BERT-NER | Pytorch-Named-Entity-Recognition-with-BERT | Natural Language Processing library

by kamalkraj Python Version: Current License: AGPL-3.0

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

kandi X-RAY | BERT-NER Summary

BERT-NER is a Python library typically used in Artificial Intelligence, Natural Language Processing, Pytorch, Bert applications. BERT-NER has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has medium support. You can download it from GitHub.

Pytorch-Named-Entity-Recognition-with-BERT

Support

Quality

Security

License

Reuse

Support

BERT-NER has a medium active ecosystem.

It has 1106 star(s) with 272 fork(s). There are 24 watchers for this library.

It had no major release in the last 6 months.

There are 31 open issues and 67 have been closed. On average issues are closed in 4 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of BERT-NER is current.

Quality

BERT-NER has 0 bugs and 0 code smells.

Security

BERT-NER has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

BERT-NER code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

BERT-NER is licensed under the AGPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

BERT-NER 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.

Installation instructions, examples and code snippets are available.

It has 599 lines of code, 22 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Convert examples to features
Tokenize text
Predict text
Return predictions for the given text
Preprocess the input text
Get train examples
Reads a text file
Create input examples
Read a TSV file
Get dev examples
Get test examples

Get all kandi verified functions for this library.

BERT-NER Key Features

No Key Features are available at this moment for BERT-NER.

BERT-NER Examples and Code Snippets

No Code Snippets are available at this moment for BERT-NER.

Community Discussions

Trending Discussions on BERT-NER

Python logger format broken: I0716 instead of INFO

Tensorflow _tpu_ops.so not found while using GPU

QUESTION

Python logger format broken: I0716 instead of INFO

Asked 2019-Jul-18 at 12:39

By some reason python logger format sometimes kinda broken. I'm not sure what's wrong, looks like encoding issue:

...

ANSWER

Answered 2019-Jul-18 at 12:39

Python API

If you just want to customize tensorflows logging format, replace the formatter in absl and tensorflow loggers:

Source https://stackoverflow.com/questions/57065036

QUESTION

Tensorflow _tpu_ops.so not found while using GPU

Asked 2019-Mar-24 at 15:31

I ported this BERT NER github code to google colab, where I manually set the flags to run it (https://github.com/kyzhouhzau/BERT-NER).

I set use_tpu to False, so it should be using GPU.

flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")

The TF version used on colab is 1.13.1 and the command tf.test.gpu_device_name() returns '/device:GPU:0'.

This is the error message that I get when running tf.app.run(). Is this failing because it's looking for a TPU? How can I fix it? Thanks for your help!

...

ANSWER

Answered 2019-Mar-24 at 15:31

I figured it out. When I was downloading the tf_metrics library from https://github.com/guillaumegenthial/tf_metrics.git using !pip install git+https://github.com/guillaumegenthial/tf_metrics.git, it somehow re-installed tensorflow-gpu and my guess is that it corrupted it.

I downloaded tf_metrics.py separately instead and it's now working on google colab.

Source https://stackoverflow.com/questions/55307840

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

Vulnerabilities

No vulnerabilities reported

Install BERT-NER

install cmake, tested with cmake version 3.10.2. unzip downloaded model and libtorch in BERT-NER. NB: Bert-Base C++ model is split in to two parts.
install cmake, tested with cmake version 3.10.2
unzip downloaded model and libtorch in BERT-NER
Compile C++ App cd cpp-app/ cmake -DCMAKE_PREFIX_PATH=../libtorch make
Runing APP ./app ../base
Bert Feature extractor and NER classifier.
This is done because jit trace don't support input depended for loop or if conditions inside forword function of model.