LibMultiLabel — a Library for Multi-label Text Classification

LibMultiLabel is a simple tool with the following functionalities.

end-to-end services from raw texts to final evaluation/analysis
support of common network architectures for multi-label text classification

This is an on-going development so many improvements are still being made. Comments are very welcome.

Environments and Installation

Python: 3.6+
CUDA: 10.2 (if GPU used)
Pytorch 1.8+

If you have a different version of CUDA, go to the website for the detail of PyTorch installation.

To install the latest development version, run:

pip3 install -r requirements.txt

Quick Start via an Example
Usage
Data Format
Training and Prediction

Quick Start via an Example

Step 1. Data Preparation

Create a data sub-directory within LibMultiLabel and go to this sub-directory.

mkdir -p data/rcv1
cd data/rcv1

Download the rcv1 dataset from LIBSVM data sets by the following commands.

wget -O train.txt.bz2 https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel/rcv1_topics_train.txt.bz2
wget -O test.txt.bz2 https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel/rcv1_topics_test.txt.bz2

Uncompress data files and change the directory back to LibMultiLabel.

bzip2 -d *.bz2
cd ../..

Step 2. Training and Prediction

Train a cnn model and predict the test set by an example config. Use --cpu to run the program on the cpu.

python3 main.py --config example_config/rcv1/kim_cnn.yml

Usage

The LibMultiLabel toolkit uses a yaml file to configure the dataset and the training process. At a high level, the config file is split into four parts:

data: the data config consists of data paths that tell where to place the datasets, pre-trained word embeddings, and vocabularies.
model: the model config defines the parameters that are related to the network definition (i.e., model name).
train: the train config specifies the hyperparameters (i.e., batch size, learning rate, etc.) used when training a model.
eval: the eval config decides metrics monitored or reported during the evaluation process.

For more information, run --help or check the example configs.

python3 main.py --help

Each parameter can also be specified through the command line.

Data Format

Put labels and texts in the training, validation, and test set separately in train.txt, valid.txt, and test.txt, or specifying the path by the arguments train_path, valid_path, and test_path. If validation set is not provided, then the program internally splits the training set to two parts for training and validation. To specify the size of the validation set, use val_size in one of the following two ways:

a ratio in [0,1] to indicate the percentage of training data allocated for validation
an integer to indicate the number of training data allocated for validation

Examples of a training file:

one sample per line
seperate ID, labels and texts by <TAB> (the ID column is optional)
labels are split by spaces

With ID column:

2286<TAB>E11 ECAT M11 M12 MCAT<TAB>recov recov recov recov excit ...
2287<TAB>C24 CCAT<TAB>uruguay uruguay compan compan compan ...

Without ID column:

E11 ECAT M11 M12 MCAT<TAB>recov recov recov recov excit ...
C24 CCAT<TAB>uruguay uruguay compan compan compan ...

Examples of a test file:

In the test set, the labels are used to calculate accuracy or errors. If it's unknown, any string (even an empty one) is fine. For example,

With ID column:

2286<TAB><TAB>recov recov recov recov excit ...
2287<TAB><TAB>uruguay uruguay compan compan compan ...

Without ID column:

<TAB>recov recov recov recov excit ...
<TAB>uruguay uruguay compan compan compan ...

Training and Prediction

Training

In the training procedure, you can build a model from scratch or start from some pre-obtained information.

python3 main.py --config CONFIG_PATH [--load_checkpoint CHECKPOINT_PATH] \
[--embed_file EMBED_NAME_OR_EMBED_PATH] [--vocab_file VOCAB_CSV_PATH]

config: configure parameters in a yaml file. See the section Usage.

If a model was trained before by this package, the training procedure can start with it.

load_checkpoint: specify the path to a pre-trained model.

To use your own word embeddings or vocabulary set, specify the following parameters:

embed_file: choose one of the pretrained embeddings defined in torchtext or specify the path to your word embeddings with each line containing a word followed by its vectors. Example:
```
team -0.17901678383350372 1.2405720949172974 ...
language 0.8667483925819397 5.001194953918457 ...
```
vocab_file: set the file path to a predefined vocabulary set that contains lines of words.

For the validation process in the training procedure, you can evaluate the model with a set of evaluation metrics. Set monitor_metrics to define what you want to print on the screen. The argument val_metric is the metric for picking the best model. Example:

monitor_metrics: [P@1, P@3, P@5]
val_metric: P@1

If test_path is specified or DATA_DIR/test.txt exists, the model with the highest val_metric will be evaluated after training.

Prediction

To deploy/evaluate a model (i.e., a pre-obtained checkpoint), you can predict a test set by the following command.

python3 main.py --eval --config CONFIG_PATH --load_checkpoint CHECKPOINT_PATH --test_path TEST_DATA_PATH --save_k_predictions K --predict_out_path PREDICT_OUT_PATH

Use --save_k_predictions to save the top K predictions for each instance in the test set. K=100 if not specified.
Use --predict_out_path to specify the file for storing the predicted top-K labels/scores.

avain / LibMultiLabel