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README.md

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+---
+inference: false
+tags:
+- onnx
+- text-classification
+- bert
+- adapterhub:qa/boolq
+- adapter-transformers
+datasets:
+- boolq
+language:
+- en
+---
+
+# ONNX export of Adapter `AdapterHub/bert-base-uncased-pf-boolq` for bert-base-uncased
+## Conversion of [AdapterHub/bert-base-uncased-pf-boolq](https://huggingface.co/AdapterHub/bert-base-uncased-pf-boolq) for UKP SQuARE
+
+
+## Usage
+```python
+onnx_path = hf_hub_download(repo_id='UKP-SQuARE/bert-base-uncased-pf-boolq-onnx', filename='model.onnx') # or model_quant.onnx for quantization
+onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider'])
+
+context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.'
+question = 'What are advantages of ONNX?'
+tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/bert-base-uncased-pf-boolq-onnx')
+
+inputs = tokenizer(question, context, padding=True, truncation=True, return_tensors='np')
+inputs_int64 = {key: np.array(inputs[key], dtype=np.int64) for key in inputs}
+outputs = onnx_model.run(input_feed=dict(inputs_int64), output_names=None)
+```
+
+## Architecture & Training
+
+The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
+In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
+
+
+## Evaluation results
+
+Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
+
+## Citation
+
+If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
+
+```bibtex
+@inproceedings{poth-etal-2021-pre,
+    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
+    author = {Poth, Clifton  and
+      Pfeiffer, Jonas  and
+      R{"u}ckl{'e}, Andreas  and
+      Gurevych, Iryna},
+    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2021",
+    address = "Online and Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.emnlp-main.827",
+    pages = "10585--10605",
+}
+```