claf 0.1.6

claf:clova语言框架

claf是一个建立在pytorch上的语言框架，它提供以下两个高级功能：

• Experiment通过提供各种方法来控制一般nlp中的训练流。
  • claf的设计灵感来自于AllenNLP的设计原则，如更高层次的概念和可重用的代码，但大多基于pytorch的公共模块，因此用户可以根据自己的需求轻松修改代码。
• Machine有助于组合各种模块，在一个地方构建nlp机器。
  • 有基于知识的、组件的、经过训练的实验，在模块中推理出一个实例。

目录

• Installation
  • Requirements
  • Install via pip
• Overview
• Experiment
  • Features
  • Usage
    • Training
    • Evaluate
    • Predict
    • Docker Images
• Machine
• Contributing
• Maintainers
• Citing
• License

安装

要求

• python 3.6
• Pythorch=0.4.1
• MeCab用于朝鲜语标记器
  • sh script/install_mecab.sh

建议使用虚拟环境。
Conda是建立虚拟环境的最简单方法。

conda create -n claf python=3.6
conda activate claf

(claf) ✗ pip install -r requirements.txt

通过PIP安装

通过pip安装的命令

pip install claf

概述

• multilingual支持建模（目前支持英语和朝鲜语）。
• 轻量级系统化和模块化。
• 模型易于扩展和实现。
• 实验的广泛变化具有可重复和全面的日志记录
• 提供了诸如“1示例推理延迟”之类的服务度量。
• 通过组合模块，易于构建nlpmachine。

实验

• 培训流程

功能

• Full Documentation
• Dataset And Model
• Pretrained Vector
• Tokens
• 报告
  • HistoryQA
  • KorQuAD
  • SQuAD
  • WikiSQL
• 摘要（1例推理延迟）
  • Reading Comprehension

使用量

培训

1. 仅参数

   python train.py --train_file_path {file_path} --valid_file_path {file_path} --model_name {name} ...
   

2. 仅baseconfig（跳过/base_config路径）

   python train.py --base_config {base_config}
   

3. 基本配置+参数

   python train.py --base_config {base_config} --learning_rate 0.002
   

   • 加载baseconfig，然后覆盖learning_rate到0.002

基本配置

声明性实验配置（.json）

• 只需与对象的参数匹配
存在于{{CD5}}目录中的示例

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']

评估

python eval.py <data_path> <model_checkpoint_path>

• 示例

✗ 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)

• 一个示例推理延迟（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

预测

python predict.py <model_checkpoint_path> --<arguments>

• 示例

✗ 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}

Docker图像

• Docker Hub
• 使用Docker图像运行
  • 拉码头图片 ✗ docker pull claf/claf:latest
  • 跑步 docker run --rm -i -t claf/claf:latest /bin/bash

机器

• 机器结构

用法

• 定义配置文件（.json），比如machine_config/目录中的BaseConfig
• 运行claf machine（跳过/machine_config路径）

✗ python machine.py --machine_config {machine_config}

• 预定义的列表Machine：

Machine Config:
  --machine_config MACHINE_CONFIG
    Use pre-defined machine_config (.json (.json))

    ['ko_wiki', 'nlu']

打开QA（DRQA）

drqa是一个应用于开放领域问答的阅读理解系统。该系统必须将文档检索（查找相关文档）和文本机器理解（从这些文档中识别答案）的挑战结合起来。

• ko_wiki：韩文wiki版本

✗ 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
    }
]

NLU（去离子alog）

✗ 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"]
    }
}

@misc{CL,author={Lee, Dongjun and Yang, Sohee and Kim, Minjeong},title={CLaF: Open-Source Clova Language Framework},year={2019},publisher={GitHub},journal={GitHub repository},howpublished={\url{https://github.com/naver/claf}}}

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