Downloads · 30 days
4
29% of all-time downloads
bert-base-uncased_qa_squad2_pfeiffer
AdapterHub/bert-base-uncased_qa_squad2_pfeiffer
bert-base-uncased_qa_squad2_pfeiffer is a question answering model from AdapterHub. Use it when the input is a question plus a passage. It is set up for adapter-transformers. The card lists the license as apache-2.0.
Adapter for bert-base-uncased in Pfeiffer architecture trained on the SQuAD 2.0 dataset for 15 epochs with early stopping and a learning rate of 1e-4.
All-time downloads
14
Public
Repo size
3.6 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin3.6 MB · 100%
At a glance
- Task
- Question Answering
- Library
- adapter-transformers
- License
- apache-2.0
- Access
- Public
- Created
- May 5, 2024
- Updated
- May 5, 2024
- SHA
- 85bb1770
Try a prompt
Datasets
Model card
From the Hugging Face model README
Adapter bert-base-uncased_qa_squad2_pfeiffer for bert-base-uncased
Adapter for bert-base-uncased in Pfeiffer architecture trained on the SQuAD 2.0 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("bert-base-uncased")
adapter_name = model.load_adapter("AdapterHub/bert-base-uncased_qa_squad2_pfeiffer")
model.set_active_adapters(adapter_name)
Architecture & Training
- Adapter architecture: pfeiffer
- Prediction head: question answering
- Dataset: SQuAD 2.0
Author Information
- Author name(s): Clifton Poth
- Author email: [email protected]
- Author links: Website, GitHub, Twitter
Citation
This adapter has been auto-imported from https://github.com/Adapter-Hub/Hub/blob/master/adapters/ukp/bert-base-uncased_qa_squad2_pfeiffer.yaml.
- Task
- Question Answering
- Library
- adapter-transformers
- License
- apache-2.0
- Created
- May 5, 2024
- Updated
- May 5, 2024