun-19 at 11:43

I am new to PyTorch/Deep learning and I am trying to understand the use of the following line to define a convolutional layer:

self.layer1 = nn.Sequential(nn.Conv1d(input_dim, n_conv_filters, kernel_size=7, padding=0), nn.ReLU(), nn.MaxPool1d(3))

I understand that that it is creating a 1d convolutional layer to the network with max pooling 3 wide. However, I don't understand the function of the sequential module or RelU. How do these function in creating a layer?

For reference, the rest of the code can be found here: https://github.com/ArdalanM/nlp-benchmarks/blob/master/src/cnn/net.py

...

ANSWER

Answered 2020-Jun-19 at 11:43

As per the description provided it seems you are in the process of developing a convolutional architecture for a problem (More likely a Computer Vision one as CNNs are usually targeted for solving CV problems).

Now talking about the code by using Sequential module you are telling the PyTorch that you are developing an architecture that will work in a sequential manner and by specifying ReLU you are bringing the concept of Non-Linearity in the picture (ReLU is one of the widely used activation functions in the Deep learning framework). Non-Linearity helps CNNs to generalize to complex decision boundaries and ultimately helps them to perform better.

PS: I recommend reviewing the https://towardsdatascience.com/convolutional-neural-network-for-image-classification-with-implementation-on-python-using-pytorch-7b88342c9ca9 for getting better idea from a coder perspective.

Source https://stackoverflow.com/questions/62468497

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install nlp-benchmarks

You can download it from GitHub.
You can use nlp-benchmarks 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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