Update README.md

README.md

@@ -2,44 +2,6 @@
 license: apache-2.0
 ---
 
-## Convert pytorch model to onnx format.
-
-```
-import torch
-import onnx
-import onnxruntime
-from onnxruntime import InferenceSession
-from transformers import RobertaTokenizer, RobertaModel
-from transformers.convert_graph_to_onnx import convert
-import numpy as np
-from onnxruntime.transformers import optimizer
-from pathlib import Path
-from onnxruntime.quantization import quantize_dynamic, QuantType
-from sentence_transformers import SentenceTransformer, util
-
-sbert = SentenceTransformer('sentence-transformers/all-roberta-large-v1')
-sbert.save('sbert-all-roberta-large-v1')
-
-tokenizer = RobertaTokenizer.from_pretrained('sentence-transformers/all-roberta-large-v1')
-model = RobertaModel.from_pretrained('sentence-transformers/all-roberta-large-v1')
-model.save_pretrained('./all-roberta-large-v1/')
-tokenizer.save_pretrained('./all-roberta-large-v1/')
-
-opt_model_path = "onnx-model/sbert-roberta-large.onnx"
-convert(framework='pt', model='./all-roberta-large-v1/', output= Path(opt_model_path), opset=12, use_external_format=False, pipeline_name='feature-extraction')
-
-quantize_dynamic(
-    model_input='onnx-model/sbert-roberta-large.onnx',
-    model_output='onnx-model/sbert-roberta-large-quant.onnx',
-    per_channel=True,
-    reduce_range=True,
-    activation_type=QuantType.QUInt8,
-    weight_type=QuantType.QInt8,
-    optimize_model=False,
-    use_external_data_format=False
-) 
-```
-##Copy pooling layer and tokenizer files to the output directory
 
 ```
 ##How to download the model?