readme: ---
  license: apache-2.0
  ---
  # DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings 
  
  [![GitHub Stars](https://img.shields.io/github/stars/voidism/DiffCSE?style=social)](https://github.com/voidism/DiffCSE/)
  
  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/voidism/DiffCSE/blob/master/diffcse_evaluation.ipynb)
  
  arXiv link: https://arxiv.org/abs/2204.10298  
  To be published in [**NAACL 2022**](https://2022.naacl.org/)
  
  Authors:
  [Yung-Sung Chuang](https://people.csail.mit.edu/yungsung/), 
  [Rumen Dangovski](http://super-ms.mit.edu/rumen.html),
  [Hongyin Luo](http://people.csail.mit.edu/hyluo/),
  [Yang Zhang](https://mitibmwatsonailab.mit.edu/people/yang-zhang/),
  [Shiyu Chang](https://code-terminator.github.io/),
  [Marin Soljačić](http://www.mit.edu/~soljacic/marin.html),
  [Shang-Wen Li](https://swdanielli.github.io/),
  [Scott Wen-tau Yih](https://scottyih.org/),
  [Yoon Kim](https://people.csail.mit.edu/yoonkim/),
  [James Glass](http://groups.csail.mit.edu/sls/people/glass.shtml)
  
  
  Our code is mainly based on the code of [SimCSE](https://arxiv.org/abs/2104.08821). Please refer to their [repository](https://github.com/princeton-nlp/SimCSE) for more detailed information.
  
  ## Overview
  ![DiffCSE](https://github.com/voidism/DiffCSE/raw/master/diffcse.png)
  
  We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings. DiffCSE learns sentence embeddings that are sensitive to the difference between the original sentence and an edited sentence, where the edited sentence is obtained by stochastically masking out the original sentence and then sampling from a masked language model. We show that DiffSCE is an instance of equivariant contrastive learning [(Dangovski et al., 2021)](https://arxiv.org/abs/2111.00899), which generalizes contrastive learning and learns representations that are insensitive to certain types of augmentations and sensitive to other "harmful" types of augmentations. Our experiments show that DiffCSE achieves state-of-the-art results among unsupervised sentence representation learning methods, outperforming unsupervised SimCSE by 2.3 absolute points on semantic textual similarity tasks. 
  
  ## Setups
  
  [![Python](https://img.shields.io/badge/python-3.9.5-blue?logo=python&logoColor=FED643)](https://www.python.org/downloads/release/python-395/)
  
  ### Requirements
  * Python 3.9.5
  
  ### Install our customized Transformers package
  ```
  cd transformers-4.2.1
  pip install .
  ```
  > If you have already installed `transformers==4.2.1` through pip, you need to put `modeling_bert.py` into `<your_python_env>/site-packages/transformers/models/bert/modeling_bert.py` and `modeling_roberta.py` into `<your_python_env>/site-packages/transformers/models/bert/modeling_roberta.py`.
  > We modify these two files in the package so that we can perform _conditional_ pretraining tasks using BERT/RoBERTa. If possible, please directly pip install our customized Transformers package.
  
  ### Install other packages
  ```
  pip install -r requirements.txt
  ```
  
  ### Download the pretraining dataset
  ```
  cd data
  bash download_wiki.sh
  ```
  
  ### Download the downstream dataset
  ```
  cd SentEval/data/downstream/
  bash download_dataset.sh
  ```
  
  ## Training
  (The same as `run_diffcse.sh`.)
  ```bash
  python train.py \
      --model_name_or_path bert-base-uncased \
      --generator_name distilbert-base-uncased \
      --train_file data/wiki1m_for_simcse.txt \
      --output_dir <your_output_model_dir> \
      --num_train_epochs 2 \
      --per_device_train_batch_size 64 \
      --learning_rate 7e-6 \
      --max_seq_length 32 \
      --evaluation_strategy steps \
      --metric_for_best_model stsb_spearman \
      --load_best_model_at_end \
      --eval_steps 125 \
      --pooler_type cls \
      --mlp_only_train \
      --overwrite_output_dir \
      --logging_first_step \
      --logging_dir <your_logging_dir> \
      --temp 0.05 \
      --do_train \
      --do_eval \
      --batchnorm \
      --lambda_weight 0.005 \
      --fp16 --masking_ratio 0.30
  ```
  
  Our new arguments:
  * `--lambda_weight`: the lambda coefficient mentioned in Section 3 of our paper.
  * `--masking_ratio`: the masking ratio for MLM generator to randomly replace tokens.
  * `--generator_name`: the model name of generator. For `bert-base-uncased`, we use `distilbert-base-uncased`. For `roberta-base`, we use `distilroberta-base`.
  
  
  Arguments from [SimCSE](https://github.com/princeton-nlp/SimCSE):
  * `--train_file`: Training file path (`data/wiki1m_for_simcse.txt`). 
  * `--model_name_or_path`: Pre-trained checkpoints to start with such as BERT-based models (`bert-base-uncased`, `bert-large-uncased`, etc.) and RoBERTa-based models (`RoBERTa-base`, `RoBERTa-large`).
  * `--temp`: Temperature for the contrastive loss. We always use `0.05`.
  * `--pooler_type`: Pooling method.
  * `--mlp_only_train`: For unsupervised SimCSE or DiffCSE, it works better to train the model with MLP layer but test the model without it. You should use this argument wh