Hugging-Py-Face v0.1.1: Fix for Sentence Similarity Task and Improved Unit Tests

This release includes a bug fix for a critical issue that was affecting the sentence similarity task (on DataFrames).

Change Log

• Fixed a bug where the options parameter was not being used in the sentence similarity task (on DataFrames)
• Improved unit tests

Users are strongly encouraged to use this version as the previous release, especially if they are looking to implement the sentence similarity task.

Hugging-Py-Face v0.2.0: Supported Additional NLP Tasks, Added a Retry Mechanism and Logging

This release introduces a few non-breaking changes to the package.

Change Log

• Added support for the question answering, sentence similarity and zero shot classification tasks to be applied on DataFrames
• Added support for the table question answering task (DataFrames includedd)
• Added a retry mechanism to be applied in situations where the HTTP service is unavailable
• Added logging capabilities
• Fixed a few minor bugs
• Improved unit tests

Users are strongly encouraged to use this version as it includes additional functionality and improves the stability of the package through the retry mechanism.

Hugging-Py-Face v0.1.1: Fix for Including Config Files as Package Data

This release includes a bug fix for a critical issue that was affecting the stability of the package.

Change Log

• Fixed the bug where the config files were not included as part of the package data
• Fixed a few other minor bugs
• Added the license for the package

Users are strongly encouraged to use this version as the previous release crashes due to the absence of the config files.

Hugging-Py-Face v0.1.0: Initial Release

Overview

Hugging-Py-Face is a powerful Python package that provides seamless integration with the Hugging Face Inference API, allowing you to easily perform inference on your machine learning models hosted on the Hugging Face Model Hub.

One of the key benefits of using the Hugging Face Inference API is that it provides a scalable and efficient way to perform inference on your models, by allowing you to easily deploy and serve your models in the cloud. Additionally, the Inference API provides a simple and standardized API that can be used across different programming languages, making it easy to integrate your models with other services and tools.

With Hugging-Py-Face, you can take advantage of these benefits while also enjoying the simplicity and flexibility of using Python.

It allows you to easily customize your API requests, adjust request parameters, handle authentication and access tokens, and interact with a wide range of machine learning models hosted on the Hugging Face Model Hub.

Overall, Hugging-Py-Face is an awesome tool for any machine learning developer or data scientist who wants to perform efficient and scalable inference on their models, while also enjoying the simplicity and flexibility of using Python. Whether you're working on a personal project or a large-scale enterprise application, Hugging-Py-Face can help you achieve your machine learning goals with ease.

Installation

To install Hugging-Py-Face, simply run pip install hugging_py_face.

Change Log

This initial release has implemented the following,

• NLP (Natural Language Processing): This component deals with processing and analyzing human language. It includes various techniques such as text classification, text generation, summarization and many more.
• Computer Vision: This component deals with the analysis of visual data from the real world. It includes the image classification and object detection techniques.
• Audio Processing: This component deals with the analysis of audio signals. It includes the audio classification and speech recognition techniques.

Usage

To use Hugging-Py-Face, simply import the package and use the provided functions to load and apply pre-trained models.

The library will first need to be configured with a User Access Tokens from the Hugging Face website.

NLP (Natural Language Processing)

from hugging_py_face import NLP

# initialize the NLP class with the user access token
nlp = NLP('hf_...')

# perform text classification
nlp.text_classification("I like you. I love you.")

# perform object detection
nlp.text_generation("The answer to the universe is")

The inputs to these methods can also be a list of strings. For example:

nlp.text_classification(["I like you. I love you.", "I hate you. I despise you."])

Additionally, the fill mask, summarization, text classification and text generation tasks can also be performed on a pandas DataFrame. For example:

nlp.text_classification_in_df(df, 'text')
# where df is a pandas DataFrame and 'text' is the column name containing the text

Computer Vision

from hugging_py_face import ComputerVision

# initialize the ComputerVision class with the user access token
cp = ComputerVision('hf_...')

# perform image classification
# the image can be a local file or a URL
cp.image_classification("cats.jpg")

# perform object detection
# the image can be a local file or a URL
cp.object_detection("cats.jpg")

The inputs to these methods can also be a list of images. For example:

cp.image_classification(["cats.jpg", "dogs.jpg"])

Additionally, the image classification task can also be performed on a pandas DataFrame. For example:

cp.image_classification_in_df(df, 'images')
# where df is a pandas DataFrame and 'images' is the column name containing the image file paths or URLs

Audio Processing

from hugging_py_face import AudioProcessing

# initialize the AudioProcessing class with the user access token
ap = AudioProcessing('hf_...')

# perform audio classification
# the audio file can be a local file or a URL
ap.audio_classification("dogs.wav")

# perform speech recognition
# the audio file can be a local file or a URL
ap.speech_recognition("dogs.wav")

The inputs to these methods can also be a list of audio files. For example:

ap.audio_classification(["dogs.wav", "cats.wav"])

Additionally, both of the above tasks can also be performed on a pandas DataFrame. For example:

ap.audio_classification_in_df(df, 'audio')
# where df is a pandas DataFrame and 'audio' is the column name containing the audio file paths or URLs

Contributors Wanted

I am actively seeking contributors to help develop and improve Hugging-Py-Face. Whether you're a seasoned developer or just starting out, there are plenty of ways to get involved. If you're interested in contributing to this project, please reach out to me via email or by creating a new issue on the GitHub repository. I'd love to hear from you!

Contact Information

If you have any questions or issues with the package, please contact me at [email protected].

License

Hugging-Py-Face is released under the GPL-3.0 License.