pytorch_pretrained_BERT | repository contains an op-for-op PyTorch reimplementation | Machine Learning library

by Meelfy Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | pytorch_pretrained_BERT Summary

pytorch_pretrained_BERT is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Bert, Transformer applications. pytorch_pretrained_BERT has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

This repository contains an op-for-op PyTorch reimplementation of Google's TensorFlow repository for the BERT model that was released together with the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. This implementation is provided with Google's pre-trained models, examples, notebooks and a command-line interface to load any pre-trained TensorFlow checkpoint for BERT is also provided.

Support

Quality

Security

License

Reuse

Support

pytorch_pretrained_BERT has a low active ecosystem.

It has 186 star(s) with 62 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 1 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 pytorch_pretrained_BERT is current.

Quality

pytorch_pretrained_BERT has no bugs reported.

Security

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

License

pytorch_pretrained_BERT 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

pytorch_pretrained_BERT 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.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Write prediction results to file
Get the final prediction
Get the n_best_size of the logits
Compute softmax
Convert examples to features
Truncates a sequence pair
Check if the word spans in the document
Convert a sequence of tokens to ids
Reads the input file
Evaluate the prediction
Compute the raw scores for each prediction
Compute f1 score
Parse command line arguments
Make an ordered dictionary of scores
Create a histogram of no - answer probabilities
Apply noans
Given a list of features select the field
Find the best score for each prediction
Compute precision - recall curve
Convert TensorFlow checkpoint to PyTorch model
Reads SWAG examples from a file
Load a model from a pretrained model
Read squad example
Perform a single step
Create a vocabulary from a pretrained model
Compute the attention matrix

Get all kandi verified functions for this library.

pytorch_pretrained_BERT Key Features

No Key Features are available at this moment for pytorch_pretrained_BERT.

pytorch_pretrained_BERT Examples and Code Snippets

No Code Snippets are available at this moment for pytorch_pretrained_BERT.

Community Discussions

Trending Discussions on pytorch_pretrained_BERT

Need to Fine Tune a BERT Model to Predict Missing Words

Migrating from `pytorch-pretrained-bert` to `pytorch-transformers` issue regarding model() output

Error in importing Keras with tensorflow-gpu backend (can't find libcublas.so.10.0)

QUESTION

Need to Fine Tune a BERT Model to Predict Missing Words

Asked 2020-Mar-02 at 19:28

I'm aware that BERT has a capability in predicting a missing word within a sentence, which can be syntactically correct and semantically coherent. Below is a sample code:

...

ANSWER

Answered 2020-Mar-02 at 19:28

BERT is a masked Language Model, meaning it is trained on exactly this task. That is why it can do it. So in that sense, no fine tuning is needed.

However, if the text you will see at runtime is different than the text BERT was trained on, your performance may be much better if you fine tune on the type of text you expect to see.

Source https://stackoverflow.com/questions/60486655

QUESTION

Migrating from `pytorch-pretrained-bert` to `pytorch-transformers` issue regarding model() output

Asked 2020-Feb-25 at 08:22

I'm having trouble migrating my code from pytorch_pretrained_bert to pytorch_transformers. I'm attempting to run a cosine similarity exercise. I want to extract text embeddings values of the second-to-last of the 12 hidden embedding layer.

...

ANSWER

Answered 2020-Feb-25 at 08:22

First of all, the newest version is called transformers (not pytorch-transformers).

You need to tell the model that you wish to get all the hidden states

Source https://stackoverflow.com/questions/60345277

QUESTION

Error in importing Keras with tensorflow-gpu backend (can't find libcublas.so.10.0)

Asked 2019-May-23 at 09:50

I'm trying to run a library included in Keras, given that it's very power-consuming I'd like to use tensorflow-gpu as a backend. During import, I get this ImportError

...

ANSWER

Answered 2019-May-23 at 09:23

You can try to uninstall tensorflow with :

Source https://stackoverflow.com/questions/56271619

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

Vulnerabilities

No vulnerabilities reported

Install pytorch_pretrained_BERT

This repo was tested on Python 3.5+ and PyTorch 0.4.1/1.0.0.

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: