https://github.com/EhimeNLP/AcademicRoBERTa

https://github.com/EhimeNLP/AcademicRoBERTa

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• Host: GitHub
• URL: https://github.com/EhimeNLP/AcademicRoBERTa
• Owner: EhimeNLP
• Created: 2022-09-05T06:58:56.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-05-16T04:48:22.000Z (over 1 year ago)
• Last Synced: 2024-08-03T21:03:05.871Z (5 months ago)
• Language: Python
• Size: 705 KB
• Stars: 8
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-japanese-llm - AcademicRoBERTa

README

        
# AcademicRoBERTa

We pretrained a RoBERTa-based Japanese masked language model on paper abstracts from the academic database CiNii Articles.  

[A Japanese Masked Language Model for Academic Domain](https://aclanthology.org/2022.sdp-1.16/)

## Download

They include a pretrained roberta model (700000_model.pt), a sentencepiece model (sp.model) , a dictionary (dict.txt) and code for applying sentencepiece (apply-sp.py) .

```

wget http://aiweb.cs.ehime-u.ac.jp/~yamauchi/academic_model/Academic_RoBERTa_base.tar.gz

```

## Hugging Face

https://huggingface.co/EhimeNLP/AcademicRoBERTa

## Requirements

Python >= 3.8 


[fairseq](https://github.com/facebookresearch/fairseq) == 0.12.2 (In working order)


[sentencepiece](https://github.com/google/sentencepiece) 


tensorboardX (optional) 


## Preprocess

We applied SentencePiece for subword segmentation. 


Prepare datasets ($TRAIN_SRC, ...), which format assumes a tab delimiter between text and label.

```

python ./apply_sp.py $TRAIN_SRC $DATASET_DIR/train.src-tgt -bpe_model $SENTENCEPIECE_MODEL

python ./apply_sp.py $VALID_SRC $DATASET_DIR/valid.src-tgt -bpe_model $SENTENCEPIECE_MODEL

python ./apply_sp.py $TEST_SRC $DATASET_DIR/test.src-tgt -bpe_model $SENTENCEPIECE_MODEL

```

```

fairseq-preprocess \

    --source-lang "src" \

    --target-lang "tgt" \

    --trainpref "${DATASET_DIR}/train.src-tgt" \

    --validpref "${DATASET_DIR}/valid.src-tgt" \

    --testpref "${DATASET_DIR}/test.src-tgt" \

    --destdir "data-bin/" \

    --workers 60 \

    --srcdict ${DICT} \

    --tgtdict ${DICT}

```

## Finetune

This work was supported by papers in Japanese. 


The procedure for sentence classification using AcademicRoBERTa is as follows.

```

fairseq-train data-bin/ \

    --restore-file $ROBERTA_PATH \

    --max-positions 512 \

    --batch-size $MAX_SENTENCES \

    --task sentence_prediction \

    --reset-optimizer --reset-dataloader --reset-meters \

    --required-batch-size-multiple 1 \

    --init-token 0 --separator-token 2 \

    --arch roberta_base \

    --criterion sentence_prediction \

    --classification-head-name $HEAD_NAME \

    --num-classes $NUM_CLASSES \

    --dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \

    --lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \

    --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-08 \

    --clip-norm 0.0 \

    --max-epoch 999 \

    --patience 10 \

    --no-epoch-checkpoints --seed 88 --log-format simple --log-interval $LOG_INTERVAL --save-interval-updates $SAVE_INTERVAL \

    --best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \

    --shorten-method "truncate" \

    --find-unused-parameters

```

## Reference

- Hiroki Yamauchi, Tomoyuki Kajiwara, Marie Katsurai, Ikki Ohmukai, Takashi Ninomiya. 
 

  [A Japanese Masked Language Model for Academic Domain](https://aclanthology.org/2022.sdp-1.16). 
 

  In Proceedings of the Third Workshop on Scholarly Document Processing (SDP), pp.152-157, 2022.

- 山内洋輝, 梶原智之, 桂井麻里衣, 大向一輝, 二宮崇. 
 

  [学術ドメインに特化した日本語事前訓練モデルの構築](https://www.anlp.jp/proceedings/annual_meeting/2023/pdf_dir/Q11-4.pdf). 
 

  言語処理学会第29回年次大会, pp.2842-2846, 2023.

```

@inproceedings{yamauchi-etal-2022-japanese,

    title = "A {J}apanese Masked Language Model for Academic Domain",

    author = "Yamauchi, Hiroki  and

      Kajiwara, Tomoyuki  and

      Katsurai, Marie  and

      Ohmukai, Ikki  and

      Ninomiya, Takashi",

    booktitle = "Proceedings of the Third Workshop on Scholarly Document Processing",

    year = "2022",

    url = "https://aclanthology.org/2022.sdp-1.16",

    pages = "152--157",

}

```

license: apache-2.0