lm_finetuning | Language Model Fine-tuning for Moby Dick | Natural Language Processing library

by Kyubyong Python Version: Current License: No License

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kandi X-RAY | lm_finetuning Summary

lm_finetuning is a Python library typically used in Manufacturing, Utilities, Machinery, Process, Artificial Intelligence, Natural Language Processing, Bert applications. lm_finetuning has no bugs, it has no vulnerabilities and it has low support. However lm_finetuning build file is not available. You can download it from GitHub.

Language Model Fine-tuning for Moby Dick

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Quality

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Support

lm_finetuning has a low active ecosystem.

It has 41 star(s) with 5 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

lm_finetuning has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of lm_finetuning is current.

Quality

lm_finetuning has 0 bugs and 0 code smells.

Security

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

lm_finetuning code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

lm_finetuning 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

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

lm_finetuning 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.

lm_finetuning saves you 46 person hours of effort in developing the same functionality from scratch.

It has 123 lines of code, 4 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Refine text
Tokenize a sentence
Generate text
Download text from url

Get all kandi verified functions for this library.

lm_finetuning Key Features

No Key Features are available at this moment for lm_finetuning.

lm_finetuning Examples and Code Snippets

No Code Snippets are available at this moment for lm_finetuning.

Community Discussions

Trending Discussions on lm_finetuning

How to use fine-tuned BERT model for sentence encoding?

QUESTION

How to use fine-tuned BERT model for sentence encoding?

Asked 2021-Mar-19 at 12:53

I fine-tuned the BERT base model on my own dataset following the script here:

https://github.com/cedrickchee/pytorch-pretrained-BERT/tree/master/examples/lm_finetuning

I saved the model as a .pt file and I want to use it now for a sentence similarity task. Unfortunately, it is not clear to me, how to load the fine-tuned model. I tried the following:

...

ANSWER

Answered 2021-Mar-19 at 12:53

To load a model with BertModel.from_pretrained() you need to have saved it using save_pretrained() (link).

Any other storage method would require the corresponding load. I am not familiar with S3, but I assume you can use get_object (link) to retrieve the model, and then save it using the huggingface api. From then on, you should be able to use from_pretrained() normally.

Source https://stackoverflow.com/questions/66707770

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

Vulnerabilities

No vulnerabilities reported

Install lm_finetuning

You can download it from GitHub.
You can use lm_finetuning 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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