PyTorch-NLP | Basic Utilities for PyTorch Natural Language Processing | Natural Language Processing library

by PetrochukM Python Version: 0.5.0 License: BSD-3-Clause

X-Ray Key Features Code Snippets(3)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | PyTorch-NLP Summary

PyTorch-NLP is a Python library typically used in Manufacturing, Utilities, Energy, Utilities, Artificial Intelligence, Natural Language Processing, Deep Learning, Pytorch applications. PyTorch-NLP has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

Basic Utilities for PyTorch Natural Language Processing (NLP)

Support

Quality

Security

License

Reuse

Support

PyTorch-NLP has a medium active ecosystem.

It has 2157 star(s) with 260 fork(s). There are 57 watchers for this library.

It had no major release in the last 12 months.

There are 20 open issues and 49 have been closed. On average issues are closed in 101 days. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of PyTorch-NLP is 0.5.0

Quality

PyTorch-NLP has 0 bugs and 0 code smells.

Security

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

PyTorch-NLP code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

PyTorch-NLP 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

PyTorch-NLP releases are available to install and integrate.

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

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Load SNLI dataset
Check if a file is downloaded
Download a file or directory
Download a file from drive
Performs softmax computation
Calculate the log probability for the given weights
Splits the hiddens onto the given targets
Train the model
Repack hidden layers
Wrapper for batch_encode
Packs the given tensors into a sequence of tensors
Decodes the given list of subtokens
Encode the raw_text
Find the version string
Add stylesheet
Builds a SubwordTextTokenizer from the given corpus
Collate a batch tensor
Encodes an iterable of objects
Wrapper for batch_decode
Encodes a label
Create a directory structure
Evaluate the model
Parse arguments
Calculate the softmax of the model
Read a file into memory
Load the model

Get all kandi verified functions for this library.

PyTorch-NLP Key Features

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

PyTorch-NLP Examples and Code Snippets

PyTorch Natural Language Processing Project Template,Usage,Config file format

Python

Lines of Code : 60

License : No License

Copy

{
  "name": "Rick_And_Morty",
  "n_gpu": 1,
  "embedding": {
    "type": "GloveEmbedding",
    "args": {
      "name": "6B",
      "dim": 100
    }
  },
  "arch": {
    "type": "MortyFire",
    "args": {
      "lstm_size": 256,
      "seq_length": 20

PyTorch Natural Language Processing Project Template,Folder Structure

Python

Lines of Code : 52

License : No License

Copy

pytorch-nlp-project-template/
│
├── train.py - main script to start training
├── test.py - evaluation of trained model
│
│
├── config.json - holds configuration for training
├── parse_config.py - class to handle config file and cli options
│
├── new_

pytorch-nlp-tutorial,Day 2,Day 2 Data

Jupyter Notebook

Lines of Code : 26

License : No License

Copy

# install anaconda (if needed)

conda create -n dl4nlp python=3.6
source activate dl4nlp
conda install ipython
conda install jupyter
python -m ipykernel install --user --name dl4nlp

# install pytorch
# visit pytorch.org

# assume we are inside a fol

Community Discussions

Trending Discussions on PyTorch-NLP

pytorch embedding index out of range

PyTorch custom loss function

QUESTION

pytorch embedding index out of range

Asked 2019-May-06 at 19:00

I'm following this tutorial here https://cs230-stanford.github.io/pytorch-nlp.html. In there a neural model is created, using nn.Module, with an embedding layer, which is initialized here

...

ANSWER

Answered 2019-May-06 at 19:00

You've got some things wrong. Please correct those and re-run your code:

params['vocab_size'] is the total number of unique tokens. So, it should be len(vocab) in the tutorial.
params['embedding_dim'] can be 50 or 100 or whatever you choose. Most folks would use something in the range [50, 1000] both extremes inclusive. Both Word2Vec and GloVe uses 300 dimensional embeddings for the words.
self.embedding() would accept arbitrary batch size. So, it doesn't matter. BTW, in the tutorial the commented things such as # dim: batch_size x batch_max_len x embedding_dim indicates the shape of output tensor of that specific operation, not the inputs.

Source https://stackoverflow.com/questions/56010551

QUESTION

PyTorch custom loss function

Asked 2018-Dec-30 at 19:26

How should a custom loss function be implemented ? Using below code is causing error :

...

ANSWER

Answered 2018-Dec-30 at 19:26

Your loss function is programmatically correct except for below:

Source https://stackoverflow.com/questions/53980031

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

Vulnerabilities

No vulnerabilities reported

Install PyTorch-NLP

Make sure you have Python 3.6+ and PyTorch 1.0+. You can then install pytorch-nlp using pip:.
Within an NLP data pipeline, you'll want to implement these basic steps:.

Support

We've released PyTorch-NLP because we found a lack of basic toolkits for NLP in PyTorch. We hope that other organizations can benefit from the project. We are thankful for any contributions from the community.

Find more information at: