sequence_tagging | Named Entity Recognition - Tensorflow | Natural Language Processing library

by guillaumegenthial Python Version: Current License: Apache-2.0

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

kandi X-RAY | sequence_tagging Summary

sequence_tagging is a Python library typically used in Artificial Intelligence, Natural Language Processing, Deep Learning, Tensorflow applications. sequence_tagging 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.

Named Entity Recognition (LSTM + CRF) - Tensorflow

Support

Quality

Security

License

Reuse

Support

sequence_tagging has a medium active ecosystem.

It has 1928 star(s) with 705 fork(s). There are 75 watchers for this library.

It had no major release in the last 6 months.

There are 18 open issues and 65 have been closed. On average issues are closed in 81 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of sequence_tagging is current.

Quality

sequence_tagging has 0 bugs and 9 code smells.

Security

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

sequence_tagging code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

sequence_tagging 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

sequence_tagging 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.

sequence_tagging saves you 290 person hours of effort in developing the same functionality from scratch.

It has 700 lines of code, 51 functions and 9 files.

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

Top functions reviewed by kandi - BETA

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

Build the neural network
Add a training op
Adds logits op
Add softmax loss op
Runs the model
Predict
Calculate the alignment of the data
Predict a batch of sentences
Load the vocabulary
Returns a function for processing words
Get the trimmed embeddings from a file
Load a vocabulary file
Train the model
Save current session
Add a summary to the graph
Run a single epoch
Return a function for processing a word
Evaluate the model over a given test set
Get the vocabulary of the given dataset
Builds a vocabulary from a file
Build the vocabulary from datasets
Load a vocabulary
Exports embedding vectors
Write a vocabulary
Restore the latest trained session

Get all kandi verified functions for this library.

sequence_tagging Key Features

No Key Features are available at this moment for sequence_tagging.

sequence_tagging Examples and Code Snippets

bert-of-theseus-tf,evals on Chinese NER based on bert_mrc

Python

Lines of Code : 6

License : Permissive (Apache-2.0)

Copy

|         method           | f1-micro-avg |
| :---------------------:  | :----------: |
| two-stage,const prob=0.5 |    0.9459    |
| two-stage,linear stratege|    0.9491    |
| one-stage                |    0.9342    |
| orig bert+mrc+focalloss  |

Community Discussions

Trending Discussions on sequence_tagging

Why doesn't Tensorflow automatically handle hidden states of recurrent cells?

QUESTION

Why doesn't Tensorflow automatically handle hidden states of recurrent cells?

Asked 2018-Feb-26 at 19:27

I am going through couple of Tensorflow examples that use LSTM cells and trying to understand the purpose of initial_state variable that is used in one implementation but not in the other for some unknown reason.

For example PTB example uses it as:

...

ANSWER

Answered 2018-Feb-26 at 19:27

As I understood, it it appears to be specific setup for Tensorflow PTB model which is supposed to be running not only with single LSTM cells but with several ones (who would even try to train it on more than 2 cells I wonder). For that it needs to keep track of c and h tensors between the cells and thus the _initial_state variable. It also is supposed to be running in parallel over several GPUs as well, continue if interrupted etc. And that is why PTB example code looks ugly and overengineered to a newcomer.

Source https://stackoverflow.com/questions/48911479

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

Vulnerabilities

No vulnerabilities reported

Install sequence_tagging

Alternatively, you can download them manually here and update the glove_filename entry in config.py. You can also choose not to load pretrained word vectors by changing the entry use_pretrained to False in model/config.py.
Download the GloVe vectors with
Build the training data, train and evaluate the model with

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: