jsrimr / kobert-nsmc

Naver movie review sentiment classification with KoBERT

KoBERT-nsmc

• sentiment classification using KoBERT
• used 🤗Huggingface Tranformers🤗 library
• for more explanation, please visit https://medium.com/@jsrimr2/classifying-movie-reviews-in-your-language-using-hugging-face-model-9197eaea669d

Branches

• using alternative model(kykim_model) : features/kykim_model
• using alternative preprocessing method : features/preprocessing

Dependencies

• torch==1.4.0
• transformers==2.10.0

How to use

To check the base flow, Please refer to this Train_and_Eval.ipynb

Also, you can check basic hugging-face usage in this HuggingFace Practice.ipynb

Usage

$ python3 main.py --model_type kobert --do_train --do_eval

Prediction

$ python3 predict.py --input_file {INPUT_FILE_PATH} --output_file {OUTPUT_FILE_PATH} --model_dir {SAVED_CKPT_PATH}

Results

you can check visulized results on https://app.neptune.ai/jeffrey/nsmc/experiments?compare=EwBgNAzGCMQ&split=tbl&dash=charts&viewId=standard-view&sortBy=%5B%22sys%2Fcreation_time%22%5D&sortFieldType=%5B%22datetime%22%5D&sortFieldAggregationMode=%5B%22auto%22%5D&sortDirection=%5B%22descending%22%5D&suggestionsEnabled=false&lbViewUnpacked=true

	Accuracy (%)
KoBERT	89.63

References

• KoBERT
• Huggingface Transformers
• NSMC dataset