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This repository contains the code and pre-trained models to replicate the paper SimCSE: Simple Contrastive Learning of Sentence Embeddings. The code reused most of the original code from the authors.

The SimCSE paper proposes a simple contrastive learning framework that works with both unlabeled and labeled data. Unsupervised SimCSE simply takes an input sentence and predicts itself in a contrastive learning framework, with only standard dropout used as noise. Supervised SimCSE incorporates annotated pairs from NLI datasets into contrastive learning by using entailment pairs as positives and contradiction pairs as hard negatives. The following figure is an illustration of the models from the paper.

data/ contains the scripts to download the data.

exp_scripts/ contains the scripts to run experiments.

convert_anli_to_csv.py contains the extra code to process the ANLI data.

train.py file is modified for us to reproduce the experiments.

Below is a list of scripts in exp_scripts:

• run_exp.sh is the general run experiment file. This makes scripts execute in background and print logs into logs/.

• eval_hf_pretrained.sh runs evaluation on all the available supervised models (sup-simcse-bert-base-uncased, sup-simcse-bert-large-uncased, sup-simcse-roberta-base, sup-simcse-roberta-large) that are made available on Huggingface by the authors.

The authors provided their models on Huggingface. To use their models, run the following to install their pacakage.

python setup.py install

Note that if you want to enable GPU encoding, you should install the correct version of PyTorch that supports CUDA. See PyTorch official website for instructions.

After installing the package, one can load the model by code below:

from simcse import SimCSE
model = SimCSE("princeton-nlp/sup-simcse-bert-base-uncased")

See model list for a full list of available models.

The authors' released models are listed as following. You can import these models by using the simcse package or using HuggingFace's Transformers.

Naming rules: unsup and sup represent "unsupervised" (trained on Wikipedia corpus) and "supervised" (trained on NLI datasets) respectively.

First, install PyTorch by following the instructions from the official website. To faithfully reproduce the results, please use the correct 1.7.1 version corresponding to your platforms/CUDA versions. PyTorch version higher than 1.7.1 should also work. For example, if you use Linux and CUDA11 (how to check CUDA version), install PyTorch by the following command,

pip install torch==1.7.1+cu110 -f https://download.pytorch.org/whl/torch_stable.html

If you instead use CUDA <11 or CPU, install PyTorch by the following command,

pip install torch==1.7.1

Then run the following script to install the remaining dependencies,

pip install -r requirements.txt

The evaluation code for sentence embeddings is based on a modified version of SentEval. It evaluates sentence embeddings on semantic textual similarity (STS) tasks and downstream transfer tasks. For STS tasks, the evaluation takes the "all" setting, and report Spearman's correlation. See the original paper (Appendix B) for evaluation details.

Before evaluation, please download the evaluation datasets by running

cd SentEval/data/downstream/
bash download_dataset.sh

Then come back to the root directory, you can evaluate any transformers-based pre-trained models using the evaluation code. For example,

python evaluation.py \
    --model_name_or_path princeton-nlp/sup-simcse-bert-base-uncased \
    --pooler cls \
    --task_set sts \
    --mode test

which is expected to output the results in a tabular format:

------ test ------
+-------+-------+-------+-------+-------+--------------+-----------------+-------+
| STS12 | STS13 | STS14 | STS15 | STS16 | STSBenchmark | SICKRelatedness |  Avg. |
+-------+-------+-------+-------+-------+--------------+-----------------+-------+
| 75.30 | 84.67 | 80.19 | 85.40 | 80.82 |    84.26     |      80.39      | 81.58 |
+-------+-------+-------+-------+-------+--------------+-----------------+-------+

Arguments for the evaluation script are as follows,

• --model_name_or_path: The name or path of a transformers-based pre-trained checkpoint. You can directly use the models in the above table, e.g., princeton-nlp/sup-simcse-bert-base-uncased.
• --pooler: Pooling method. Now support
  • cls (default): Use the representation of [CLS] token. A linear+activation layer is applied after the representation (it's in the standard BERT implementation). If you use supervised SimCSE, you should use this option.
  • cls_before_pooler: Use the representation of [CLS] token without the extra linear+activation. If you use unsupervised SimCSE, you should take this option.
  • avg: Average embeddings of the last layer. If you use checkpoints of SBERT/SRoBERTa (paper), you should use this option.
  • avg_top2: Average embeddings of the last two layers.
  • avg_first_last: Average embeddings of the first and last layers. If you use vanilla BERT or RoBERTa, this works the best.
• --mode: Evaluation mode
  • test (default): The default test mode. To faithfully reproduce the results, you should use this option.
  • dev: Report the development set results. Note that in STS tasks, only STS-B and SICK-R have development sets, so only their numbers are reported. It also takes a fast mode for transfer tasks, so the running time is much shorter than the test mode (though numbers are slightly lower).
  • fasttest: It is the same as test, but with a fast mode so the running time is much shorter, but the reported numbers may be lower (only for transfer tasks).
• --task_set: What set of tasks to evaluate on (if set, it will override --tasks)
  • sts (default): Evaluate on STS tasks, including STS 12~16, STS-B and SICK-R. This is the most commonly-used set of tasks to evaluate the quality of sentence embeddings.
  • transfer: Evaluate on transfer tasks.
  • full: Evaluate on both STS and transfer tasks.
  • na: Manually set tasks by --tasks.
• --tasks: Specify which dataset(s) to evaluate on. Will be overridden if --task_set is not na. See the code for a full list of tasks.

Data

Run data/download_wiki.sh and data/download_nli.sh to download the two datasets that were used for supervised and unsupervised SimCSE.

Training scripts

The authors provide example training scripts for both unsupervised and supervised SimCSE. run_unsup_example.sh provide a single-GPU (or CPU) example for the unsupervised version, and run_sup_example.sh gives a multiple-GPU example for the supervised version. Both scripts call train.py for training. The arguments are the following:

• --train_file: Training file path. It can be either "txt" files (one line for one sentence) or "csv" files (2-column: pair data with no hard negative; 3-column: pair data with one corresponding hard negative instance). You can use our provided Wikipedia or NLI data, or you can use your own data with the same format.
• --model_name_or_path: Pre-trained checkpoints to start with. For now BERT-based models (bert-base-uncased, bert-large-uncased, etc.) and RoBERTa-based models (RoBERTa-base, RoBERTa-large, etc.) are supported.
• --temp: Temperature for the contrastive loss.
• --pooler_type: Pooling method. It's the same as the --pooler_type in the evaluation part.
• --mlp_only_train: For unsupervised SimCSE, it works better to train the model with MLP layer but test the model without it. You should use this argument when training unsupervised SimCSE models.
• --hard_negative_weight: If using hard negatives (i.e., there are 3 columns in the training file), this is the logarithm of the weight. For example, if the weight is 1, then this argument should be set as 0 (default value).
• --do_mlm: Whether to use the MLM auxiliary objective. If True:
  • --mlm_weight: Weight for the MLM objective.
  • --mlm_probability: Masking rate for the MLM objective.

Hyperparameters

The following hyperparamters are used for training SimCSE: