Rhythm-Finder is a unsupervised ML driven python powered web-application that can find the songs that suits you.

Overview

Logo

Project Name

An awesome README template to jumpstart your projects!
Explore the docs »

View Demo · Report Bug · Request Feature

Table of Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. Roadmap
  5. Contributing
  6. License
  7. Contact

About The Project

![Product Name Screen Shot][product-screenshot]

Rhythm-Finder is a unsupervised ML driven python powered web-application that can find the songs that suits you. It takes your basic data over music taste and gives you some song which you might like .

Built With

This section should list any major frameworks that you built your project using. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.

Getting Started

This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

Installation

  1. Clone the repo
    git clone https://github.com/gdscjgec/Rhythm-Finder.git
  2. Install dependencies
    cd Rhythm-Finder
    pip install requirements.txt
    
  3. Open terminal and go to the folder using cd command example : cd
  4. Run the command
    streamlit run app.py
    

Usage

Use this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.

For more examples, please refer to the Documentation

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch ( git checkout -b feature/AmazingFeature )
  3. Add your Changes ( git add . )
  4. Commit your Changes ( git commit -m 'Add some AmazingFeature' )
  5. Push to the Branch ( git push origin feature/AmazingFeature )
  6. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Mail us at - [email protected]

Owner
Google Developer Student Clubs - JGEC
Official GitHub organization for the Google Developer Student Clubs - JGEC
Google Developer Student Clubs - JGEC
ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab

AliceMind AliceMind: ALIbaba's Collection of Encoder-decoders from MinD (Machine IntelligeNce of Damo) Lab This repository provides pre-trained encode

Alibaba 1.4k Jan 04, 2023
PyABSA - Open & Efficient for Framework for Aspect-based Sentiment Analysis

PyABSA - Open & Efficient for Framework for Aspect-based Sentiment Analysis

YangHeng 567 Jan 07, 2023
Natural language Understanding Toolkit

Natural language Understanding Toolkit TOC Requirements Installation Documentation CLSCL NER References Requirements To install nut you need: Python 2

Peter Prettenhofer 119 Oct 08, 2022
Black for Python docstrings and reStructuredText (rst).

Style-Doc Style-Doc is Black for Python docstrings and reStructuredText (rst). It can be used to format docstrings (Google docstring format) in Python

Telekom Open Source Software 13 Oct 24, 2022
Machine learning classifiers to predict American Sign Language .

ASL-Classifiers American Sign Language (ASL) is a natural language that serves as the predominant sign language of Deaf communities in the United Stat

Tarek idrees 0 Feb 08, 2022
Basic yet complete Machine Learning pipeline for NLP tasks

Basic yet complete Machine Learning pipeline for NLP tasks This repository accompanies the article on building basic yet complete ML pipelines for sol

Ivan 20 Aug 22, 2022
A Persian Image Captioning model based on Vision Encoder Decoder Models of the transformers🤗.

Persian-Image-Captioning We fine-tuning the Vision Encoder Decoder Model for the task of image captioning on the coco-flickr-farsi dataset. The implem

Hamtech-ai 15 Aug 25, 2022
A full spaCy pipeline and models for scientific/biomedical documents.

This repository contains custom pipes and models related to using spaCy for scientific documents. In particular, there is a custom tokenizer that adds

AI2 1.3k Jan 03, 2023
An Explainable Leaderboard for NLP

ExplainaBoard: An Explainable Leaderboard for NLP Introduction | Website | Download | Backend | Paper | Video | Bib Introduction ExplainaBoard is an i

NeuLab 319 Dec 20, 2022
Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"

T5: Text-To-Text Transfer Transformer The t5 library serves primarily as code for reproducing the experiments in Exploring the Limits of Transfer Lear

Google Research 4.6k Jan 01, 2023
Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx

Anchored CorEx: Hierarchical Topic Modeling with Minimal Domain Knowledge Correlation Explanation (CorEx) is a topic model that yields rich topics tha

Greg Ver Steeg 592 Dec 18, 2022
IMDB film review sentiment classification based on BERT's supervised learning model.

IMDB film review sentiment classification based on BERT's supervised learning model. On the other hand, the model can be extended to other natural language multi-classification tasks.

Paris 1 Apr 17, 2022
A fast and easy implementation of Transformer with PyTorch.

FasySeq FasySeq is a shorthand as a Fast and easy sequential modeling toolkit. It aims to provide a seq2seq model to researchers and developers, which

宁羽 7 Jul 18, 2022
Text preprocessing, representation and visualization from zero to hero.

Text preprocessing, representation and visualization from zero to hero. From zero to hero • Installation • Getting Started • Examples • API • FAQ • Co

Jonathan Besomi 2.7k Jan 08, 2023
LUKE -- Language Understanding with Knowledge-based Embeddings

LUKE (Language Understanding with Knowledge-based Embeddings) is a new pre-trained contextualized representation of words and entities based on transf

Studio Ousia 587 Dec 30, 2022
The first online catalogue for Arabic NLP datasets.

Masader The first online catalogue for Arabic NLP datasets. This catalogue contains 200 datasets with more than 25 metadata annotations for each datas

ARBML 94 Dec 26, 2022
Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written form.

Neural G2P to portuguese language Grapheme-to-phoneme (G2P) conversion is the process of generating pronunciation for words based on their written for

fluz 11 Nov 16, 2022
Amazon Multilingual Counterfactual Dataset (AMCD)

Amazon Multilingual Counterfactual Dataset (AMCD)

35 Sep 20, 2022
BMInf (Big Model Inference) is a low-resource inference package for large-scale pretrained language models (PLMs).

BMInf (Big Model Inference) is a low-resource inference package for large-scale pretrained language models (PLMs).

OpenBMB 377 Jan 02, 2023
A python package to fine-tune transformer-based models for named entity recognition (NER).

nerblackbox A python package to fine-tune transformer-based language models for named entity recognition (NER). Resources Source Code: https://github.

Felix Stollenwerk 13 Jul 30, 2022