bunruija 0.1.0

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

output_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

output_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:
        type: torch.optim.AdamW
        args:
          lr: 3e-5
          weight_decay: 0.01
          betas:
            - 0.9
            - 0.999
      max_epochs: 3

CLI

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

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

Config

data

You can set data-related settings in data.

data:
  train: train.csv  # training data
  dev: dev.csv # development data
  test: test.csv # test data
  label_column: label
  text_column: text

You can set local files in train, dev, and test. Supported types are csv, json and jsonl. label_column and text_column are field names of label and text. When you set label_column to label and text_column to text, which are the default values, actual data must be as follows:

Format of csv:

label,text
label_name,sentence
…

Format of json:

[{"label", "label_name", "text": "sentence"}]

Format of jsonl:

{"label", "label_name", "text": "sentence"}

pipeline

You can set pipeline of your model in pipeline

Prediction using the trained classifier in Python code

After you trained a classification model, you can use that model for prediction as follows:

from bunruija import Predictor

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

output_dir is a directory that is specified in output_dir in config.