PyABSA | Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc; | Predictive Analytics library

by yangheng95 Jupyter Notebook Version: 2.4.1.post1 License: MIT

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

kandi X-RAY | PyABSA Summary

PyABSA is a Jupyter Notebook library typically used in Analytics, Predictive Analytics, Pytorch, Bert, Neural Network applications. PyABSA has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

package root (including all interfaces). checkpoint manager entry, inference model entry. training module, every trainer return a inference model.

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Quality

Security

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Support

PyABSA has a low active ecosystem.

It has 688 star(s) with 128 fork(s). There are 11 watchers for this library.

There were 3 major release(s) in the last 12 months.

There are 16 open issues and 213 have been closed. On average issues are closed in 14 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of PyABSA is 2.4.1.post1

Quality

PyABSA has 0 bugs and 0 code smells.

Security

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

PyABSA code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

PyABSA 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

PyABSA releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 11232 lines of code, 532 functions and 183 files.

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

Top functions reviewed by kandi - BETA

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

Convert examples to features
Prepare input for OPC
Ensures that the origin label map is correct
Check if labels are valid
Try to detect a dataset
Performs TC boost augmentation analysis
Performs APC augmentation
Convert an APC dataset to a
Get TADText classifier
List available checkpoint
Compute the forward query
Transpose x
Performs the forward computation
Generate training set for the given APC dataset
Performs batch inference
Perform a forward computation
Convert an APC set to a ate file
Build tokenizer
Return a Sentiment Classifier instance
Get a Text Classifier object
Refactor a chinese dataset
Get an aspect extractor
Prepare the dependency graph
Make an ABSA dataset
Forward computation
List available checkpoints
Runs the inference
Forward computation

Get all kandi verified functions for this library.

PyABSA Key Features

No Key Features are available at this moment for PyABSA.

PyABSA Examples and Code Snippets

No Code Snippets are available at this moment for PyABSA.

Community Discussions

Trending Discussions on Predictive Analytics

will TensorFlow utilize GPU for predictive Analysis?

Restructuring Pandas Dataframe for large number of columns

Display data from two json files in react native

QUESTION

will TensorFlow utilize GPU for predictive Analysis?

Asked 2020-Nov-21 at 21:35

GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?

note: predictive Analysis requires no graphics processing.

...

ANSWER

Answered 2020-Nov-21 at 21:35

The short answer: yes, it will! The slightly longer answer:

The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.

The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.

If you want to know more about this, I'd recommend you start here or here.

some things to consider:

how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.

Source https://stackoverflow.com/questions/64948197

QUESTION

Restructuring Pandas Dataframe for large number of columns

Asked 2020-Nov-01 at 19:39

I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.

The current structure is as below.

What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):

Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.

...

ANSWER

Answered 2020-Nov-01 at 19:39

You might want try pivoting your table:

Source https://stackoverflow.com/questions/64630691

QUESTION

Display data from two json files in react native

Asked 2020-May-17 at 23:55

I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files

...

ANSWER

Answered 2020-May-17 at 23:55

The new object to get params in React Navigation 5 is:

Source https://stackoverflow.com/questions/61859411

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

Vulnerabilities

No vulnerabilities reported

Install PyABSA

Please do not install the version without corresponding release note to avoid installing a test version.
To use PyABSA, install the latest version from pip or source code:.
Create a new python environment (Recommended) and install latest pyabsa
Find a suitable demo script (ATEPC , APC , Text Classification) to prepare your training script. (Welcome to share your demo script)
Format or Annotate your dataset referring to ABSADatasets or use public dataset in ABSADatasets
Init your config to specify Model, Dataset, hyper-parameters
Training your model and get checkpoints
Share your checkpoint and dataset

Support

Except for the following models, we provide a template model involving LCF vec, you can develop your model based on the LCF-APC model template or LCF-ATEPC model template.

Find more information at: