Deep Text Search is an AI-powered multilingual text search and recommendation engine with state-of-the-art transformer-based multilingual text embedding (50+ languages).

Last update: Sep 29, 2022

Overview

Deep Text Search - AI Based Text Search & Recommendation System

Deep Text Search is an AI-powered multilingual text search and recommendation engine with state-of-the-art transformer-based multilingual text embedding (50+ languages).

Creators

Nilesh Verma

Features

Faster Search.
High Accurate Text Recommendation and Search Output Result.
Best for Implementing on python based web application or APIs.
Best implementation for College students and freshers for project creation.
Applications are Text-based News, Social media post, E-commerce Product recommendation and other text-based platforms that want to implement text recommendation and search.

Installation

This library is compatible with both windows and Linux system you can just use PIP command to install this library on your system:

pip install DeepTextSearch

How To Use?

We have provided the Demo folder under the GitHub repository, you can find the example in both .py and .ipynb file. Following are the ideal flow of the code:

1. Importing the Important Classes

There are three important classes you need to load LoadData - for data loading, TextEmbedder - for embedding the text to data, TextSearch - For searching the text.

# Importing the proper classes
from DeepTextSearch import LoadData,TextEmbedder,TextSearch

2. Loading the Texts Data

For loading the Texts data we need to use the LoadData object, from there we can import text data as python list object from the CSV/Text file.

# Load data from CSV file
data = LoadData().from_csv("../your_file_name.csv")
# Load data from Text file
data = LoadData().from_text("../your_file_name.txt")

3. Embedding and Saving The File in Local Folder

For Embedding we are using state of the art multilingual Sentence Transformer Embedding, We also store the information of the Embedding for further use on the local path [embedding-data/] folder.

You can also use the load embedding() method in a TextEmbedder() class to load saved embedding data.

# To use Serching, we must first embed data. After that, we must save all of the data on the local path.
TextEmbedder().embed(corpus_list=data)

# Loading Embedding data
corpus_embedding = TextEmbedder().load_embedding()

3. Searching

We compare Cosian Similarity for searching and recommending, and then the corpus is sorted according to the similarity score:

# You must include the query text and the quantity of comparable texts you want to search for.
TextSearch().find_similar(query_text="What are the key features of Node.js?",top_n=10)

Complete Code

# Importing the proper classes
from DeepTextSearch import LoadData,TextEmbedder,TextSearch
# Load data from CSV file
data = LoadData().from_csv("../your_file_name.csv")
# To use Serching, we must first embed data. After that, we must save all of the data on the local path
TextEmbedder().embed(corpus_list=data)
# You must include the query text and the quantity of comparable texts you want to search for
TextSearch().find_similar(query_text="What are the key features of Node.js?",top_n=10)

License

MIT License

Copyright (c) 2021 Nilesh Verma

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Please do STAR the repository, if it helped you in anyway.

More cool features will be added in future. Feel free to give suggestions, report bugs and contribute.

On-device speech-to-index engine powered by deep learning.

30 Nov 24, 2022

This repository contains the code, data, and models of the paper titled "XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages" published in Findings of the Association for Computational Linguistics: ACL 2021.

XL-Sum This repository contains the code, data, and models of the paper titled "XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Lang

190 Jan 3, 2023

State of the Art Neural Networks for Deep Learning

pyradox This python library helps you with implementing various state of the art neural networks in a totally customizable fashion using Tensorflow 2

60 May 29, 2022

😇A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc

------ Update September 2018 ------ It's been a year since TorchMoji and DeepMoji were released. We're trying to understand how it's being used such t

865 Dec 24, 2022

Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning

Here is deepparse. Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning. Use deepparse to Use the pr

192 Dec 20, 2022

LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models

LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models. Developers can reproduce these SOTA methods and build their own methods.

405 Jan 4, 2023

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering Papers with code | Paper Nikola Zubić Pietro Lio University

213 Dec 27, 2022

Recommendationsystem - Movie-recommendation - matrixfactorization colloborative filtering recommendation system user

recommendationsystem matrixfactorization colloborative filtering recommendation

1 Jan 1, 2022

Summary Explorer is a tool to visually explore the state-of-the-art in text summarization.

Summary Explorer Summary Explorer is a tool to visually inspect the summaries from several state-of-the-art neural summarization models across multipl

42 Aug 14, 2022

Deep Text Search is an AI-powered multilingual text search and recommendation engine with state-of-the-art transformer-based multilingual text embedding (50+ languages).

Related tags

Overview

Deep Text Search - AI Based Text Search & Recommendation System

Creators

Nilesh Verma

Features

Installation

How To Use?

1. Importing the Important Classes

2. Loading the Texts Data

3. Embedding and Saving The File in Local Folder

3. Searching

Complete Code

License

Please do STAR the repository, if it helped you in anyway.

You might also like...

On-device speech-to-index engine powered by deep learning.

This repository contains the code, data, and models of the paper titled "XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages" published in Findings of the Association for Computational Linguistics: ACL 2021.

State of the Art Neural Networks for Deep Learning

😇A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc

Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning

LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

Recommendationsystem - Movie-recommendation - matrixfactorization colloborative filtering recommendation system user

Summary Explorer is a tool to visually explore the state-of-the-art in text summarization.

Releases(v_03)

v_03(May 31, 2021)

v_02(May 30, 2021)

v_01(May 30, 2021)

Owner

Nest Protect integration for Home Assistant. This will allow you to integrate your smoke, heat, co and occupancy status real-time in HA.

End-to-end image segmentation kit based on PaddlePaddle.

Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, IMPALA ...

Minimal PyTorch implementation of Generative Latent Optimization from the paper "Optimizing the Latent Space of Generative Networks"

Bringing Characters to Life with Computer Brains in Unity

Image segmentation with private İstanbul Dataset

[NeurIPS 2021] PyTorch Code for Accelerating Robotic Reinforcement Learning with Parameterized Action Primitives

Easy to use Audio Tagging in PyTorch

BraTs-VNet - BraTS(Brain Tumour Segmentation) using V-Net

Author: Wenhao Yu ([email protected]). ACL 2022. Commonsense Reasoning on Knowledge Graph for Text Generation

A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.

PyTorch implementation of "PatchGame: Learning to Signal Mid-level Patches in Referential Games" to appear in NeurIPS 2021

Red Team tool for exfiltrating files from a target's Google Drive that you have access to, via Google's API.

Codes for Causal Semantic Generative model (CSG), the model proposed in "Learning Causal Semantic Representation for Out-of-Distribution Prediction" (NeurIPS-21)

Level Based Customer Segmentation

MT-GAN-PyTorch - PyTorch Implementation of Learning to Transfer: Unsupervised Domain Translation via Meta-Learning

Dieser Scanner findet Websites, die nicht direkt in Suchmaschinen auftauchen, aber trotzdem erreichbar sind.

Pytorch implementation of Decoupled Spatial-Temporal Transformer for Video Inpainting

[ACMMM 2021 Oral] Enhanced Invertible Encoding for Learned Image Compression

novel deep learning research works with PaddlePaddle