Instructions to use AdapterHub/bert-base-uncased-pf-boolq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use AdapterHub/bert-base-uncased-pf-boolq with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("bert-base-uncased") model.load_adapter("AdapterHub/bert-base-uncased-pf-boolq", set_active=True) - Notebooks
- Google Colab
- Kaggle
Download head_config.json from AdapterHub/bert-base-uncased-pf-boolq: direct link, hf CLI and curl.
- Browser
- Download file 382 Bytes
-
https://huggingface.co/AdapterHub/bert-base-uncased-pf-boolq/resolve/main/head_config.json
- Command line
-
hf download hf://AdapterHub/bert-base-uncased-pf-boolq/head_config.json
-
curl -L -o head_config.json https://huggingface.co/AdapterHub/bert-base-uncased-pf-boolq/resolve/main/head_config.json
382 Bytes
| { | |
| "config": { | |
| "activation_function": "tanh", | |
| "bias": true, | |
| "head_type": "classification", | |
| "label2id": { | |
| "False": 0, | |
| "True": 1 | |
| }, | |
| "layers": 2, | |
| "num_labels": 2, | |
| "use_pooler": false | |
| }, | |
| "hidden_size": 768, | |
| "model_class": "BertModelWithHeads", | |
| "model_name": "bert-base-uncased", | |
| "model_type": "bert", | |
| "name": "super_glue_boolq" | |
| } |