license: apache-2.0
HPD-TinyBERT-F128
This repository contains the pre-trained models for our paper Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation.
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/TinyBERT_L-4_H-312_v2
.
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-TinyBERT-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:
STS12 | STS13 | STS14 | STS15 | STS16 | STS-B | SICK-R | Avg. |
---|---|---|---|---|---|---|---|
74.29 | 83.05 | 78.80 | 84.62 | 81.17 | 84.36 | 80.83 | 81.02 |
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': 312, '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': 312, '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}
}