text-classification-baseline 0.1.2

TF-IDF + LogReg baseline for text classification

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

2. 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}
}