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

An adapter for the distilroberta-base model that was trained on the AllNLI, Sentence compression and Stackexchange duplicate question datasets (see information here).

This adapter was created for usage with the adapter-transformers library. See this paper and repository 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

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

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

Evaluation results

See this paper

Citation

@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},
}