bunruija 0.0.4

A text classification toolkit

Bunruija

Bunruija is a text classification toolkit. Bunruija aims at enabling pre-processing, training and evaluation of text classification models with minimum coding effort. Bunruija is mainly focusing on Japanese though it is also applicable to other languages.

See example for understanding how bunruija is easy to use.

Features

• Minimum requirements of coding: bunruija enables users to train and evaluate their models through command lines. Because all experimental settings are stored in a yaml file, users do not have to write codes.
• Easy to compare neural-based model with non-neural-based model: because bunruija supports models based on scikit-learn and PyTorch in the same framework, users can easily compare classification accuracies and prediction times of neural- and non-neural-based models.
• Easy to reproduce the training of a model: because all hyperparameters of a model are stored in a yaml file, it is easy to reproduce the model.

Install

pip install bunruija

Example configs

Example of sklearn.svm.SVC

data:
  train: train.csv
  dev: dev.csv
  test: test.csv

bin_dir: models/svm-model

pipeline:
  - type: sklearn.feature_extraction.text.TfidfVectorizer
    args:
      tokenizer:
        type: bunruija.tokenizers.mecab_tokenizer.MeCabTokenizer
        args:
          lemmatize: true
          exclude_pos:
            - 助詞
            - 助動詞
      max_features: 10000
      min_df: 3
      ngram_range:
        - 1
        - 3
  - type: sklearn.svm.SVC
    args:
      verbose: false
      C: 10.

Example of BERT

data:
  train: train.csv
  dev: dev.csv
  test: test.csv

bin_dir: models/transformer-model

pipeline:
  - type: bunruija.feature_extraction.sequence.SequenceVectorizer
    args:
      tokenizer:
        type: transformers.AutoTokenizer
        args:
          pretrained_model_name_or_path: cl-tohoku/bert-base-japanese
  - type: bunruija.classifiers.transformer.TransformerClassifier
    args:
      device: cpu
      pretrained_model_name_or_path: cl-tohoku/bert-base-japanese
      optimizer: adamw
      lr: 3e-5
      max_epochs: 3
      weight_decay: 0.01

CLI

# Training a classifier
bunruija-train -y config.yaml

# Evaluating the trained classifier
bunruija-evaluate -y config.yaml

Prediction using the trained classifier in Python code

from bunruija import Predictor

predictor = Predictor(args.yaml)
while True:
    text = input("Input:")
    label: list[str] = predictor([text], return_label_type="str")
    print(label[0])