transformer-lm | Transformer language model with sentencepiece | Natural Language Processing library

by lopuhin Python Version: Current License: No License

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

kandi X-RAY | transformer-lm Summary

transformer-lm is a Python library typically used in Artificial Intelligence, Natural Language Processing, Tensorflow, Bert, Neural Network, Transformer applications. transformer-lm has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Transformer language model (GPT-2) with sentencepiece tokenizer

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Quality

Security

License

Reuse

Support

transformer-lm has a low active ecosystem.

It has 155 star(s) with 43 fork(s). There are 9 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 15 have been closed. On average issues are closed in 46 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of transformer-lm is current.

Quality

transformer-lm has 0 bugs and 0 code smells.

Security

transformer-lm has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

transformer-lm code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

transformer-lm 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.

Reuse

transformer-lm 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.

It has 1564 lines of code, 124 functions and 19 files.

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

Top functions reviewed by kandi - BETA

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

Train a model .
Sample a sequence of tokens .
Build a sentencepiece model .
Command line parser .
Calculates the log probability for each token .
Attention function .
View function .
Compute the model .
Forward forward computation .
Decorate a function to return a wrapped function .

Get all kandi verified functions for this library.

transformer-lm Key Features

No Key Features are available at this moment for transformer-lm.

transformer-lm Examples and Code Snippets

No Code Snippets are available at this moment for transformer-lm.

Community Discussions

Trending Discussions on transformer-lm

Deploy PyTorch model to GCP resulting in memory limit exceeded

QUESTION

Deploy PyTorch model to GCP resulting in memory limit exceeded

Asked 2019-Oct-15 at 12:30

I have trained a language model using transformer-lm which uses PyTorch. I would like to deploy the resulting model to the Google Cloud Platform as a Cloud Function. Cloud Functions are limited to 2 GB of memory.

The problem is that loading the model leads to an error as too much memory is used (memory limit exceeded). The model.pt file is 1.32 GB, and I use

...

ANSWER

Answered 2019-Oct-15 at 12:30

Cloud Functions run their own server instances when triggered. If a called function is not used after some time, that instance is terminated. However, if you call it again while the instance is still running, the same instance along with the elements in your execution environment will be used again. This can cause your function crash.

In order to avoid that, you might want to implement a Garbage Collector. In Python, you can use the gc module for that. In particular, you can try the function gc.collect() to clear the memory.

Source https://stackoverflow.com/questions/58351544

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

Vulnerabilities

No vulnerabilities reported

Install transformer-lm

You can download it from GitHub.
You can use transformer-lm 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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