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

Related tags

Text Data & NLPpythonnlpfastdata-sciencemachine-learningnatural-language-processingtext-classificationtextclassificationbaselinelogistic-regressiontf-idf
Overview

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