metadata
inference: false
tags:
- onnx
- question-answering
- bert
- adapterhub:qa/squad2
- adapter-transformers
datasets:
- squad_v2
language:
- en
ONNX export of Adapter AdapterHub/bert-base-uncased-pf-squad_v2
for bert-base-uncased
Conversion of AdapterHub/bert-base-uncased-pf-squad_v2 for UKP SQuARE
Usage
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/bert-base-uncased-pf-squad_v2-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-squad_v2-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.
Evaluation results
Refer to the paper for more information on results.
Citation
If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
@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",
}