NLP-Benchmark | NLP-Benchmark -

by Koredotcom Python Version: BP-6.4.0+ License: No License

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

kandi X-RAY | NLP-Benchmark Summary

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

NLP-Benchmark

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NLP-Benchmark has a low active ecosystem.

It has 7 star(s) with 9 fork(s). There are 13 watchers for this library.

It had no major release in the last 12 months.

NLP-Benchmark has no issues reported. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of NLP-Benchmark is BP-6.4.0+

Quality

NLP-Benchmark has no bugs reported.

Security

NLP-Benchmark has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

NLP-Benchmark 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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NLP-Benchmark releases are available to install and integrate.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

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

Write a csv file
Generate a formula
Inserts the number of instructions into the database
Replace row at index
Create a bro bot
Create Builder streams
Adds a callback to kore
Add intent to kore
Process the input file
Language validation
Display information about the user
Read input from user
Test the bot
Add an intent to a bot
Add utterances to a bot
Add a new intent to bot
Processambiguity assignment
Create a bot in Luis
Translate dialog components
Prepare intent in luis
Create intents in kore
Login to Kore
Get the threshold for intent
Add an intent and utterances
Create a config file
Create a new Watson bot

Get all kandi verified functions for this library.

NLP-Benchmark Key Features

No Key Features are available at this moment for NLP-Benchmark.

NLP-Benchmark Examples and Code Snippets

No Code Snippets are available at this moment for NLP-Benchmark.

Community Discussions

Trending Discussions on NLP-Benchmark

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-Benchmark

You can download it from GitHub.
You can use NLP-Benchmark 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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