Accelerated gpt2-chinese-cluecorpussmall model
hello！I want to speed up the generation of gpt2 Chinese model！Convert to onnx as required by the document.huggingface.coExport 🤗 Transformers ModelsWe’re on a journey to advance and democratize artificial intelligence through open source and open science.1、Test according to document codeimport onnxruntime as ortfrom transformers import BertTokenizerFasttokenizer = BertTokenizerFast.from_pretrained(“bert-base-cased”)ort_session = ort.InferenceSession(“onnx/bert-base-cased/model.onnx”)inputs = tokenizer(“Using BERT in ONNX!”, return_tensors=“np”)outputs = ort_session.run([“last_hidden_state”, “pooler_output”], dict(inputs))outputs how should I decode it？After reading this case.github.commicrosoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/notebooks/Inference_GPT2_with_OnnxRuntime_on_CPU.ipynb{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference PyTorch GPT2 Model with ONNX Runtime on CPU\n",
    "\n",
    "In this tutorial, you'll be introduced to how to load a GPT2 model from PyTorch, convert it to ONNX, and inference it using ONNX Runtime using IO Binding. Note that past state is used to get better performance."
   ]
  },
  {This file has been truncated.show originalI really don’t understand. Forgive me for being a little white.Hope to get help！