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--- |
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inference: false |
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tags: |
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- onnx |
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- text-classification |
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- adapterhub:rc/multirc |
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- roberta |
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- adapter-transformers |
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language: |
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- en |
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--- |
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# ONNX export of Adapter `AdapterHub/roberta-base-pf-multirc` for roberta-base |
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## Conversion of [AdapterHub/roberta-base-pf-multirc](https://huggingface.co/AdapterHub/roberta-base-pf-multirc) for UKP SQuARE |
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## Usage |
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```python |
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onnx_path = hf_hub_download(repo_id='UKP-SQuARE/roberta-base-pf-multirc-onnx', filename='model.onnx') # or model_quant.onnx for quantization |
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onnx_model = InferenceSession(onnx_path, providers=['CPUExecutionProvider']) |
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context = 'ONNX is an open format to represent models. The benefits of using ONNX include interoperability of frameworks and hardware optimization.' |
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question = 'What are advantages of ONNX?' |
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choices = ["Cat", "Horse", "Tiger", "Fish"]tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/roberta-base-pf-multirc-onnx') |
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raw_input = [[context, question + + choice] for choice in choices] |
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inputs = tokenizer(raw_input, padding=True, truncation=True, return_tensors="np") |
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inputs['token_type_ids'] = np.expand_dims(inputs['token_type_ids'], axis=0) |
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inputs['input_ids'] = np.expand_dims(inputs['input_ids'], axis=0) |
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inputs['attention_mask'] = np.expand_dims(inputs['attention_mask'], axis=0) |
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outputs = onnx_model.run(input_feed=dict(inputs), output_names=None) |
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``` |
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## Architecture & Training |
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The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. |
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In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). |
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## Evaluation results |
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Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. |
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## Citation |
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If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): |
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```bibtex |
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@inproceedings{poth-etal-2021-pre, |
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title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection", |
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author = {Poth, Clifton and |
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Pfeiffer, Jonas and |
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R{"u}ckl{'e}, Andreas and |
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Gurevych, Iryna}, |
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booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", |
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month = nov, |
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year = "2021", |
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address = "Online and Punta Cana, Dominican Republic", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.emnlp-main.827", |
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pages = "10585--10605", |
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} |
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``` |
