metadata
tags:
- text-classification
- adapter-transformers
- adapterhub:sentiment/rotten_tomatoes
- distilbert
datasets:
- rotten_tomatoes
license: apache-2.0
Adapter distilbert-base-uncased_sentiment_rotten_tomatoes_pfeiffer for distilbert-base-uncased
Adapter for distilbert-base-uncased in Pfeiffer architecture trained on the Rotten Tomatoes dataset for 15 epochs with early stopping and a learning rate of 1e-4.
This adapter was created for usage with the Adapters library.
Usage
First, install adapters:
pip install -U adapters
Now, the adapter can be loaded and activated like this:
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("distilbert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/distilbert-base-uncased_sentiment_rotten_tomatoes_pfeiffer")
model.set_active_adapters(adapter_name)
Architecture & Training
- Adapter architecture: pfeiffer
- Prediction head: classification
- Dataset: Rotten Tomatoes Movie Reviews
Author Information
- Author name(s): Clifton Poth
- Author email: calpt@mail.de
- Author links: Website, GitHub, Twitter
Citation
This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/distilbert-base-uncased_sentiment_rotten_tomatoes_pfeiffer.yaml.