|
--- |
|
inference: false |
|
tags: |
|
- onnx |
|
- roberta |
|
- adapter-transformers |
|
datasets: |
|
- quartz |
|
language: |
|
- en |
|
--- |
|
|
|
# ONNX export of Adapter `AdapterHub/roberta-base-pf-quartz` for roberta-base |
|
## Conversion of [AdapterHub/roberta-base-pf-quartz](https://huggingface.co/AdapterHub/roberta-base-pf-quartz) for UKP SQuARE |
|
|
|
|
|
## Usage |
|
```python |
|
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/roberta-base-pf-quartz-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-quartz-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-what-to-pre-train-on, |
|
title={What to Pre-Train on? Efficient Intermediate Task Selection}, |
|
author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, |
|
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", |
|
month = nov, |
|
year = "2021", |
|
address = "Online", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/2104.08247", |
|
pages = "to appear", |
|
} |
|
``` |
