Pipeline for training LSA models using Scikit-Learn.

Overview

tests linter codecov

python 3.6 release (latest by date) license

pre-commit code style: black

pypi version pypi downloads

Latent Semantic Analysis

Pipeline for training LSA models using Scikit-Learn.

Usage

Instead of writing custom code for latent semantic analysis, you just need:

  1. install pipeline:
pip install latent-semantic-analysis
  1. run pipeline:
  • either in terminal:
lsa-train --path_to_config config.yaml
  • or in python:
import latent_semantic_analysis

latent_semantic_analysis.train(path_to_config="config.yaml")

NOTE: more about config file here.

No data preparation is needed, only a csv file with raw text column (with arbitrary name).

Config

The user interface consists of only one files:

  • config.yaml - general configuration with sklearn TF-IDF and SVD parameters

Change config.yaml to create the desired configuration and train LSA model with the following command:

  • terminal:
lsa-train --path_to_config config.yaml
  • python:
import latent_semantic_analysis

latent_semantic_analysis.train(path_to_config="config.yaml")

Default config.yaml:

seed: 42
path_to_save_folder: models

# data
data:
  data_path: data/data.csv
  sep: ','
  text_column: text

# tf-idf
tf-idf:
  lowercase: true
  ngram_range: (1, 1)
  max_df: 1.0
  min_df: 1

# svd
svd:
  n_components: 10
  algorithm: arpack

NOTE: tf-idf and svd are sklearn TfidfVectorizer and TruncatedSVD parameters correspondingly, so you can parameterize instances of these classes however you want.

Output

After training the model, the pipeline will return the following files:

  • model.joblib - sklearn pipeline with LSA (TF-IDF and SVD steps)
  • config.yaml - config that was used to train the model
  • logging.txt - logging file
  • doc2topic.json - document embeddings
  • term2topic.json - term embeddings

Requirements

Python >= 3.6

Citation

If you use latent-semantic-analysis in a scientific publication, we would appreciate references to the following BibTex entry:

@misc{dayyass2021lsa,
    author       = {El-Ayyass, Dani},
    title        = {Pipeline for training LSA models},
    howpublished = {\url{https://github.com/dayyass/latent-semantic-analysis}},
    year         = {2021}
}
You might also like...
This repository contains all the source code that is needed for the project : An Efficient Pipeline For Bloom’s Taxonomy Using Natural Language Processing and Deep Learning
This repository contains all the source code that is needed for the project : An Efficient Pipeline For Bloom’s Taxonomy Using Natural Language Processing and Deep Learning

Pipeline For NLP with Bloom's Taxonomy Using Improved Question Classification and Question Generation using Deep Learning This repository contains all

Universal End2End Training Platform, including pre-training, classification tasks, machine translation, and etc.

背景 安装教程 快速上手 (一)预训练模型 (二)机器翻译 (三)文本分类 TenTrans 进阶 1. 多语言机器翻译 2. 跨语言预训练 背景 TrenTrans是一个统一的端到端的多语言多任务预训练平台,支持多种预训练方式,以及序列生成和自然语言理解任务。 安装教程 git clone git

Toy example of an applied ML pipeline for me to experiment with MLOps tools.

Toy Machine Learning Pipeline Table of Contents About Getting Started ML task description and evaluation procedure Dataset description Repository stru

Pipeline for chemical image-to-text competition
Pipeline for chemical image-to-text competition

BMS-Molecular-Translation Introduction This is a pipeline for Bristol-Myers Squibb – Molecular Translation by Vadim Timakin and Maksim Zhdanov. We got

Pipeline for fast building text classification TF-IDF + LogReg baselines.

Text Classification Baseline Pipeline for fast building text classification TF-IDF + LogReg baselines. Usage Instead of writing custom code for specif

A Multilingual Latent Dirichlet Allocation (LDA) Pipeline with Stop Words Removal, n-gram features, and Inverse Stemming, in Python.

Multilingual Latent Dirichlet Allocation (LDA) Pipeline This project is for text clustering using the Latent Dirichlet Allocation (LDA) algorithm. It

MHtyper is an end-to-end pipeline for recognized the Forensic microhaplotypes in Nanopore sequencing data.

MHtyper is an end-to-end pipeline for recognized the Forensic microhaplotypes in Nanopore sequencing data. It is implemented using Python.

BookNLP, a natural language processing pipeline for books
BookNLP, a natural language processing pipeline for books

BookNLP BookNLP is a natural language processing pipeline that scales to books and other long documents (in English), including: Part-of-speech taggin

Vad-sli-asr - A Python scripts for a speech processing pipeline with Voice Activity Detection (VAD)
Vad-sli-asr - A Python scripts for a speech processing pipeline with Voice Activity Detection (VAD)

VAD-SLI-ASR Python scripts for a speech processing pipeline with Voice Activity

Releases(v0.1.0)
Owner
Dani El-Ayyass
NLP Tech Lead @ Sber AI, Master Student in Applied Mathematics and Computer Science @ CMC MSU
Dani El-Ayyass
A minimal Conformer ASR implementation adapted from ESPnet.

Conformer ASR A minimal Conformer ASR implementation adapted from ESPnet. Introduction I want to use the pre-trained English ASR model provided by ESP

Niu Zhe 3 Jan 24, 2022
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition

CRNN paper:An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition 1. create your ow

Tsukinousag1 3 Apr 02, 2022
TFPNER: Exploration on the Named Entity Recognition of Token Fused with Part-of-Speech

TFPNER TFPNER: Exploration on the Named Entity Recognition of Token Fused with Part-of-Speech Named entity recognition (NER), which aims at identifyin

1 Feb 07, 2022
aMLP Transformer Model for Japanese

aMLP-japanese Japanese aMLP Pretrained Model aMLPとは、Liu, Daiらが提案する、Transformerモデルです。 ざっくりというと、BERTの代わりに使えて、より性能の良いモデルです。 詳しい解説は、こちらの記事などを参考にしてください。 この

tanreinama 13 Aug 11, 2022
Transcribing audio files using Hugging Face's implementation of Wav2Vec2 + "chain-linking" NLP tasks to combine speech-to-text with downstream tasks like translation and summarisation.

PART 2: CHAIN LINKING AUDIO-TO-TEXT NLP TASKS 2A: TRANSCRIBE-TRANSLATE-SENTIMENT-ANALYSIS In notebook3.0, I demo a simple workflow to: transcribe a lo

Chua Chin Hon 30 Jul 13, 2022
Sorce code and datasets for "K-BERT: Enabling Language Representation with Knowledge Graph",

K-BERT Sorce code and datasets for "K-BERT: Enabling Language Representation with Knowledge Graph", which is implemented based on the UER framework. R

Weijie Liu 834 Jan 09, 2023
Maix Speech AI lib, including ASR, chat, TTS etc.

Maix-Speech 中文 | English Brief Now only support Chinese, See 中文 Build Clone code by: git clone https://github.com/sipeed/Maix-Speech Compile x86x64 c

Sipeed 267 Dec 25, 2022
Application for shadowing Chinese.

chinese-shadowing Simple APP for shadowing chinese. With this application, it is very easy to record yourself, play the sound recorded and listen to s

Thomas Hirtz 5 Sep 06, 2022
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge. Proceedings of EMNLP 2021

AAGCN-ACSA EMNLP 2021 Introduction This repository was used in our paper: Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment An

Akuchi 36 Dec 18, 2022
运小筹公众号是致力于分享运筹优化(LP、MIP、NLP、随机规划、鲁棒优化)、凸优化、强化学习等研究领域的内容以及涉及到的算法的代码实现。

OlittleRer 运小筹公众号是致力于分享运筹优化(LP、MIP、NLP、随机规划、鲁棒优化)、凸优化、强化学习等研究领域的内容以及涉及到的算法的代码实现。编程语言和工具包括Java、Python、Matlab、CPLEX、Gurobi、SCIP 等。 关注我们: 运筹小公众号 有问题可以直接在

运小筹 151 Dec 30, 2022
🏖 Easy training and deployment of seq2seq models.

Headliner Headliner is a sequence modeling library that eases the training and in particular, the deployment of custom sequence models for both resear

Axel Springer Ideas Engineering GmbH 231 Nov 18, 2022
A Chinese to English Neural Model Translation Project

ZH-EN NMT Chinese to English Neural Machine Translation This project is inspired by Stanford's CS224N NMT Project Dataset used in this project: News C

Zhenbang Feng 29 Nov 26, 2022
this repository has datasets containing information of Uber pickups in NYC from April 2014 to September 2014 and January to June 2015. data Analysis , virtualization and some insights are gathered here

uber-pickups-analysis Data Source: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city Information about data set The dataset contain

1 Nov 02, 2021
Code for the Findings of NAACL 2022(Long Paper): AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks

AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks arXiv link: upcoming To be published in Findings of NA

Allen 16 Nov 12, 2022
Implementation for paper BLEU: a Method for Automatic Evaluation of Machine Translation

BLEU Score Implementation for paper: BLEU: a Method for Automatic Evaluation of Machine Translation Author: Ba Ngoc from ProtonX BLEU score is a popul

Ngoc Nguyen Ba 6 Oct 07, 2021
BERTAC (BERT-style transformer-based language model with Adversarially pretrained Convolutional neural network)

BERTAC (BERT-style transformer-based language model with Adversarially pretrained Convolutional neural network) BERTAC is a framework that combines a

6 Jan 24, 2022
Nystromformer: A Nystrom-based Algorithm for Approximating Self-Attention

Nystromformer: A Nystrom-based Algorithm for Approximating Self-Attention April 6, 2021 We extended segment-means to compute landmarks without requiri

Zhanpeng Zeng 322 Jan 01, 2023
숭실대학교 컴퓨터학부 전공종합설계프로젝트

✨ 시각장애인을 위한 버스도착 알림 장치 ✨ 👀 개요 현대 사회에서 대중교통 위치 정보를 이용하여 사람들이 간단하게 이용할 대중교통의 정보를 얻고 쉽게 대중교통을 이용할 수 있다. 해당 정보는 각종 어플리케이션과 대중교통 이용시설에서 위치 정보를 제공하고 있지만 시각

taegyun 3 Jan 25, 2022
The simple project to separate mixed voice (2 clean voices) to 2 separate voices.

Speech Separation The simple project to separate mixed voice (2 clean voices) to 2 separate voices. Result Example (Clisk to hear the voices): mix ||

vuthede 31 Oct 30, 2022