CLaF is a Language Framework built on PyTorch that provides following two high-level features:
Experiment
enables the control of training flow in general NLP by offering variousTokenMaker
methods.- CLaF is inspired by the designe principle of AllenNLP such as the higher level concepts and reusable code, but mostly based on PyTorch’s common module, so that user can easily modify the code on their demands.
Machine
helps to combine various modules to build a NLP Machine in one place.- There are knowledge-based, components and trained experiments which infer 1-example in modules.
- Python 3.6
- PyTorch >= 0.4.1
- MeCab for Korean Tokenizer
sh script/install_mecab.sh
It is recommended to use the virtual environment.
Conda is the easiest way to set up a virtual environment.
conda create -n claf python=3.6
conda activate claf
(claf) ✗ pip install -r requirements.txt
Commands to install via pip
pip install claf
- Multilingual modeling support (currently, English and Korean are supported).
- Light weighted Systemization and Modularization.
- Easy extension and implementation of models.
- A wide variation of Experiments with reproducible and comprehensive logging
- The metrics for services such as "1-example inference latency" are provided.
- Easy to build of a NLP Machine by combining modules.
- Training Flow
-
only Arguments
python train.py --train_file_path {file_path} --valid_file_path {file_path} --model_name {name} ...
-
only BaseConfig (skip
/base_config
path)python train.py --base_config {base_config}
-
BaseConfig + Arguments
python train.py --base_config {base_config} --learning_rate 0.002
- Load BaseConfig then overwrite
learning_rate
to 0.002
- Load BaseConfig then overwrite
Declarative experiment config (.json)
- Simply matching with object's parameters
- Exists samples in
/base_config
directory
Base Config:
--base_config BASE_CONFIG
Use pre-defined base_config:
* SQuAD:
['squad/bert_large_uncased', 'squad/bidaf', 'squad/drqa_paper', 'squad/drqa', 'squad/bert_base_uncased', 'squad/qanet', 'squad/docqa+elmo', 'squad/bidaf_no_answer', 'squad/docqa_no_answer', 'squad/qanet_paper', 'squad/bidaf+elmo', 'squad/docqa']
* KorQuAD:
['korquad/bidaf', 'korquad/docqa']
* WikiSQL:
['wikisql/sqlnet']
* CoLA:
['cola/bert_large_uncased', 'cola/structured_self_attention']
* CoNLL 2003:
['conll2003/bert_large_cased']
python eval.py <data_path> <model_checkpoint_path>
- Example
✗ python eval.py data/squad/dev-v1.1.json logs/squad/bidaf/checkpoint/model_19.pkl
...
[INFO] - {
"valid/loss": 2.59111491665019,
"valid/epoch_time": 60.7434446811676,
"valid/start_acc": 63.17880794701987,
"valid/end_acc": 67.19016083254493,
"valid/span_acc": 54.45600756859035,
"valid/em": 68.10785241248817,
"valid/f1": 77.77963381714842
}
# write predictions files (<log_dir>/predictions/predictions-valid-19.json)
- 1-example Inference Latency (Summary)
✗ python eval.py data/squad/dev-v1.1.json logs/squad/bidaf/checkpoint/model_19.pkl
...
# Evaluate Inference Latency Mode.
...
[INFO] - saved inference_latency results. bidaf-cpu.json # file_format: {model_name}-{env}.json
python predict.py <model_checkpoint_path> --<arguments>
- Example
✗ python predict.py logs/squad/bidaf/checkpoint/model_19.pkl \
--question "When was the last Super Bowl in California?" \
--context "On May 21, 2013, NFL owners at their spring meetings in Boston voted and awarded the game to Levi's Stadium. The $1.2 billion stadium opened in 2014. It is the first Super Bowl held in the San Francisco Bay Area since Super Bowl XIX in 1985, and the first in California since Super Bowl XXXVII took place in San Diego in 2003."
>>> Predict: {'text': '2003', 'score': 4.1640071868896484}
- Machine Architecture
- Define the config file (.json) like BaseConfig in
machine_config/
directory - Run CLaF Machine (skip
/machine_config
path)
✗ python machine.py --machine_config {machine_config}
- The list of pre-defined
Machine
:
Machine Config:
--machine_config MACHINE_CONFIG
Use pre-defined machine_config (.json (.json))
['ko_wiki', 'nlu']
DrQA is a system for reading comprehension applied to open-domain question answering. The system has to combine the challenges of document retrieval (finding the relevant documents) with that of machine comprehension of text (identifying the answers from those documents).
- ko_wiki: Korean Wiki Version
✗ python machine.py --machine_config ko_wiki
...
Completed!
Question > 동학의 2대 교주 이름은?
--------------------------------------------------
Doc Scores:
- 교주 : 0.5347289443016052
- 이교주 : 0.4967213571071625
- 교주도 : 0.49036136269569397
- 동학 : 0.4800325632095337
- 동학중학교 : 0.4352934956550598
--------------------------------------------------
Answer: [
{
"text": "최시형",
"score": 11.073444366455078
},
{
"text": "충주목",
"score": 9.443866729736328
},
{
"text": "반월동",
"score": 9.37778091430664
},
{
"text": "환조 이자춘",
"score": 4.64817476272583
},
{
"text": "합포군",
"score": 3.3186707496643066
}
]
The reason why NLU machine does not return the full response is that response generation may require various task-specific post-processing techniques or additional logic(e.g. API calls, template-decision rules, template filling rules, nn-based response generation model) Therefore, for flexible usage, NLU machine returns only the NLU result.
✗ python machine.py --machine_config nlu
...
Utterance > "looking for a flight from Boston to Seoul or Incheon"
NLU Result: {
"intent": "flight",
"slots": {
"city.depart": ["Boston"],
"city.dest": ["Seoul", "Incheon"]
}
}
Thanks for your interest in contributing! There are many ways to contribute to this project.
Get started here.
CLaF is currently maintained by
- Dongjun Lee (Author)
- Sohee Yang
- Minjeong Kim
docs/
directory which includes documentation created by Sphinx.
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