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README.md

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+---
+inference: false
+tags:
+- onnx
+- text-classification
+- adapterhub:rc/multirc
+- roberta
+- adapter-transformers
+language:
+- en
+---
+
+# ONNX export of Adapter `AdapterHub/roberta-base-pf-multirc` for roberta-base
+## Conversion of [AdapterHub/roberta-base-pf-multirc](https://huggingface.co/AdapterHub/roberta-base-pf-multirc) for UKP SQuARE
+
+
+## Usage
+```python
+onnx_path = hf_hub_download(repo_id='UKP-SQuARE/roberta-base-pf-multirc-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?'
+choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/roberta-base-pf-multirc-onnx')
+
+raw_input = [[context, question +  + choice] for choice in choices]
+inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np")
+inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0)
+inputs['input_ids'] =  np.expand_dims(inputs['input_ids'], axis=0)
+inputs['attention_mask'] =  np.expand_dims(inputs['attention_mask'], axis=0)
+outputs = onnx_model.run(input_feed=dict(inputs), 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",
+}
+```