onnx object for sentiment

sentiment_onnx.py

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+import numpy as np
+from transformers import AutoTokenizer,AutoModelForSequenceClassification
+import transformers.convert_graph_to_onnx as onnx_convert
+from pathlib import Path
+import transformers
+from onnxruntime.quantization import quantize_dynamic,QuantType
+import onnx
+import torch
+import onnxruntime as ort
+import streamlit as st
+
+"""
+type in cmd  to create onnx model of hugging face chkpt
+python3 -m transformers.onnx --model= distilbert-base-uncased-finetuned-sst-2-english sentiment_onnx/
+"""
+
+model= AutoModelForSequenceClassification.from_pretrained('sentiment_classifier/')
+tokenizer= AutoTokenizer.from_pretrained('sentiment_classifier/')
+
+""" 
+or download the model directly from hub --
+chkpt='distilbert-base-uncased-finetuned-sst-2-english'
+model= AutoModelForSequenceClassification.from_pretrained(chkpt)
+tokenizer= AutoTokenizer.from_pretrained(chkpt)
+"""
+
+
+pipeline=transformers.pipeline("text-classification",model=model,tokenizer=tokenizer)
+
+""" convert pipeline to onnx object"""
+onnx_convert.convert_pytorch(pipeline,
+                             opset=11,
+                             output=Path("sent_clf_onnx/sentiment_classifier_onnx.onnx"),
+                             use_external_format=False
+                             )
+
+""" convert onnx object to another onnx object with int8 quantization """
+quantize_dynamic("sent_clf_onnx/sentiment_classifier_onnx.onnx","sent_clf_onnx/sentiment_classifier_onnx_int8.onnx",
+                 weight_type=QuantType.QUInt8)
+
+print(ort.__version__)
+print(onnx.__version__)
+
+