ZurichNLP
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unsup-simcse-xlm-roberta-base

Feature Extraction

text-embeddings-inference

Inference Endpoints

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jvamvas commited on May 22, 2023

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@@ -98,12 +98,12 @@ license: mit
 pipeline_tag: feature-extraction
 ---
-[xlm-roberta-base](https://huggingface.co/xlm-roberta-base) fine-tuned with [SimCSE](http://dx.doi.org/10.18653/v1/2021.emnlp-main.552) (Gao et al., EMNLP 2021).
-See a similar English model released by Gao et al.: https://huggingface.co/princeton-nlp/unsup-simcse-roberta-base
-Fine-tuning was done using the [reference implementation of SimCSE](https://github.com/princeton-nlp/SimCSE) and the 1M sentences from English Wikipedia released by the authors.
-As a sentence representation we use the average of the last hidden states (`pooler_type=avg`), which ensures compatibility to Sentence-BERT.
 Fine-tuning command:
 ```bash

 pipeline_tag: feature-extraction
 ---
+[xlm-roberta-base](https://huggingface.co/xlm-roberta-base) fine-tuned for sentence embeddings with [SimCSE](http://dx.doi.org/10.18653/v1/2021.emnlp-main.552) (Gao et al., EMNLP 2021).
+See a similar English model released by Gao et al.: https://huggingface.co/princeton-nlp/unsup-simcse-roberta-base.
+Fine-tuning was done using the [reference implementation of unsupervised SimCSE](https://github.com/princeton-nlp/SimCSE) and the 1M sentences from English Wikipedia released by the authors.
+As a sentence representation, we used the average of the last hidden states (`pooler_type=avg`), which is compatible with Sentence-BERT.
 Fine-tuning command:
 ```bash