bert_serving | export bert model | Natural Language Processing library
kandi X-RAY | bert_serving Summary
kandi X-RAY | bert_serving Summary
bert_serving is a Python library typically used in Artificial Intelligence, Natural Language Processing, Tensorflow, Bert applications. bert_serving 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.
export bert model for serving
export bert model for serving
Support
Quality
Security
License
Reuse
Support
bert_serving has a low active ecosystem.
It has 139 star(s) with 39 fork(s). There are 3 watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 12 have been closed. On average issues are closed in 310 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of bert_serving is current.
Quality
bert_serving has 0 bugs and 0 code smells.
Security
bert_serving has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
bert_serving code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
bert_serving 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
bert_serving 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 are not available. Examples and code snippets are available.
bert_serving saves you 1650 person hours of effort in developing the same functionality from scratch.
It has 3662 lines of code, 186 functions and 13 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed bert_serving and discovered the below as its top functions. This is intended to give you an instant insight into bert_serving implemented functionality, and help decide if they suit your requirements.
- Write predictions to file
- Return the final prediction
- Compute softmax probability
- Get the list of n_best_size_size
- Convert examples to features
- Convert a single example
- Return a string representation of text
- Truncate a sequence pair
- Transformer transformer model
- Build a function that builds TPUEstimator
- Embedding postprocessor
- Read a squad example file
- Write examples to examples
- Builds an input function
- Tokenize text
- Embed word embedding
- Builds a tf input_fn
- Creates training instances
- Creates the attention mask from from_tensor
- Process a single feature
- Validate flags
- Reads input_file
- Reads train examples
- Return list of input examples
- Load vocabulary from file
- Returns a list of dev examples
Get all kandi verified functions for this library.
bert_serving Key Features
No Key Features are available at this moment for bert_serving.
bert_serving Examples and Code Snippets
No Code Snippets are available at this moment for bert_serving.
Community Discussions
Trending Discussions on bert_serving
QUESTION
Calculate a measure between keywords and each word of a textfile
Asked 2020-Jun-22 at 08:21
I have two .txt files, one that contains 200.000 words and the second contains 100 keywords( one each line). I want to calculate the cosine similarity between each of the 100 keywords and each word of my 200.000 words , and display for every keyword the 50 words with the highest score.
Here's what I did, note that Bertclient is what i'm using to extract vectors :
...ANSWER
Answered 2020-Jun-20 at 18:15I know nothing of Bert...but there's something fishy with the import and run. I don't think you have it installed correctly or something. I tried to pip install it and just run this:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install bert_serving
You can download it from GitHub.
You can use bert_serving 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.
You can use bert_serving 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 .
Find more information at:
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
