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klue-level2-nlp-19

Main Subject

Relation Extraction is a problem of predicting attributes and relationships for words in a sentence. Relationship extraction is a key component for building a knowledge graph, and is important in natural language processing applications such as structured search, emotional analysis, question answering, and summary.

In this competition, we will learn a model that infers the relationship between words in sentences through information on sentences and words. Through this, our artificial intelligence model can understand the attributes and relationships of words and learn concepts.

Installation

1. Set up the python environment:

  • Recommended python version 3.8.5
 conda create -n venv python=3.8.5 pip
 conda activate venv

2. Install other required packages

  • pandas==1.1.5
  • scikit-learn~=0.24.1
  • transformers==4.10.0
  • fairseq
  • numpy
  • sentencepiece
  • konlpy
 cd whatever-your-directory_contain_repo
 pip install -r klue-level2-nlp-19/requirements.txt

Dataset

It's unusable due to copyright. You have to prepare your own data set.

Classes for Classification

  • 30 Classes about relationship between two entities


Function Description

train.py - The designated Arguments can be hyperparameters to learn Entry marker, Data augment, LR Scheduling, etc.

inference.py - Based on the stored model and config file, the prediction result for the test data is stored in csv.

load_data.py - It is a module that allows data to be imported and preprocessed and tokenized.

entity_marker.py - Add special token using punctual mark.

new_model.py - MLP layer followed by RobertaMaskedLM.

modify_csv.ipynb - Data deduplication.

EDA.ipynb - Data Augmentation with Entity swapping and Easy Data Augmentation algorithms.

RE_generator.ipynb - Data Augmentation with Seq2Seq model(KoBART). Generating Sentences when Entities and relation is given.

ensemble.ipynb - Ensemble with Soft Voting.

USAGE

1. Data Generation

  • Before Data Generation:
dataset
├──train/
|   └──train.csv
└──test/
    └──test.csv
  • Run all of below jupyter notebook to generate datasets
 python modify_csv.ipynb
 python EDA.ipynb
  • After Data Generation:
dataset
├──train/
|   ├──addDataset.csv
|   └──train.csv
└──test/
    └──test.csv

new_dataset
├──dev.csv
└──train.csv

2. Model Training

 python train.py \
    --batch_size  40\
    --weight_decay  0.01 \
    --learning_rate  0.00001 \
    --entity_marker  True

3. Inference

 python inference.py
  • Running the line above will generate submission.csv in prediction folder as below
prediction/
├──.ipynb_checkpoints/
├──sample_submission.csv
└──submission.csv