NeuroNER | entity recognition using neural networks | Natural Language Processing library

by Franck-Dernoncourt Python Version: 1.0-dev2 License: MIT

X-Ray Key Features Code Snippets Community Discussions(9)Vulnerabilities Install Support

kandi X-RAY | NeuroNER Summary

NeuroNER is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Tensorflow applications. NeuroNER has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However NeuroNER has 1 bugs. You can install using 'pip install NeuroNER' or download it from GitHub, PyPI.

NeuroNER is a program that performs named-entity recognition (NER). Website: neuroner.com. This page gives step-by-step instructions to install and use NeuroNER.

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Quality

Security

License

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Support

NeuroNER has a medium active ecosystem.

It has 1638 star(s) with 484 fork(s). There are 84 watchers for this library.

It had no major release in the last 12 months.

There are 84 open issues and 67 have been closed. On average issues are closed in 261 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of NeuroNER is 1.0-dev2

Quality

NeuroNER has 1 bugs (1 blocker, 0 critical, 0 major, 0 minor) and 59 code smells.

Security

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

NeuroNER code analysis shows 0 unresolved vulnerabilities.

There are 2 security hotspots that need review.

License

NeuroNER is licensed under the MIT License. This license is Permissive.

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

Reuse

NeuroNER 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, examples and code snippets are available.

NeuroNER saves you 1037 person hours of effort in developing the same functionality from scratch.

It has 2353 lines of code, 92 functions and 15 files.

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

Top functions reviewed by kandi - BETA

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

Load a dataset
Convert to indices
Creates the graph folder where the model is saved
Fits the model
Restore from pretrained model
Load token embeddings
Load embeddings from a pretrained model
Load parameters from file
Convert dtypes dtypes to strings
Convert xml files tobrat
Convert CONLL to BIOes format
Load token embedding file
Performs prediction step
Write results to a plot
Fetch a trained model
Check the validity of the BRAT - XML files
Loads token embedding file
Fetch package data
Checks the parameter validity
Fit the model
Convert from BRAT to CONLL
Parse the arguments
Evaluate the model
Predict for given text
Prepare a pretrained model
Plot classification report

Get all kandi verified functions for this library.

NeuroNER Key Features

No Key Features are available at this moment for NeuroNER.

NeuroNER Examples and Code Snippets

No Code Snippets are available at this moment for NeuroNER.

Community Discussions

Trending Discussions on NeuroNER

What Loss Or Reward Is Backpropagated In Policy Gradients For Reinforcement Learning?

How to replace Character Embedding using LSTM with CharCNN?

Tensorflow Error Concatenating Char and Word embedding

Can someone check what is wrong with my xor neural network code

Altering string using a list of dictionaries

Use lambda, apply, and join function on a pandas dataframe

How to check if distutils are installed and used properly?

Implementing RNN and LSTM into DQN Pytorch code

Older versions of spaCy throws "KeyError: 'package'" error when trying to install a model

QUESTION

What Loss Or Reward Is Backpropagated In Policy Gradients For Reinforcement Learning?

Asked 2020-Oct-24 at 15:29

I have made a small script in Python to solve various Gym environments with policy gradients.

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ANSWER

Answered 2020-Sep-09 at 06:58

The loss here depends on what output on each problem. Generaly, loss for backpropagate should be a number that represents for everything you have processed. For policy gradient, it will be the reward that it think it will get compare with the original reward, the log is just a way to bring it back to a probabily random variable. Single dimension. If you want to inspect the behavior behind codes, you should always check the shape/dimension between each process to fully understand

Source https://stackoverflow.com/questions/63602222

QUESTION

How to replace Character Embedding using LSTM with CharCNN?

Asked 2020-Oct-07 at 06:54

I'm working with this repo https://github.com/Franck-Dernoncourt/NeuroNER It is using LSTM for Char-Level-Embedding and I want to use CNN for this.

Link where it is using LSTM for Char-Level-Embedding

I tried using multiple implementation of CharCNN like this

...

ANSWER

Answered 2020-Oct-05 at 14:57

As the error message suggests, the rank of your tensors are different for concat operation hence causing the error.

Below is the simple code to reproduce your error.

Code to reproduce the error -

Source https://stackoverflow.com/questions/64182501

QUESTION

Tensorflow Error Concatenating Char and Word embedding

Asked 2020-Oct-07 at 06:46

I want to concatenate Char Embeddings (generated using CNN) with my Word Embedding (using Glove vectors) but getting the error since the shape of Char Embeddings is different from Word Embeddings.

How can fix the error or concatenate these?

...

ANSWER

Answered 2020-Oct-07 at 06:46

Finally, I was able to resolve the problem but Flattening the char embedding then it can be easily concatenated with Word embeddings. By adding this line, It worked.

Source https://stackoverflow.com/questions/64231130

QUESTION

Can someone check what is wrong with my xor neural network code

Asked 2019-Nov-17 at 17:58

I've been trying to create a XOR neural network but the outputs would always converge to a certain value (like 1, or 0, or 0.5) for all inputs. This is my latest attempt:

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ANSWER

Answered 2019-Nov-17 at 17:58

Your sigmoid derivative was wrong, it should be as follows:

Source https://stackoverflow.com/questions/58902225

QUESTION

Altering string using a list of dictionaries

Asked 2019-Oct-22 at 23:17

Background

I am using NeuroNER http://neuroner.com/ to label text data sample_string as seen below.

sample_string = 'Patient Jane Candy was seen by Dr. Smith on 12/1/2000 and her number is 1111112222'

Output (using NeuroNER)

My output is a list of dictionary dic_list

...

ANSWER

Answered 2019-Oct-11 at 17:35

I may be missing something but you can just use .replace():

Source https://stackoverflow.com/questions/56991572

QUESTION

Use lambda, apply, and join function on a pandas dataframe

Asked 2019-Aug-25 at 15:32

Goal

Apply deid_notes function to df

Background

I have a df that resembles this sample df

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ANSWER

Answered 2019-Jul-13 at 21:51

Assuming you are returning a pandas series as output from deid_notes function taking text as the only input argument. Pass the axis = 1 argument to the apply instead of died_notes. For eg.

Source https://stackoverflow.com/questions/57022698

QUESTION

How to check if distutils are installed and used properly?

Asked 2018-Nov-05 at 16:54

I am using NeuroNER

Unfortunately,I have problem to run python main.py NeuroNER version: 1.0-dev

...

ANSWER

Answered 2018-Nov-05 at 16:54

You don't have an issue with distutils. The name to be defined is utils. So doing import distutils.util will never fix that issue.

If you look at the source, there's a utils.py module with the convert_configparser_to_dictionary function defined. You can see that here.

Try import utils instead. This will define the name utils, and it should work as expected. Note: I cannot test this.

HTH.

Source https://stackoverflow.com/questions/53158556

QUESTION

Implementing RNN and LSTM into DQN Pytorch code

Asked 2018-Apr-08 at 16:36

I have some troubles finding some example on the great www to how i implement a recurrent neural network with LSTM layer into my current Deep q-network in Pytorch so it become a DRQN.. Bear with me i am just getting started.. Futhermore, I am NOT working with images processing, thereby CNN so do not worry about this. My states are purely temperatures values.

Here is my code that i am currently train my DQN with:

...

ANSWER

Answered 2018-Apr-08 at 16:36

From my point of view, I think you could add RNN, LSTM layer to the Network#__init__,Network#forward; shape of data should be reshaped into sequences...

For more detail, I think you should read these two following articles; after that implementing RNN, LSTM not hard as it seem to be.

http://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html#sphx-glr-beginner-nlp-sequence-models-tutorial-py

http://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html

Source https://stackoverflow.com/questions/49065222

QUESTION

Older versions of spaCy throws "KeyError: 'package'" error when trying to install a model

Asked 2017-Jul-19 at 17:15

I use spaCy 1.6.0 on Ubuntu 14.04.4 LTS x64 with python3.5. To install the English model of spaCy, I tried to run:

This gives me the error message:

...

ANSWER

Answered 2017-Jul-19 at 17:15

TL;DR

That's because the sputnik package has been deprecated since spacy > 1.5.

Best bet is to upgrade your Spacy to the latest one. Or at least up till 1.7 =)

Otherwise, you could try:

Source https://stackoverflow.com/questions/43010861

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

Vulnerabilities

No vulnerabilities reported

Install NeuroNER

For GPU support, GPU requirements for Tensorflow must be satisfied. If your system does not meet these requirements, you should use the CPU version. To install neuroner:. You will also need to download some support packages.
The English language module for Spacy:
Download word embeddings from http://neuroner.com/data/word_vectors/glove.6B.100d.zip, unzip them to the folder ./data/word_vectors
Load sample datasets. These can be loaded by calling the neuromodel.fetch_data() function from a Python interpreter or with the --fetch_data argument at the command line.
Load a pretrained model. The models can be loaded by calling the neuromodel.fetch_model() function from a Python interpreter or with the --fetch_trained_models argument at the command line.

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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