
# ONNX export of Adapter `AdapterHub/bert-base-uncased-pf-quartz` for bert-base-uncased
## Conversion of [AdapterHub/bert-base-uncased-pf-quartz](https://huggingface.co/AdapterHub/bert-base-uncased-pf-quartz) for UKP SQuARE


## Usage
```python
onnx_path = hf_hub_download(repo_id='UKP-SQuARE/bert-base-uncased-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/bert-base-uncased-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",
}
```
{"language": ["en"], "tags": ["onnx", "bert", "adapter-transformers"], "datasets": ["quartz"], "inference": false}
adapter-transformers onnx bert en dataset:quartz arxiv:2104.08247 region:us
TAGS
#adapter-transformers #onnx #bert #en #dataset-quartz #arxiv-2104.08247 #region-us 


# ONNX export of Adapter 'AdapterHub/bert-base-uncased-pf-quartz' for bert-base-uncased
## Conversion of AdapterHub/bert-base-uncased-pf-quartz for UKP SQuARE


## Usage


## Architecture & Training

The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.


## Evaluation results

Refer to the paper for more information on results.

If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":


# ONNX export of Adapter 'AdapterHub/bert-base-uncased-pf-quartz' for bert-base-uncased ## Conversion of AdapterHub/bert-base-uncased-pf-quartz for UKP SQuARE ## Usage ## Architecture & Training

The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here. ## Evaluation results

Refer to the paper for more information on results.

If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
TAGS
#adapter-transformers #onnx #bert #en #dataset-quartz #arxiv-2104.08247 #region-us 
 # ONNX export of Adapter 'AdapterHub/bert-base-uncased-pf-quartz' for bert-base-uncased ## Conversion of AdapterHub/bert-base-uncased-pf-quartz for UKP SQuARE ## Usage ## Architecture & Training

The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here. ## Evaluation results

Refer to the paper for more information on results.

If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":
TAGS
#adapter-transformers #onnx #bert #en #dataset-quartz #arxiv-2104.08247 #region-us 
# ONNX export of Adapter 'AdapterHub/bert-base-uncased-pf-quartz' for bert-base-uncased## Conversion of AdapterHub/bert-base-uncased-pf-quartz for UKP SQuARE## Usage## Architecture & Training

The training code for this adapter is available at URL
In particular, training configurations for all tasks can be found here.## Evaluation results

Refer to the paper for more information on results.

If you use this adapter, please cite our paper "What to Pre-Train on? Efficient Intermediate Task Selection":