bert-for-tf2 | Keras TensorFlow 2.0 implementation | Machine Learning library

by kpe Python Version: 0.14.9 License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | bert-for-tf2 Summary

bert-for-tf2 is a Python library typically used in Artificial Intelligence, Machine Learning, Tensorflow, Keras, Bert, Neural Network, Transformer applications. bert-for-tf2 has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However bert-for-tf2 has 1 bugs. You can install using 'pip install bert-for-tf2' or download it from GitHub, PyPI.

A Keras TensorFlow 2.0 implementation of BERT, ALBERT and adapter-BERT.

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Quality

Security

License

Reuse

Support

bert-for-tf2 has a low active ecosystem.

It has 711 star(s) with 158 fork(s). There are 35 watchers for this library.

It had no major release in the last 12 months.

There are 20 open issues and 63 have been closed. On average issues are closed in 18 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of bert-for-tf2 is 0.14.9

Quality

bert-for-tf2 has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 52 code smells.

Security

bert-for-tf2 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

bert-for-tf2 code analysis shows 0 unresolved vulnerabilities.

There are 7 security hotspots that need review.

License

bert-for-tf2 is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

bert-for-tf2 releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

bert-for-tf2 saves you 1463 person hours of effort in developing the same functionality from scratch.

It has 3265 lines of code, 210 functions and 33 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed bert-for-tf2 and discovered the below as its top functions. This is intended to give you an instant insight into bert-for-tf2 implemented functionality, and help decide if they suit your requirements.

Load weights for the given Bert model
Map name to tfhub albert variable name
Check if tfhub model is a tfhub model
Checks if the given ckpt_path exists
Call the layer
Create attention mask
Tokenize text
Convert input to unicode
Split text into tokens
Connects the model
Get activation function
Connects the transformer
Load vocabulary
Call the model
Call the attention layer
Convert ids to tokens
Convert a list of items into a list
Get the version number
Encode the ids in text
Encode a piece of pieces
Ensure text is printable

Get all kandi verified functions for this library.

bert-for-tf2 Key Features

No Key Features are available at this moment for bert-for-tf2.

bert-for-tf2 Examples and Code Snippets

No Code Snippets are available at this moment for bert-for-tf2.

Community Discussions

Trending Discussions on bert-for-tf2

Converting Albert to tflite (Albert implemented in Keras via bert-for-tf2)

Albert_base : weights from ckpt not loaded properly when calling with bert-for-tf2

QUESTION

Converting Albert to tflite (Albert implemented in Keras via bert-for-tf2)

Asked 2020-Jan-08 at 06:01

I am having a hard time converting albert (more specifically, albert_base model) to tflite. Here is my code defining my model using bert-for-tf2 (https://github.com/kpe/bert-for-tf2) <- thanks for this great implementation by the way...

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ANSWER

Answered 2019-Dec-11 at 07:47

Funny that I have been struggling with the problem for hours but only right after I uploaded question I solved the problem...

So the solution is, use tensorflow version 1.15.0! Using tensorflow2 seems to cause the issue.

However, I still cannot convert the model to tflite since it does not support 'IdentityN' ops yet. I don't think I can write a custom op myself, so I think I should just wait for tflite update....

Source https://stackoverflow.com/questions/59279738

QUESTION

Albert_base : weights from ckpt not loaded properly when calling with bert-for-tf2

Asked 2019-Dec-19 at 09:37

I wanted to fine-tune Albert_base with further mlm task, but I realized there is no pretrained ckpt file provided for albert-base. So my plan was to convert the saved_model(or model loaded from tf-hub) to checkpoint myself, and then pretrain albert-base using the code provided (https://github.com/google-research/ALBERT/blob/master/run_pretraining.py).

Before further pretraining, to check whether the conversion to ckpt was successful, I re-converted the ckpt file to saved_model format, and loaded it as keras layer using bert-for-tf2 (https://github.com/kpe/bert-for-tf2/tree/master/bert) However, when I loaded the re-converted albert_base, its embeddings were different from those from the one loaded from the original albert_base.

Here is how I converted the original saved_model to ckpt, and then back to saved_model. (I used tf vertsion = 1.15.0 on colab)

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ANSWER

Answered 2019-Dec-18 at 06:15

Problem Solved! So the problem WAS actually the difference in tensor names. So I changed the names of tensors in the checkpoints using the following code (https://gist.github.com/batzner/7c24802dd9c5e15870b4b56e22135c96).

Just need to change 'module/bert/....' to 'bert/....' and it's all good.

Source https://stackoverflow.com/questions/59371228

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

Vulnerabilities

No vulnerabilities reported

Install bert-for-tf2

You can install using 'pip install bert-for-tf2' or download it from GitHub, PyPI.
You can use bert-for-tf2 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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