deep-siamese-text-similarity | Tensorflow based implementation of deep siamese LSTM network | Machine Learning library

by dhwajraj Python Version: Current License: MIT

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kandi X-RAY | deep-siamese-text-similarity Summary

deep-siamese-text-similarity is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras, Neural Network applications. deep-siamese-text-similarity has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However deep-siamese-text-similarity build file is not available. You can download it from GitHub.

Tensorflow based implementation of deep siamese LSTM network to capture phrase/sentence similarity using character/word embeddings

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deep-siamese-text-similarity has a medium active ecosystem.

It has 1390 star(s) with 452 fork(s). There are 77 watchers for this library.

It had no major release in the last 6 months.

deep-siamese-text-similarity has no issues reported. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of deep-siamese-text-similarity is current.

Quality

deep-siamese-text-similarity has 0 bugs and 0 code smells.

Security

deep-siamese-text-similarity has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

deep-siamese-text-similarity code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

deep-siamese-text-similarity is licensed under the MIT License. This license is Permissive.

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

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deep-siamese-text-similarity releases are not available. You will need to build from source code and install.

deep-siamese-text-similarity 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.

deep-siamese-text-similarity saves you 244 person hours of effort in developing the same functionality from scratch.

It has 594 lines of code, 23 functions and 6 files.

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

Top functions reviewed by kandi - BETA

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

Builds data sets for training .
Initialize model .
Implementation of BiRNN .
Loads tsv data from file .
Constructs a stacked RNN cell .
Generate word ids .
Tokenizer .
Tokenize an iterator .

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deep-siamese-text-similarity Key Features

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deep-siamese-text-similarity Examples and Code Snippets

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Community Discussions

Trending Discussions on deep-siamese-text-similarity

TypeError: Fetch argument array has invalid type numpy.ndarray, must be a string or Tensor. (Can not convert a ndarray into a Tensor or Operation.)

QUESTION

TypeError: Fetch argument array has invalid type numpy.ndarray, must be a string or Tensor. (Can not convert a ndarray into a Tensor or Operation.)

Asked 2017-Dec-04 at 11:30

I am trying to reproduce the results in siaseme LSTM to compare the semantic similarity of two sentences from here :- https://github.com/dhwajraj/deep-siamese-text-similarity

I am using tensorflow 1.4 & python 2.7

The train.py is working properly. For evaluating the model, I created a match_valid.tsv file which is a subset of "train_snli.txt" available there. I have modified the getTsvTestData function present in the input_helpers.py file.

...

ANSWER

Answered 2017-Dec-04 at 11:30

The problem is that you are replacing the value of sim, which (I suppose) initially contains a reference to a TensorFlow tensor or operation, with the result of evaluating it (which is a NumPy array), so the second iteration fails because sim is not a TensorFlow tensor or operation anymore.

You can try something like this:

Source https://stackoverflow.com/questions/47632262

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Vulnerabilities

No vulnerabilities reported

Install deep-siamese-text-similarity

You can download it from GitHub.
You can use deep-siamese-text-similarity 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.

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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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