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# HPD-TinyBERT-F128
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This repository contains the pre-trained models for our paper [Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation](https://arxiv.org/abs/2203.07687).
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Then you can use our model for **encoding sentences into embeddings**
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```python
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sentences = ['He plays guitar.', 'A street vendor is outside.'
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sentence_embeddings = model.encode(sentences)
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for sentence, embedding in zip(sentences, sentence_embeddings):
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We evaluate our model on semantic textual similarity (STS) tasks. The results are:
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+-------+-------+-------+-------+-------+--------------+-----------------+-------+
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| STS12 | STS13 | STS14 | STS15 | STS16 | STSBenchmark | SICKRelatedness | Avg. |
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| 74.29 | 83.05 | 78.80 | 84.62 | 81.17 | 84.36 | 80.83 | 81.02 |
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```
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## Training
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---
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license: apache-2.0
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---
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# HPD-TinyBERT-F128
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This repository contains the pre-trained models for our paper [Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation](https://arxiv.org/abs/2203.07687).
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Then you can use our model for **encoding sentences into embeddings**
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```python
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sentences = ['He plays guitar.', 'A street vendor is outside.']
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sentence_embeddings = model.encode(sentences)
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for sentence, embedding in zip(sentences, sentence_embeddings):
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We evaluate our model on semantic textual similarity (STS) tasks. The results are:
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| STS12 | STS13 | STS14 | STS15 | STS16 | STSBenchmark | SICKRelatedness | Avg. |
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|-------|-------|-------|-------|-------|--------------|-----------------|-------|
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| 74.29 | 83.05 | 78.80 | 84.62 | 81.17 | 84.36 | 80.83 | 81.02 |
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## Training
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