bert-syntax | Assessing syntactic abilities of BERT | Machine Learning library

by yoavg Python Version: Current License: Apache-2.0

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

kandi X-RAY | bert-syntax Summary

bert-syntax is a Python library typically used in Artificial Intelligence, Machine Learning, Bert applications. bert-syntax has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However bert-syntax build file is not available. You can download it from GitHub.

Assessing syntactic abilities of BERT

Support

Quality

Security

License

Reuse

Support

bert-syntax has a low active ecosystem.

It has 145 star(s) with 18 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 2 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of bert-syntax is current.

Quality

bert-syntax has 0 bugs and 0 code smells.

Security

bert-syntax has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

bert-syntax code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

bert-syntax 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

bert-syntax releases are not available. You will need to build from source code and install.

bert-syntax 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.

bert-syntax saves you 955 person hours of effort in developing the same functionality from scratch.

It has 2176 lines of code, 82 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 bert-syntax and discovered the below as its top functions. This is intended to give you an instant insight into bert-syntax implemented functionality, and help decide if they suit your requirements.

Inflect text
Join words together
Generate inflect from a vocab file
Return the noun of a word
Return the plural of text
Get count
Make a list of lists from a list of lists
Return a set of words
Join stems
Parse a group of Tensors
Evaluate marvin
Compare two words
Define plural verb
Return a set of words by size
Compares two words
Evaluate the gulordava
Join stems into a single word
Evaluate Lgd dataset
Define a noun
Deprecated
Converts a group into a string
Define A rule
Define a regular expression
Parse an amo object
Return a noun
Return True if the match object is participle

Get all kandi verified functions for this library.

bert-syntax Key Features

No Key Features are available at this moment for bert-syntax.

bert-syntax Examples and Code Snippets

No Code Snippets are available at this moment for bert-syntax.

Community Discussions

Trending Discussions on bert-syntax

Access spaCy Masked Language Model

QUESTION

Access spaCy Masked Language Model

Asked 2019-Jun-01 at 10:45

As of v2.1, spaCy has a BERT-style language model (LM). It predicts word-vectors instead of words, so I am going to use "words" and "word vectors" interchangeably here.

I need to take a sentence with a word masked, and a list of words, and rank the words by how likely they are to appear in the masked slot. Currently I am using BERT for this (similar to bert-syntax). I would like to see if spaCy's performance on this task is acceptable. Between this file and this one I'm pretty sure it's possible to build something. However, it feels like reaching deeper into the internals of the library than I'd like.

Is there a straightforward way to interact with spaCy's masked language model?

...

ANSWER

Answered 2019-Jun-01 at 10:45

This is basically the disadvantage of the LMAO approximation. I actually hadn't realised this until it was pointed out to me by someone on the /r/machinelearning subreddit.

Because we're predicting a vector, we really only get to predict one point in the vector-space. This is really different from predicting a distribution over the words. Imagine we had a gap like The __ of corn. Let's say a good distribution of fillers for that would be {kernel, ear, piece}. The vectors for these words aren't especially close, as the word2vec algorithm is constructing a vector space based on all contexts of the words, and the words are only interchangeable in this context. In the vast majority of uses of piece, the word ear would be a really bad substitution.

If the likely fillers aren't close together in the vector-space, there will be no way for the LMAO model to return you an answer that corresponds to that set of words.

If you only need the 1-best answer, the algorithm in spacy pretrain has a good chance of giving it to you. But if you need the distribution, the approximation breaks down, and you should use something like BERT.

Source https://stackoverflow.com/questions/56298584

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

Vulnerabilities

No vulnerabilities reported

Install bert-syntax

You can download it from GitHub.
You can use bert-syntax 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 .

Find more information at: