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

Overview

tests linter codecov

python 3.6 release (latest by date) license

pre-commit code style: black

pypi version pypi downloads

Text Classification Baseline

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

Usage

Instead of writing custom code for specific text classification task, you just need:

  1. install pipeline:
pip install text-classification-baseline
  1. run pipeline:
  • either in terminal:
text-clf-train
  • or in python:
import text_clf

text_clf.train()

No data preparation is needed, only a csv file with two raw columns (with arbitrary names):

  • text
  • target

NOTE: the target can be presented in any format, including text - not necessarily integers from 0 to n_classes-1.

Config

The user interface consists of only one file config.yaml.

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

  • terminal:
text-clf-train --path_to_config config.yaml
  • python:
import text_clf

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

Default config.yaml:

seed: 42
verbose: true
path_to_save_folder: models

# data
data:
  train_data_path: data/train.csv
  valid_data_path: data/valid.csv
  sep: ','
  text_column: text
  target_column: target_name_short

# tf-idf
tf-idf:
  lowercase: true
  ngram_range: (1, 1)
  max_df: 1.0
  min_df: 0.0

# logreg
logreg:
  penalty: l2
  C: 1.0
  class_weight: balanced
  solver: saga
  multi_class: auto
  n_jobs: -1

NOTE: tf-idf and logreg are sklearn TfidfVectorizer and LogisticRegression 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 TF-IDF and LogReg steps
  • target_names.json - mapping from encoded target labels from 0 to n_classes-1 to it names
  • config.yaml - config that was used to train the model
  • logging.txt - logging file

Requirements

Python >= 3.6

Citation

If you use text-classification-baseline in a scientific publication, we would appreciate references to the following BibTex entry:

@misc{dayyass2021textclf,
    author       = {El-Ayyass, Dani},
    title        = {Pipeline for training text classification baselines},
    howpublished = {\url{https://github.com/dayyass/text-classification-baseline}},
    year         = {2021}
}
You might also like...
Code for EMNLP 2021 main conference paper
Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

Code for EMNLP 2021 main conference paper "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

This repository contains data used in the NAACL 2021 Paper - Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems

Proteno This is the data release associated with the corresponding NAACL 2021 Paper - Proteno: Text Normalization with Limited Data for Fast Deploymen

PyTorch implementation of Microsoft's text-to-speech system FastSpeech 2: Fast and High-Quality End-to-End Text to Speech.
PyTorch implementation of Microsoft's text-to-speech system FastSpeech 2: Fast and High-Quality End-to-End Text to Speech.

An implementation of Microsoft's "FastSpeech 2: Fast and High-Quality End-to-End Text to Speech"

glow-speak is a fast, local, neural text to speech system that uses eSpeak-ng as a text/phoneme front-end.

Glow-Speak glow-speak is a fast, local, neural text to speech system that uses eSpeak-ng as a text/phoneme front-end. Installation git clone https://g

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

Text-Summarization-using-NLP - Text Summarization using NLP  to fetch BBC News Article and summarize its text and also it includes custom article Summarization A Python package implementing a new model for text classification with visualization tools for Explainable AI :octocat:
A Python package implementing a new model for text classification with visualization tools for Explainable AI :octocat:

A Python package implementing a new model for text classification with visualization tools for Explainable AI 🍣 Online live demos: http://tworld.io/s

Text vectorization tool to outperform TFIDF for classification tasks
Text vectorization tool to outperform TFIDF for classification tasks

WHAT: Supervised text vectorization tool Textvec is a text vectorization tool, with the aim to implement all the "classic" text vectorization NLP meth

Text vectorization tool to outperform TFIDF for classification tasks
Text vectorization tool to outperform TFIDF for classification tasks

WHAT: Supervised text vectorization tool Textvec is a text vectorization tool, with the aim to implement all the "classic" text vectorization NLP meth

Comments
  • release v0.1.4

    release v0.1.4

    • fixed load_20newsgroups.py (#65 #71)
    • added Makefile (#71)
    • added logging confusion matrix (#72)
    • replaced all "valid" occurrences with "test" (#74)
    • updated docstrings (#77)
    • changed python interface - train function returns model and target_names_mapping (#78)
    enhancement 
    opened by dayyass 1
  • release v0.1.6

    release v0.1.6

    fixed token frequency support (add token frequency support #85) fixed threshold selection for binary classification (add threshold selection for binary classification #86)

    bug enhancement 
    opened by dayyass 0
  • release v0.1.5

    release v0.1.5

    • added lemmatization (#66)
    • added token frequency support (#84)
    • added threshold selection for binary classification (#79)
    • added arbitrary save folder name (#80)
    enhancement 
    opened by dayyass 0
  • release v0.1.5

    release v0.1.5

    • added lemmatization (#81)
    • added token frequency support (#85)
    • added threshold selection for binary classification (#86)
    • added arbitrary save folder name (#83)
    enhancement 
    opened by dayyass 0
Releases(v0.1.6)
  • v0.1.6(Nov 6, 2021)

    Release v0.1.6

    • fixed token frequency support (add token frequency support #85)
    • fixed threshold selection for binary classification (add threshold selection for binary classification #86)
    Source code(tar.gz)
    Source code(zip)
  • v0.1.5(Oct 21, 2021)

    Release v0.1.5 🥳🎉🍾

    • added pymorphy2 lemmatization (#81)
    • added token frequency support (#85)
    • added threshold selection for binary classification (#86)
    • added arbitrary save folder name (#83)

    pymorphy2 lemmatization (config.yaml)

    # preprocessing
    # (included in resulting model pipeline, so preserved for inference)
    preprocessing:
      lemmatization: pymorphy2
    

    token frequency support

    • text_clf.token_frequency.get_token_frequency(path_to_config) -
      get token frequency of train dataset according to the config file parameters

    threshold selection for binary classification

    • text_clf.pr_roc_curve.get_precision_recall_curve(path_to_model_folder) -
      get precision and recall metrics for precision-recall curve
    • text_clf.pr_roc_curve.get_roc_curve(path_to_model_folder) -
      get false positive rate (fpr) and true positive rate (tpr) metrics for roc curve
    • text_clf.pr_roc_curve.plot_precision_recall_curve(precision, recall) -
      plot precision-recall curve
    • text_clf.pr_roc_curve.plot_roc_curve(fpr, tpr) -
      plot roc curve
    • text_clf.pr_roc_curve.plot_precision_recall_f1_curves_for_thresholds(precision, recall, thresholds) -
      plot precision, recall, f1-score curves for probability thresholds

    arbitrary save folder name (config.yaml)

    experiment_name: model
    
    Source code(tar.gz)
    Source code(zip)
  • v0.1.4(Oct 10, 2021)

    • fixed load_20newsgroups.py (#65 #71)
    • added Makefile (#71)
    • added logging confusion matrix (#72)
    • replaced all "valid" occurrences with "test" (#74)
    • updated docstrings (#77)
    • changed python interface - train function returns model and target_names_mapping (#78)
    Source code(tar.gz)
    Source code(zip)
  • v0.1.3(Sep 2, 2021)

  • v0.1.2(Aug 19, 2021)

  • v0.1.1(Aug 11, 2021)

  • v0.1.0(Aug 7, 2021)

Owner
Dani El-Ayyass
NLP Tech Lead @ Sber AI, Master Student in Applied Mathematics and Computer Science @ CMC MSU
Dani El-Ayyass
Training code of Spatial Time Memory Network. Semi-supervised video object segmentation.

Training-code-of-STM This repository fully reproduces Space-Time Memory Networks Performance on Davis17 val set&Weights backbone training stage traini

haochen wang 128 Dec 11, 2022
[ICCV 2021] Instance-level Image Retrieval using Reranking Transformers

Instance-level Image Retrieval using Reranking Transformers Fuwen Tan, Jiangbo Yuan, Vicente Ordonez, ICCV 2021. Abstract Instance-level image retriev

UVA Computer Vision 86 Dec 28, 2022
Constituency Tree Labeling Tool

Constituency Tree Labeling Tool The purpose of this package is to solve the constituency tree labeling problem. Look from the dataset labeled by NLTK,

张宇 6 Dec 20, 2022
Client library to download and publish models and other files on the huggingface.co hub

huggingface_hub Client library to download and publish models and other files on the huggingface.co hub Do you have an open source ML library? We're l

Hugging Face 644 Jan 01, 2023
This project uses unsupervised machine learning to identify correlations between daily inoculation rates in the USA and twitter sentiment in regards to COVID-19.

Twitter COVID-19 Sentiment Analysis Members: Christopher Bach | Khalid Hamid Fallous | Jay Hirpara | Jing Tang | Graham Thomas | David Wetherhold Pro

4 Oct 15, 2022
Sequence-to-Sequence learning using PyTorch

Seq2Seq in PyTorch This is a complete suite for training sequence-to-sequence models in PyTorch. It consists of several models and code to both train

Elad Hoffer 514 Nov 17, 2022
NLP applications using deep learning.

NLP-Natural-Language-Processing NLP applications using deep learning like text generation etc. 1- Poetry Generation: Using a collection of Irish Poem

KASHISH 1 Jan 27, 2022
Google and Stanford University released a new pre-trained model called ELECTRA

Google and Stanford University released a new pre-trained model called ELECTRA, which has a much compact model size and relatively competitive performance compared to BERT and its variants. For furth

Yiming Cui 1.2k Dec 30, 2022
Unsupervised text tokenizer focused on computational efficiency

YouTokenToMe YouTokenToMe is an unsupervised text tokenizer focused on computational efficiency. It currently implements fast Byte Pair Encoding (BPE)

VK.com 847 Dec 19, 2022
Unofficial implementation of Google's FNet: Mixing Tokens with Fourier Transforms

FNet: Mixing Tokens with Fourier Transforms Pytorch implementation of Fnet : Mixing Tokens with Fourier Transforms. Citation: @misc{leethorp2021fnet,

Rishikesh (ऋषिकेश) 217 Dec 05, 2022
A fast and lightweight python-based CTC beam search decoder for speech recognition.

pyctcdecode A fast and feature-rich CTC beam search decoder for speech recognition written in Python, providing n-gram (kenlm) language model support

Kensho 315 Dec 21, 2022
SimBERT升级版(SimBERTv2)!

RoFormer-Sim RoFormer-Sim,又称SimBERTv2,是我们之前发布的SimBERT模型的升级版。 介绍 https://kexue.fm/archives/8454 训练 tensorflow 1.14 + keras 2.3.1 + bert4keras 0.10.6 下载

317 Dec 23, 2022
Use fastai-v2 with HuggingFace's pretrained transformers

FastHugs Use fastai v2 with HuggingFace's pretrained transformers, see the notebooks below depending on your task: Text classification: fasthugs_seq_c

Morgan McGuire 111 Nov 16, 2022
Code for paper "Role-oriented Network Embedding Based on Adversarial Learning between Higher-order and Local Features"

Role-oriented Network Embedding Based on Adversarial Learning between Higher-order and Local Features Train python main.py --dataset brazil-flights C

wang zhang 0 Jun 28, 2022
We have built a Voice based Personal Assistant for people to access files hands free in their device using natural language processing.

Voice Based Personal Assistant We have built a Voice based Personal Assistant for people to access files hands free in their device using natural lang

Rushabh 2 Nov 13, 2021
Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.

textgenrnn Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code, or quickly tr

Max Woolf 4.8k Dec 30, 2022
This code extends the neural style transfer image processing technique to video by generating smooth transitions between several reference style images

Neural Style Transfer Transition Video Processing By Brycen Westgarth and Tristan Jogminas Description This code extends the neural style transfer ima

Brycen Westgarth 110 Jan 07, 2023
Implementation of "Adversarial purification with Score-based generative models", ICML 2021

Adversarial Purification with Score-based Generative Models by Jongmin Yoon, Sung Ju Hwang, Juho Lee This repository includes the official PyTorch imp

15 Dec 15, 2022
Transformer training code for sequential tasks

Sequential Transformer This is a code for training Transformers on sequential tasks such as language modeling. Unlike the original Transformer archite

Meta Research 578 Dec 13, 2022
Contains the code and data for our #ICSE2022 paper titled as "CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Naming Sequences"

CodeFill This repository contains the code for our paper titled as "CodeFill: Multi-token Code Completion by Jointly Learning from Structure and Namin

Software Analytics Lab 11 Oct 31, 2022