ru_transformers | Google colab notebook

by mgrankin Python Version: Current License: Apache-2.0

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

kandi X-RAY | ru_transformers Summary

ru_transformers is a Python library typically used in Pytorch applications. ru_transformers has no vulnerabilities, it has a Permissive License and it has medium support. However ru_transformers has 2 bugs and it build file is not available. You can download it from GitHub.

Google colab notebook for finetuning. Google colab notebook for generating text corpus.

Support

Quality

Security

License

Reuse

Support

ru_transformers has a medium active ecosystem.

It has 773 star(s) with 114 fork(s). There are 30 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 44 have been closed. On average issues are closed in 57 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ru_transformers is current.

Quality

ru_transformers has 2 bugs (1 blocker, 0 critical, 1 major, 0 minor) and 34 code smells.

Security

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

ru_transformers code analysis shows 0 unresolved vulnerabilities.

There are 9 security hotspots that need review.

License

ru_transformers 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

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

ru_transformers has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

ru_transformers saves you 1077 person hours of effort in developing the same functionality from scratch.

It has 2440 lines of code, 131 functions and 16 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Evaluate a model
Generate a sequence of tensors
Filtering logits with a given threshold
Rotate checkpoints
Print a sample
Mask input tokens
Save training state
Save the weights of a pretrained model
Build a data loader
Set random seed
Evaluate the model
Sample a tensorflow model
Parse common options
Process a file
Sample a given prompt
Perform a continuous run
Get a random sample sequence
Sample from a given prompt

Get all kandi verified functions for this library.

ru_transformers Key Features

No Key Features are available at this moment for ru_transformers.

ru_transformers Examples and Code Snippets

No Code Snippets are available at this moment for ru_transformers.

Community Discussions

No Community Discussions are available at this moment for ru_transformers.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install ru_transformers

For finetuning first second Dostoyevskiy Tolstoy Pushkin Bulgakov Gogol Pelevin.
Follow instructions here https://github.com/google/sentencepiece.
Mixed precision training with opt_level O2 gives the exact same loss but much faster and with less memory. The downside - APEX with O2 doesnt work with DataParallel yet, see https://github.com/NVIDIA/apex/issues/227.

Support

Mixed precision training with opt_level O2 gives the exact same loss but much faster and with less memory. The downside - APEX with O2 doesnt work with DataParallel yet, see https://github.com/NVIDIA/apex/issues/227.

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