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

Last update: Dec 07, 2022

Overview

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:

install pipeline:

pip install text-classification-baseline

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 "Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification"

105 Jan 3, 2023

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

37 Dec 4, 2022

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"

1k Dec 30, 2022

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

8 Dec 25, 2022

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

7 Sep 20, 2022

Text-Summarization-using-NLP - Text Summarization using NLP to fetch BBC News Article and summarize its text and also it includes custom article Summarization

Text-Summarization-using-NLP Text Summarization using NLP to fetch BBC News Arti

21 Aug 6, 2022

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

285 Jan 2, 2023

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

186 Dec 29, 2022

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

160 Feb 9, 2021

Comments

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

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
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
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)
added hyper-parameters tuning (#58)

Source code(tar.gz)
Source code(zip)
v0.1.2(Aug 19, 2021)
fixed bug with multiple logging (#55)

Source code(tar.gz)
Source code(zip)
v0.1.1(Aug 11, 2021)
added logging (#43)

added unittests (#49)

added CI with linter, tests, codecov (#46 #49)

added docker (#48)

Source code(tar.gz)
Source code(zip)
v0.1.0(Aug 7, 2021)

First release.
Source code(tar.gz)
Source code(zip)

Owner

Dani El-Ayyass

NLP Tech Lead @ Sber AI, Master Student in Applied Mathematics and Computer Science @ CMC MSU

GitHub Repository https://pypi.org/project/text-classification-baseline/

Neural network sequence labeling model

Sequence labeler This is a neural network sequence labeling system. Given a sequence of tokens, it will learn to assign labels to each token. Can be u

250 Nov 03, 2022

Chinese Named Entity Recognization (BiLSTM with PyTorch)

BiLSTM-CRF for Name Entity Recognition PyTorch version A PyTorch implemention of Bi-LSTM-CRF model for Chinese Named Entity Recognition. 使用 PyTorch 实现

5 Jun 01, 2022

Training open neural machine translation models

Train Opus-MT models This package includes scripts for training NMT models using MarianNMT and OPUS data for OPUS-MT. More details are given in the Ma

167 Jan 03, 2023

iBOT: Image BERT Pre-Training with Online Tokenizer

Image BERT Pre-Training with iBOT Official PyTorch implementation and pretrained models for paper iBOT: Image BERT Pre-Training with Online Tokenizer.

435 Jan 06, 2023

A python wrapper around the ZPar parser for English.

NOTE This project is no longer under active development since there are now really nice pure Python parsers such as Stanza and Spacy. The repository w

49 Sep 12, 2022

A Multi-modal Model Chinese Spell Checker Released on ACL2021.

ReaLiSe ReaLiSe is a multi-modal Chinese spell checking model. This the office code for the paper Read, Listen, and See: Leveraging Multimodal Informa

106 Dec 29, 2022

Gpt2-WebAPI - The objective of this API is to provide the 3 best possible responses to sentences that the user would input via http GET request as a parameter

This repository is a modification of: https://github.com/openai/gpt-2 for our sp

1 Jan 06, 2022

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

Related tags

Overview

Text Classification Baseline

Usage

Config

Output

Requirements

Citation

You might also like...

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

PyTorch implementation of Microsoft's text-to-speech system 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.

Pipeline for chemical image-to-text competition

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:

Text vectorization tool to outperform TFIDF for classification tasks

Text vectorization tool to outperform TFIDF for classification tasks

Comments

release v0.1.4

release v0.1.6

release v0.1.5

release v0.1.5

Releases(v0.1.6)

v0.1.6(Nov 6, 2021)

Release v0.1.6

v0.1.5(Oct 21, 2021)

Release v0.1.5 🥳🎉🍾

pymorphy2 lemmatization (config.yaml)

token frequency support

threshold selection for binary classification

arbitrary save folder name (config.yaml)

v0.1.4(Oct 10, 2021)

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

Neural network sequence labeling model

Chinese Named Entity Recognization (BiLSTM with PyTorch)

Training open neural machine translation models

iBOT: Image BERT Pre-Training with Online Tokenizer

A python wrapper around the ZPar parser for English.

A Multi-modal Model Chinese Spell Checker Released on ACL2021.

Gpt2-WebAPI - The objective of this API is to provide the 3 best possible responses to sentences that the user would input via http GET request as a parameter

结巴中文分词

A raytrace framework using taichi language

Repository to hold code for the cap-bot varient that is being presented at the SIIC Defence Hackathon 2021.

A PyTorch-based model pruning toolkit for pre-trained language models

SimCSE: Simple Contrastive Learning of Sentence Embeddings

Code for ACL 2020 paper "Rigid Formats Controlled Text Generation"

Command Line Text-To-Speech using Google TTS

A fast and easy implementation of Transformer with PyTorch.

Transformation spoken text to written text

Create a semantic search engine with a neural network (i.e. BERT) whose knowledge base can be updated

YACLC - Yet Another Chinese Learner Corpus

Nmt - TensorFlow Neural Machine Translation Tutorial

中文医疗信息处理基准CBLUE: A Chinese Biomedical LanguageUnderstanding Evaluation Benchmark