syntaxnet | Syntaxnet Parsey McParseface wrapper for POS | Natural Language Processing library

 by   spoddutur Python Version: Current License: No License

kandi X-RAY | syntaxnet Summary

kandi X-RAY | syntaxnet Summary

syntaxnet is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. syntaxnet has no bugs, it has no vulnerabilities and it has high support. However syntaxnet build file is not available. You can download it from GitHub.

When Google declared that The World’s Most Accurate Parser i.e., SyntaxNet goes open-source, it grabbed widespread attention from machine-learning developers and researchers who were interested in core applications of NLU like automatic extraction of information, translation etc. Following gif shows how syntaxnet internally builds the dependency tree:.

            kandi-support Support

              syntaxnet has a highly active ecosystem.
              It has 68 star(s) with 20 fork(s). There are 2 watchers for this library.
              It had no major release in the last 6 months.
              There are 7 open issues and 1 have been closed. There are 1 open pull requests and 0 closed requests.
              It has a negative sentiment in the developer community.
              The latest version of syntaxnet is current.

            kandi-Quality Quality

              syntaxnet has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              syntaxnet does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              syntaxnet releases are not available. You will need to build from source code and install.
              syntaxnet 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.

            Top functions reviewed by kandi - BETA

            kandi has reviewed syntaxnet and discovered the below as its top functions. This is intended to give you an instant insight into syntaxnet implemented functionality, and help decide if they suit your requirements.
            • Wrapper for _apply_op .
            • Inception V2d .
            • Update a single step .
            • Batch normalization .
            • Wrapper for inception v3 .
            • Imports a graph definition protobuf .
            • Setup the model .
            • Compile the model .
            • Concatenate input tensors with space space .
            • Train an optimizer .
            Get all kandi verified functions for this library.

            syntaxnet Key Features

            No Key Features are available at this moment for syntaxnet.

            syntaxnet Examples and Code Snippets

            No Code Snippets are available at this moment for syntaxnet.

            Community Discussions


            pip search finds tensorflow, but pip install does not
            Asked 2020-Jan-23 at 06:55

            I am trying to build a Django app that would use Keras models to make recommendations. Right now I'm trying to use one custom container that would hold both Django and Keras. Here's the Dockerfile I've written.



            Answered 2019-Jan-02 at 22:56

            It looks like tensorflow only publishes wheels (and only up to 3.6), and Alpine linux is not manylinux1-compatible due to its use of musl instead of glibc. Because of this, pip cannot find a suitable installation candidate and fails. Your best options are probably to build from source or change your base image.



            cd command in python
            Asked 2018-Aug-27 at 08:28

            I am new to python and

            I am trying to test syntaxnet in python using this github repo.

            and in the section "how to run the parser" is as following :

            1. git clone
            2. cd
            3. python
            4. That's it!! It prints syntaxnet dependency parser output for given input english sentence

            through some research, I understood that first one indicates that I need to install the syntaxnet package in my cmd so I did and the package was successfully installed. but I don't understand how to perform the second one what does cd do, and where and how should I use it?

            also in,



            Answered 2018-Aug-27 at 08:28

            The CD command means Change Directory.

            Once you have finished cloning Syntaxnet Github Repository, you should enter its directory. That's why you have to enter CD command.

            But have in mind that CD takes one parameter - the directory you want to enter.

            In order to solve your problem you must write cd syntaxnet resulting in:



            How to get Dependency Tree in JSON format in SyntaxNet?
            Asked 2018-Aug-09 at 10:01

            I am trying to get a dependency tree in JSON format from SyntaxNet but all I get from the examples is a Sentence Object which is providing no accessors to access the parsed object or even iterate through the items listed.

            When I run the examples from the docker file provided by TensorFlow/SyntaxNet, what I get is as below



            Answered 2018-Aug-09 at 10:01

            TL;DR Code at the end...

            The Sentence object is an instance of the sentence_pb2.Setnence class, which is generated from protobuf definition files, specifically sentence.proto. This means that if you look at sentence.proto, you will see the fields that are defined for that class and their types. So you have a field called "tag" which is a string, a field called "label" which is a string, a field called head which is an integer and so on. In theory if you just convert to json using python's built-in functions it should work, but since protobuf classes are runtime generated metaclasses, they may produce some undesired results.

            So what I did was first created a map object with all the info I wanted, then converted that to json:



            Converting Dependency tree into sequence of Arc-eager transitions
            Asked 2018-Jun-06 at 14:00

            Currently I'm trying to build syntax-aware NMT model.
            In this project, I need the sequence of one of three transition actions (SHIFT, REDUCE-L, REDUCE-R)

            Similar to what is in the image a

            This chunk represents the transition-based dependency for 2 sentences(1 for 1 chunk split by empty lines)

            I'm using Syntaxnet to get the dependency parse tree first, but it doesn't directly provide that transition action sequences.
            It's results are as follows,


            Is it possible to get the action sequences similar to this image? Is it possible to convert what is achieved from this image to the original image's format.



            Answered 2018-Jun-06 at 14:00

            A function that converts a dependency tree to a sequence of transitions is called an oracle. It is a necessary component of a statistical parser. The transitions you described (shift, reduce-l, reduce-r)¹ are those of the arc-standard transition system (not the arc-eager system, which is: shift, left-arc, right-arc, reduce).

            Pseudo-code for an arc-standard oracle:



            SyntaxNet to process a large number of sentences, do GPUs increase performance?
            Asked 2017-Apr-22 at 22:03

            I have a large dataset of sentences (i.e., ~5.000.000) in raw text which I want to process using SyntaxNet already trained for English. That is, I just want to process the sentences using a SyntaxNet model, I don't want to train any new model.

            Setting up a processing environment with GPUs will have any effect on performance ?

            I understand that most of the heavy CPU operations is on estimating the parameters and weights of the network/model, once these are estimated, applying the trained network should be faster than training.

            Nevertheless, I've never worked before with Tensorflow and I don't know whether GPUs are used when one applies an already trained model to data.

            Also, does anyone knows any easy way to setup SyntaxNet as a daemon or web-service, so that batch processing can be made easily?



            Answered 2017-Apr-22 at 22:03

            You still need to do a lot of tensor operations on the graph to predict something. So GPU still provides performance improvement for inference. Take a look at this nvidia paper, they have not tested their stuff on TF, but it is still relevant:

            Our results show that GPUs provide state-of-the-art inference performance and energy efficiency, making them the platform of choice for anyone wanting to deploy a trained neural network in the field. In particular, the Titan X delivers between 5.3 and 6.7 times higher performance than the 16-core Xeon E5 CPU while achieving 3.6 to 4.4 times higher energy efficiency.

            Regarding how to deploy your model, take a look at TF serving



            How long it take to train English/Russian/... model from scratch with SyntaxNet/DragNN?
            Asked 2017-Mar-29 at 14:19

            I want to retrain existing models for SyntaxNet/DragNN and looking for some real numbers how long does it take to train models for any language (it will give me good baseline for my languages). What hardware have you used during this process?

            Thank you in advance!



            Answered 2017-Mar-29 at 14:19

            it took about 24 hours on my mac pro with cpu. (10000 iterations)



            Exhausted Virtual Memory Installing SyntaxNet Using Docker Toolbox
            Asked 2017-Mar-02 at 12:05

            I exhausted my virtual memory when trying to install SyntaxNet from this Dockerfile using the Docker Toolbox. I received this message when compiling the Dockerfile:



            Answered 2017-Mar-02 at 12:05

            There are two possibilities: You could either modify the Dockerfile so that it creates a ~/.bazelrc that contains the following text:



            How to downgrade bazel to 0.4.3
            Asked 2017-Feb-14 at 22:19

            I installed bazel and upgrade it to 0.4.4 recently.

            I want to try tensorflow/models/syntaxnet but it requires bazel 0.4.3.

            So how can I downgrade bazel 0.4.4 to 0.4.3?



            Answered 2017-Feb-14 at 22:19

            0.4.4 should work fine, too. 0.4.3 is the minimum.

            If you really want, you can install 0.4.3 from the installer.



            How much data is required to train SyntaxNet?
            Asked 2017-Jan-29 at 18:44

            I know the more data, the better it's but what would be a reasonable amount of data required to train SyntaxNet?



            Answered 2017-Jan-29 at 18:44

            Based on some trial and error, I have arrived at the following minimums:

          • Train corpus - 18,000 tokens (anything less than that and step 2 - Preprocessing with the Tagger- fails)
          • Test corpus - 2,000 tokens (anything less than that and step 2 - Preprocessing with the Tagger - fails)
          • Dev corpus - 2,000 tokens

            But please note that with this, I've only managed to get the steps in the NLP pipeline to run, I actually haven't managed to get anything usable out of it.

          • Source


            How to interpret the output of a synaxnet when annotating a corpus
            Asked 2017-Jan-26 at 15:27

            I annotated a corpus using pre-trained syntaxnet model (i.e. using Parse McParseface). I am having a problem understanding the output. There are two metrics reproted in the output. Are those for POS tagging and dependency parsing? If yes, which one is POS tagging performance and which one is for dependency parsing performance?

            Here is the output:

            INFO:tensorflow:Total processed documents: 21710 INFO:tensorflow:num correct tokens: 454150 INFO:tensorflow:total tokens: 560993 INFO:tensorflow:Seconds elapsed in evaluation: 1184.63, eval metric: 80.95% INFO:tensorflow:Processed 206 documents INFO:tensorflow:Total processed documents: 21710 INFO:tensorflow:num correct tokens: 291851 INFO:tensorflow:total tokens: 504496 INFO:tensorflow:Seconds elapsed in evaluation: 1193.17, eval metric: 57.85%



            Answered 2017-Jan-26 at 15:27

            If you're using then the first metric is POS tag accuracy, the second UAS. They are only meaningful if the conll data you input contains gold POS tags and gold dependencies.


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


            No vulnerabilities reported

            Install syntaxnet

            You can download it from GitHub.
            You can use syntaxnet 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.


            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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