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.