Instructions to use jgrc3/pfeiffer_adapter_classification_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use jgrc3/pfeiffer_adapter_classification_trained with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") model.load_adapter("jgrc3/pfeiffer_adapter_classification_trained", set_active=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from jgrc3/pfeiffer_adapter_classification_trained: direct link, hf CLI and curl.
- Browser
- Download file 1.04 kB
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https://huggingface.co/jgrc3/pfeiffer_adapter_classification_trained/resolve/main/README.md
- Command line
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hf download hf://jgrc3/pfeiffer_adapter_classification_trained/README.md
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curl -L -o README.md https://huggingface.co/jgrc3/pfeiffer_adapter_classification_trained/resolve/main/README.md
1.04 kB
metadata
tags:
- roberta
- adapter-transformers
datasets:
- BigTMiami/amazon_helpfulness
Adapter jgrc3/pfeiffer_adapter_classification_trained for roberta-base
An adapter for the roberta-base model that was trained on the BigTMiami/amazon_helpfulness dataset and includes a prediction head for classification.
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("roberta-base")
adapter_name = model.load_adapter("jgrc3/pfeiffer_adapter_classification_trained", source="hf", set_active=True)