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  - roberta
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  - adapterhub:nli/multinli
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  - adapter-transformers
 
 
 
 
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  ---
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  # Adapter `yoh/distilroberta-base-sept-adapter` for distilroberta-base
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- An [adapter](https://adapterhub.ml) for the `distilroberta-base` model that was trained on the [nli/multinli](https://adapterhub.ml/explore/nli/multinli/) dataset.
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- This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
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  ## Usage
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  <!-- Add some description here -->
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  ## Citation
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-
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- <!-- Add some description here -->
 
 
 
 
 
 
 
 
 
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  - roberta
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  - adapterhub:nli/multinli
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  - adapter-transformers
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+ license: apache-2.0
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+ language:
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+ - en
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+ library_name: adapter-transformers
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  ---
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  # Adapter `yoh/distilroberta-base-sept-adapter` for distilroberta-base
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+ An [adapter](https://adapterhub.ml) for the `distilroberta-base` model that was trained on the [AllNLI](https://public.ukp.informatik.tu-darmstadt.de/reimers/sentence-transformers/datasets/paraphrases/AllNLI.jsonl.gz), [Sentence compression](https://public.ukp.informatik.tu-darmstadt.de/reimers/sentence-transformers/datasets/paraphrases/sentence-compression.jsonl.gz) and [Stackexchange duplicate question](https://public.ukp.informatik.tu-darmstadt.de/reimers/sentence-transformers/datasets/paraphrases/stackexchange_duplicate_questions.jsonl.gz) datasets (see information [here](https://www.sbert.net/examples/training/paraphrases/README.html)).
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+ This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library. See this [paper](https://arxiv.org/abs/2311.00408) and [repository](https://github.com/UKPLab/AdaSent/blob/main/README.md) for more information on the tasks.
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  ## Usage
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  <!-- Add some description here -->
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  ## Citation
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+ ```bibtex
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+ @article{huang2023adasent,
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+ title={AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot Classification},
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+ author={Yongxin Huang and Kexin Wang and Sourav Dutta and Raj Nath Patel and Goran Glavaš and Iryna Gurevych},
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+ journal = {ArXiv preprint},
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+ url = {https://arxiv.org/abs/2311.00408},
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+ volume = {abs/2311.00408},
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+ year={2023},
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+ }
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+ ```