---
license: apache-2.0
---
# **m**utual **i**nformation **C**ontrastive **S**entence **E**mbedding (**miCSE**):
[![arXiv](https://img.shields.io/badge/arXiv-2109.05105-29d634.svg)](https://arxiv.org/abs/2211.04928)
Language model of the pre-print arXiv paper titled: "_**miCSE**: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings_" 



The **miCSE** language model is trained for sentence similarity computation. Training the model imposes alignment between the attention pattern of different views (embeddings of augmentations) during contrastive learning. Learning sentence embeddings with **miCSE** entails enforcing the syntactic consistency across augmented views for every single sentence, making contrastive self-supervised learning more sample efficient. Sentence representations correspond to the embedding of the _**[CLS]**_ token.


# Model Usage

```shell
from transformers import AutoTokenizer, AutoModel
import torch.nn as nn

tokenizer = AutoTokenizer.from_pretrained("sap-ai-research/miCSE")

model = AutoModel.from_pretrained("sap-ai-research/miCSE")


# Encoding of sentences in a list with a predefined maximum lengths of tokens (max_length)

max_length = 32

sentences = [
    "This is a sentence for testing miCSE.", 
    "This is yet another test sentence for the mutual information Contrastive Sentence Embeddings model."
]

batch = tokenizer.batch_encode_plus(
                sentences,
                return_tensors='pt',
                padding=True,
                max_length=max_length,
                truncation=True
            )

# Compute the embeddings and keep only the _**[CLS]**_ embedding (the first token)

outputs = model(**batch, output_hidden_states=True, return_dict=True)

embeddings = outputs.last_hidden_state[:,0]

# Define similarity metric, e.g., cosine similarity

sim = nn.CosineSimilarity(dim=-1)

# Compute similarity between the **first** and the **second** sentence

cos_sim = sim(embeddings.unsqueeze(1),
             embeddings.unsqueeze(0))
             
print(f"Distance: {cos_sim[0,1].detach().item()}")

```


# Benchmark

Model results on SentEval Benchmark:
```shell
+-------+-------+-------+-------+-------+--------------+-----------------+--------+                                               
| STS12 | STS13 | STS14 | STS15 | STS16 | STSBenchmark | SICKRelatedness | S.Avg. |                                               
+-------+-------+-------+-------+-------+--------------+-----------------+--------+                                               
| 71.71 | 83.09 | 75.46 | 83.13 | 80.22 |    79.70     |      73.62      | 78.13  |                                               
+-------+-------+-------+-------+-------+--------------+-----------------+--------+  
```


## Citations
If you use this code in your research or want to refer to our work, please cite:

```
@article{Klein2022miCSEMI,
  title={miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings},
  author={Tassilo Klein and Moin Nabi},
  journal={ArXiv},
  year={2022},
  volume={abs/2211.04928}
}
```

#### Authors:
 - [Tassilo Klein](https://tjklein.github.io/)
 - [Moin Nabi](https://moinnabi.github.io/)