Practical overview
When it fits
Use CLaF when you want a modifiable PyTorch framework for language-task experiments or want to compose documented NLP modules into an inference pipeline.
Who it is for
- NLP researchers comparing or training models on language-task datasets
- Developers extending PyTorch-based NLP models and tokenization components
- Teams assembling modular inference pipelines from trained experiments
Documented uses
- Configure and train NLP models using command-line arguments or reusable BaseConfig files
- Evaluate a model checkpoint against a data file
- Run prediction with a checkpoint, question and context
- Combine NLP components and trained experiments into a Machine configuration
- Explore documented datasets, models, pretrained vectors, tokenizers and token makers
Requirements & limits
The project is open source under the MIT license. The README documents installation through pip and a requirements file.
- The documented environment specifies Python 3.6 and PyTorch 1.3.1 or later.
- MeCab is required for Korean tokenization.
- The README describes English and Korean modeling support and lists task-specific dataset and model examples.
- Training can be configured with explicit arguments, BaseConfig files, or a combination of both.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install CLaF in a Python environment
Set up the documented Python environment and install the framework.
Before you start
- Python 3.6
- PyTorch 1.3.1 or later
- A virtual environment is recommended
Steps
Create and activate a Python 3.6 Conda environment.
Install project dependencies with the command below, or install CLaF with the command below.
pip install -r requirements.txtpip install clafFor Korean tokenization, install MeCab using the repository's the command below instruction.
sh script/install_mecab.sh
Expected outcome
A CLaF installation in a Python environment; MeCab is separately required for the Korean tokenizer.
Guide limitations
- The README specifies Python 3.6 and PyTorch 1.3.1 or later.
- MeCab is needed for Korean tokenization, not listed as a general requirement.
Configure and run an NLP training experiment
Start a documented training workflow using a supplied BaseConfig or explicit arguments.
Before you start
- CLaF installed with its documented requirements
- Training and validation data files when using file-path arguments
- A model name when using the explicit-arguments form, or a BaseConfig
Steps
Choose a sample BaseConfig from the repository's
base_configdirectory, or prepare training and validation file paths and a model name.Run the command below to select a BaseConfig.
python train.py --base_config {base_config}Alternatively, run the command below.
python train.py --train_file_path {file_path} --valid_file_path {file_path} --model_name {name} ...To override a configuration value, combine the options, as in the command below.
python train.py --base_config {base_config} --learning_rate 0.002
Expected outcome
A training command configured through explicit arguments, a BaseConfig, or both.
Guide limitations
- The README documents command forms but does not establish that a particular run will succeed with arbitrary data or configurations.
- Dataset and model choices depend on the selected task and configuration.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- naver/claf · Official homepage title · retained in its source language