HPD-MiniLM-F128

This repository contains the pre-trained models for our paper Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation. The sentence embedding model contains only 23M parameters and the model size is only 87MB.

Overview

We propose Homomorphic Projective Distillation (HPD) to learn compressed sentence embeddings. Our method augments a small Transformer encoder model with learnable projection layers to produce compact representations while mimicking a large pre-trained language model to retain the sentence representation quality.

Details

This is a sentence-transformers model: It maps sentences & paragraphs to a 128 dimensional dense vector space and can be used for tasks like clustering or semantic search.

The teacher model is princeton-nlp/sup-simcse-roberta-large and the student model is nreimers/MiniLM-L6-H384-uncased.

Usage

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

After installing the package, you can simply load our model

from sentence_transformers import SentenceTransformer
model = SentenceTransformer('Xuandong/HPD-MiniLM-F128')

Then you can use our model for encoding sentences into embeddings

sentences = ['He plays guitar.', 'A street vendor is outside.']
sentence_embeddings = model.encode(sentences)

for sentence, embedding in zip(sentences, sentence_embeddings):
    print("Sentence:", sentence)
    print("Embedding:", embedding)
    print("")

Evaluation Results

We evaluate our model on semantic textual similarity (STS) tasks. The results are:

Training

Please refer to the github repo (https://github.com/XuandongZhao/HPD) for the details about the training.

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Dense({'in_features': 384, 'out_features': 128, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)

Citation

Please cite our paper if you use HPD in your work:

@article{zhao2022compressing,
  title={Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation},
  author={Zhao, Xuandong and Yu, Zhiguo and Wu, Ming and Li, Lei},
  journal={arXiv preprint arXiv:2203.07687},
  year={2022}
}