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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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- bert |
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- adapterhub:qa/boolq |
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- adapter-transformers |
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datasets: |
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- boolq |
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language: |
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- en |
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--- |
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# ONNX export of Adapter `AdapterHub/bert-base-uncased-pf-boolq` for bert-base-uncased |
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## Conversion of [AdapterHub/bert-base-uncased-pf-boolq](https://huggingface.co/AdapterHub/bert-base-uncased-pf-boolq) 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/bert-base-uncased-pf-boolq-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 = 'English orthography typically represents vowel sounds with the five conventional vowel letters ⟨a, e, i, o, u⟩, as well as ⟨y⟩, which may also be a consonant depending on context. However, outside of abbreviations, there are a handful of words in English that do not have vowels, either because the vowel sounds are not written with vowel letters or because the words themselves are pronounced without vowel sounds'. |
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question = 'can there be a word without a vowel' |
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tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/bert-base-uncased-pf-boolq-onnx') |
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inputs = tokenizer(question, context, padding=True, truncation=True, return_tensors='np') |
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inputs = {key: np.array(inputs[key], dtype=np.int64) for key in inputs} |
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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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``` |
