Instructions to use jgrc3/seqBN_adapter_classification_noPre with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use jgrc3/seqBN_adapter_classification_noPre with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("roberta-base") model.load_adapter("jgrc3/seqBN_adapter_classification_noPre", set_active=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from jgrc3/seqBN_adapter_classification_noPre: direct link, hf CLI and curl.
- Browser
- Download file 1.03 kB
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https://huggingface.co/jgrc3/seqBN_adapter_classification_noPre/resolve/main/README.md
- Command line
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hf download hf://jgrc3/seqBN_adapter_classification_noPre/README.md
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curl -L -o README.md https://huggingface.co/jgrc3/seqBN_adapter_classification_noPre/resolve/main/README.md
1.03 kB
| tags: | |
| - roberta | |
| - adapter-transformers | |
| datasets: | |
| - BigTMiami/amazon_helpfulness | |
| # Adapter `jgrc3/seqBN_adapter_classification_noPre` for roberta-base | |
| An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/amazon_helpfulness](https://huggingface.co/datasets/BigTMiami/amazon_helpfulness/) dataset and includes a prediction head for classification. | |
| This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library. | |
| ## Usage | |
| First, install `adapters`: | |
| ``` | |
| pip install -U adapters | |
| ``` | |
| Now, the adapter can be loaded and activated like this: | |
| ```python | |
| from adapters import AutoAdapterModel | |
| model = AutoAdapterModel.from_pretrained("roberta-base") | |
| adapter_name = model.load_adapter("jgrc3/seqBN_adapter_classification_noPre", source="hf", set_active=True) | |
| ``` | |
| ## Architecture & Training | |
| <!-- Add some description here --> | |
| ## Evaluation results | |
| <!-- Add some description here --> | |
| ## Citation | |
| <!-- Add some description here --> |