---
tags:
- roberta
- adapterhub:nli/multinli
- adapter-transformers
license: apache-2.0
language:
- en
library_name: adapter-transformers
---

# Adapter `yoh/distilroberta-base-sept-adapter` for distilroberta-base

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)).

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.

## Usage

First, install `adapter-transformers` and `sentence-transformers`:

```
pip install -U adapter-transformers sentence-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_

Now, the adapter can be loaded and activated like this:

```python
from sentence_transformers import SentenceTransformer, models

# Load pre-trained model
word_embedding_model = models.Transformer("distilroberta-base")
# Load and activate adapter
word_embedding_model.auto_model.load_adapter("yoh/distilroberta-base-sept-adapter", source="hf", set_active=True)
# Create sentence transformer
pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), pooling_mode='mean')
model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
```

## Architecture & Training

See this [paper](https://arxiv.org/abs/2311.00408)

## Evaluation results

See this [paper](https://arxiv.org/abs/2311.00408)

## Citation
```bibtex
@article{huang2023adasent,
 title={AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot Classification}, 
 author={Yongxin Huang and Kexin Wang and Sourav Dutta and Raj Nath Patel and Goran Glavaš and Iryna Gurevych},
 journal = {ArXiv preprint},
 url = {https://arxiv.org/abs/2311.00408},
 volume = {abs/2311.00408},
 year={2023},
}
```