SCT

Implementation of An Efficient Self-Supervised Cross-View Training For Sentence Embedding (TACL 2023).

Citation

@article{10.1162/tacl_a_00620,
    author = {Limkonchotiwat, Peerat and Ponwitayarat, Wuttikorn and Lowphansirikul, Lalita and Udomcharoenchaikit, Can and Chuangsuwanich, Ekapol and Nutanong, Sarana},
    title = "{An Efficient Self-Supervised Cross-View Training For Sentence Embedding}",
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {11},
    pages = {1572-1587},
    year = {2023},
    month = {12},
    issn = {2307-387X},
    doi = {10.1162/tacl_a_00620},
    url = {https://doi.org/10.1162/tacl\_a\_00620},
    eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00620/2196817/tacl\_a\_00620.pdf},
}

Installation

git clone https://github.com/mrpeerat/SCT
cd SCT
pip install -e .

Our models (Huggingface)

Self-supervised

Distillation

Usage

Training data

We use the training data from BSL's paper: here.

Development data

We use sts-b development set from sentence transformer.

Parameters

Self-supervised:

Models	Reference Temp	Student Temp	Queue Size	Learning Rate
BERT-Tiny	0.03	0.04	131072	5e-4
BERT-Mini	0.01	0.03	131072	3e-4
BERT-Small	0.02	0.03	65536	3e-4
BERT-Base	0.04	0.05	65536	5e-4
BERT-Large	0.04	0.05	16384	5e-4

Distillation:

Models	Reference Temp	Student Temp	Queue Size	Learning Rate
BERT-Tiny	0.03	0.04	131072	5e-4
BERT-Mini	0.04	0.05	65536	1e-4
BERT-Small	0.04	0.05	131072	1e-4
BERT-Base	0.04	0.05	65536	1e-4

Train your own model

Please set the model's parameter before training.

>> bash Running_distillation_script.sh
>> bash Running_script.sh

For finetuning model parameters:

learning_rate_all=(1e-4 3e-4 5e-4)
queue_sizes=(131072 65536 16384)
teacher_temps=(0.01 0.02 0.03 0.04 0.05 0.06 0.07)
student_temps=(0.01 0.02 0.03 0.04 0.05 0.06 0.07)

Evaluation

Our evaluation code for sentence embeddings is based on a modified version of SentEval and SimCSE.

Before evaluation, please download the evaluation datasets by running

cd SentEval
pip install -e .
cd data/downstream/
bash download_dataset.sh

Evaluation - Notebook

Please see this notebooks.

Evaluation - Python

python evaluation.py \
    --model_name_or_path "your-model-path" \
    --task_set sts \
    --mode test

Main results - STS

Self-supervised:

Models	STS (Avg.)
SCT-BERT-Tiny	69.73
SCT-BERT-Mini	69.59
SCT-BERT-Small	72.56
SCT-BERT-Base	75.55
SCT-BERT-Large	78.16

Distillation:

Models	STS (Avg.)
SCT-Distillation-BERT-Tiny	76.43
SCT-Distillation-BERT-Mini	77.58
SCT-Distillation-BERT-Small	78.16
SCT-Distillation-BERT-Base	79.58

Downstream tasks - Reranking and NLI

For the reranking evaluation code, we use USEB
For the NLI evaluation code, we use SentEval

Self-supervised:

Models	Reranking (Avg.)	NLI (Avg.)
SCT-BERT-Tiny	55.29	71.89
SCT-BERT-Small	58.59	75.70
SCT-BERT-Base	60.97	77.93
SCT-BERT-Large	63.02	79.55

Distillation:

Models	Reranking (Avg.)	NLI (Avg.)
SCT-Distillation-BERT-Tiny	61.14	78.53
SCT-Distillation-BERT-Small	61.94	80.44
SCT-Distillation-BERT-Base	64.63	80.97