Update README.md

README.md

@@ -9,18 +9,18 @@ datasets:
 - narrativeqa
 ---
 
-# ONNX export of Adapter `hSterz/narrativeqa` for facebook/bart-base
+# ONNX export of Adapter `AdapterHub/narrativeqa` for facebook/bart-base
 ## Conversion of [AdapterHub/narrativeqa](https://huggingface.co/AdapterHub/narrativeqa) for UKP SQuARE
 
 
 ## Usage
 ```python
-onnx_path = hf_hub_download(repo_id='UKP-SQuARE/narrativeqa-onnx', filename='model.onnx') # or model_quant.onnx for quantization
+onnx_path = hf_hub_download(repo_id='UKP-SQuARE/bart-base-pf-narrativeqa-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/narrativeqa-onnx')
+tokenizer = AutoTokenizer.from_pretrained('UKP-SQuARE/bart-base-pf-narrativeqa-onnx')
 
 inputs = tokenizer(question, context, padding=True, truncation=True, return_tensors='np')
 outputs = onnx_model.run(input_feed=dict(inputs), output_names=None)