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Rotom

Code for the paper "Rotom: A Meta-Learned Data Augmentation Framework for Entity Matching, Data Cleaning, Text Classification, and Beyond"

Requirements

  • Python 3.7.7
  • PyTorch 1.4.0
  • Transformers 3.1.0
  • NLTK (stopwords, wordnet)
  • NVIDIA Apex (fp16 training)

Install required packages

pip install -r requirements.txt
git clone https://github.com/NVIDIA/apex.git
pip install -v --disable-pip-version-check --no-cache-dir ./apex

Install Snippext:

git clone -b v1.0 https://github.com/rit-git/Snippext_public.git
cp -r Snippext_public/snippext .

Model Training

To train a model with Rotom:

CUDA_VISIBLE_DEVICES=0 python train_any.py \
  --task em_DBLP-ACM \
  --size 300 \
  --logdir results_em/ \
  --finetuning \
  --batch_size 32 \
  --lr 3e-5 \
  --n_epochs 20 \
  --max_len 128 \
  --fp16 \
  --lm roberta \
  --da auto_filter_weight \
  --balance \
  --run_id 0

The current version supports 3 tasks: entity matching (EM), error detection (EDT), and text classification (TextCLS). The supported tasks are:

Type Dataset Names taskname pattern
EM Abt-Buy, Amazon-Google, DBLP-ACM, DBLP-GoogleScholar, Walmart-Amazon em_{dataset}, e.g., em_DBLP-ACM
EDT beers, hospital, movies, rayyan, tax cleaning_{dataset}, e.g., cleaning_beers
TextCLS AG, AMAZON2, AMAZON5, ATIS, IMDB, SNIPS, SST-2, SST-5, TREC textcls_{dataset}, e.g., textcls_AG
TextCLS, splits from Hu et al. IMDB, SST-5, TREC compare1_{dataset}, e.g., compare1_IMDB
TextCLS, splits from Kumar et al. ATIS, SST-2, TREC compare2_{dataset}, e.g., compare2_ATIS

Parameters:

  • --task: the taskname pattern specified following the above table
  • --size: the dataset size (optional). If not specified, the entire dataset will be used. The size ranges are {300, 450, 600, 750} for EM, {50, 100, 150, 200} For EDT, and {100, 300, 500} for TextCLS
  • --logdir: the path for TensorBoard logging (F1, acc, precision, and recall)
  • --finetuning: always keep this flag on
  • --batch_size, --lr, --max_len, --n_epochs: the batch size, learning rate, max sequence length, and the number of epochs for model training
  • --fp16: whether to use half-precision for training
  • --lm: the language model to fine-tune. We currently support bert, distilbert, and roberta
  • --balance: a special option for binary classification (EM and EDT) with skewed labels (#positive labels >> #negative labels). If this flag is on, then the training process will up-sample the positive labels
  • --warmup: (new) if this flag is on with SSL, then first warm up the model by training it on labeled data only before running SSL. Only support EM for now.
  • --run_id: the integer ID of the run e.g., {0, 1, 2, ...}
  • --da: the data augmentation method (See table below)
Method Operator Name(s)
No DA (simply LM fine-tuning) None
Regular transformation-based DA ['del', 'drop_col', 'append_col', 'swap', 'ins'] for EM/EDT
['del', 'token_del_tfidf', 'token_del', 'shuffle', 'token_repl', 'token_repl_tfidf'] for TextCLS
Inversed DA (InvDA) t5 / invda
Rotom (w/o semi-supervised learning) auto_filter_weight_no_ssl
Rotom (w. semi-supervised learning) auto_filter_weight

For the invda fine-tuning, see invda/README.md.

Experiment scripts

All experiment scripts are available in scripts/. To run the experiments for a task (em, cleaning, or textcls):

python scripts/run_all_em.py