fastNLP | fastNLP: A Modularized and Extensible NLP Framework Currently still in incubation | Natural Language Processing library

by fastnlp Python Version: v0.6.0 License: Apache-2.0

X-Ray Key Features Code Snippets(4)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | fastNLP Summary

fastNLP is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. fastNLP has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install fastNLP' or download it from GitHub, PyPI.

fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.

Support

Quality

Security

License

Reuse

Support

fastNLP has a medium active ecosystem.

It has 2938 star(s) with 458 fork(s). There are 83 watchers for this library.

It had no major release in the last 12 months.

There are 55 open issues and 154 have been closed. On average issues are closed in 19 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of fastNLP is v0.6.0

Quality

fastNLP has 0 bugs and 0 code smells.

Security

fastNLP has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

fastNLP code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

fastNLP is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

fastNLP releases are available to install and integrate.

Deployable package is available in PyPI.

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

Installation instructions are not available. Examples and code snippets are available.

fastNLP saves you 18161 person hours of effort in developing the same functionality from scratch.

It has 35946 lines of code, 2658 functions and 323 files.

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

Top functions reviewed by kandi - BETA

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

Generate a random token
Create a logits processor
Expand inputs for a single generation
Initialize logits processor
Create a model from pretrained model
Return the deep deep power configuration
Returns True if ZF is zero
Returns the path to the given URL or None
Gather objects from obj
Load checkpoint files
Load embedding
Load a vocabulary without vocab
Create default config
Fetch objects from obj
Load checkpoint files
Forward computation
Create a configuration object from a pretrained model
Compute the forward query
Run training
Create a Token from a pretrained model
Forward computation
Set backend
Set DistributedSampler
Perform a forward projection
Decorator to register custom callbacks
Compute the query tensor

Get all kandi verified functions for this library.

fastNLP Key Features

No Key Features are available at this moment for fastNLP.

fastNLP Examples and Code Snippets

OntoNotes-5.0-NER

Python

Lines of Code : 80

License : No License

Copy

OntoNotes-5.0-NER
  -conll-formatted-ontenotes-5.0/
  -collect_conll.py
  -README.md
  -..
  -onotenotes-release-5.0/

$ conda create --name py27 python=2.7
$ source activate py27

./conll-formatted-ontonotes-5.0/scripts/skeleton2conll.sh -D ./ontono

CoLAKE,Prepare your environment

Python

Lines of Code : 10

License : Permissive (MIT)

Copy

conda create --name colake python=3.7
source activate colake

git clone https://github.com/fastnlp/fastNLP.git
cd fastNLP/ & python setup.py install
git clone https://github.com/fastnlp/fitlog.git
cd fitlog/ & python setup.py install
pip inst

Black-Box-Tuning,Prepare your environment

Python

Lines of Code : 9

License : Permissive (MIT)

Copy

conda create --name bbt python=3.8
conda activate bbt
pip install transformers==4.1.1
pip install datasets
pip install fastNLP
pip install cma
pip install sklearn
git clone https://github.com/txsun1997/Black-Box-Tuning
cd Black-Box-Tuning

AttributeError: module 'torchtext.data' has no attribute 'Field'

Python

Lines of Code : 2

License : Strong Copyleft (CC BY-SA 4.0)

Copy

from torchtext.legacy import data

Community Discussions

Trending Discussions on fastNLP

AttributeError: module 'torchtext.data' has no attribute 'Field'

QUESTION

AttributeError: module 'torchtext.data' has no attribute 'Field'

Asked 2021-Mar-07 at 15:08

I want to run a git project used pytorch and torchtext but when I run it, it raise error:

...

ANSWER

Answered 2021-Mar-07 at 15:08

From TorchText 0.9.0 Release Notes

torchtext.data.Field -> torchtext.legacy.data.Field
This means, all features are still available, but within torchtext.legacy instead of torchtext.

torchtext.data.Field has been moved to torchtext.legacy.data.Field

And the imports would change this way:

Source https://stackoverflow.com/questions/66516388

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

Vulnerabilities

No vulnerabilities reported

Install fastNLP

You can install using 'pip install fastNLP' or download it from GitHub, PyPI.
You can use fastNLP 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 .

Find more information at: