BLSTM-CRF-NER | BiLSTMCNNCRF NER , using pytorch | Natural Language Processing library

by AngusMonroe Python Version: BLSTM-CRF-NER License: GPL-3.0

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | BLSTM-CRF-NER Summary

BLSTM-CRF-NER is a Python library typically used in Artificial Intelligence, Natural Language Processing, Pytorch applications. BLSTM-CRF-NER has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However BLSTM-CRF-NER build file is not available. You can download it from GitHub.

BiLSTM+CNN+CRF NER, using pytorch

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Quality

Security

License

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Support

BLSTM-CRF-NER has a low active ecosystem.

It has 9 star(s) with 8 fork(s). There are 2 watchers for this library.

It had no major release in the last 12 months.

BLSTM-CRF-NER has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of BLSTM-CRF-NER is BLSTM-CRF-NER

Quality

BLSTM-CRF-NER has 0 bugs and 0 code smells.

Security

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

BLSTM-CRF-NER code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

BLSTM-CRF-NER is licensed under the GPL-3.0 License. This license is Strong Copyleft.

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

Reuse

BLSTM-CRF-NER releases are available to install and integrate.

BLSTM-CRF-NER has no build file. You will be need to create the build yourself to build the component from source.

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

It has 1256 lines of code, 55 functions and 7 files.

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

Top functions reviewed by kandi - BETA

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

Takes a text file
Evaluate the model
Load sentences from file
Replace zero digits
Prepare a training dataset
Create input
Insert singletons into words
Pad a list of words
Update the tag scheme with the given tag scheme
Verify IOB2 tags
Replace IOB tags
Load embeddings from a pretrained embedding file
Create a mapping from a dictionary
Get a batch from data
Pad a sequence
Prepare a dataset
Compute the score for a given sentence
Return the answer
Generate a mapping between sentences
Creates a mapping of unique entity tags to id to id
Negative log likelihood
Given a list of sentences return the word mapping
Prepare a sentence
Generate a random batch
Loads sentences from file
Divide training data
Adjust the learning rate of the optimizer

Get all kandi verified functions for this library.

BLSTM-CRF-NER Key Features

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

BLSTM-CRF-NER Examples and Code Snippets

BLSTM-CRF-NER,File orgnization

Python

Lines of Code : 5

License : Strong Copyleft (GPL-3.0)

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|- train.py 
|- debug.py 
|- [dir] dataset (word library)
|- [dir] evaluation (help tools when training)
|- [dir] models (well-trained models)

BLSTM-CRF-NER,Data Format

Python

Lines of Code : 1

License : Strong Copyleft (GPL-3.0)

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BLSTM-CRF-NER,usage:

Python

Lines of Code : 1

License : Strong Copyleft (GPL-3.0)

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python train.py

Community Discussions

Trending Discussions on BLSTM-CRF-NER

Tensorflow character-level CNN - input shape

QUESTION

Tensorflow character-level CNN - input shape

Asked 2018-Nov-15 at 03:22

I'm trying to add 2-stacked character-level CNNs into a larger neural network system but I'm getting ValueError for the input dimensions.

What I want to achieve is to get orthographic representations for the input words by replacing characters (according to capitalization, or being numeric or alphabetic) and feeding them into CNN. I'm aware that this can be achieved with LSTM/RNN but the requirements indicate using CNN so using another NN is not optional.

Most of the examples out there naturally uses image datasets (MNIST etc.) but not text datasets. So I'm confused and not sure how to "reshape" character embeddings so that they can be valid inputs for the CNN.

So here is the part of the code I'm trying to run:

...

ANSWER

Answered 2018-May-11 at 15:16

conv1d expects channel dimension to be defined during the creating of the graph. So you cant pass the dimension as None.

You need to make the following changes :

Source https://stackoverflow.com/questions/50291346

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

Vulnerabilities

No vulnerabilities reported

Install BLSTM-CRF-NER

You can download it from GitHub.
You can use BLSTM-CRF-NER 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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