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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.
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# Benchmark
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Model results on SentEval Benchmark:
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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.
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# Usage
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```shell
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tokenizer = AutoTokenizer.from_pretrained("sap-ai-research/<----Enter Model Name---->")
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model = AutoModelWithLMHead.from_pretrained("sap-ai-research/<----Enter Model Name---->")
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```
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# Benchmark
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Model results on SentEval Benchmark:
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