update colab for metadata description and training parameters.
PiperOrigin-RevId: 334549353 Change-Id: I81cad2cd1b86e67de181a13361dbce71b68d1807
This commit is contained in:
Yuqi Li
TensorFlower Gardener
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tensorflow/lite/g3doc/tutorials
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"name": "model_maker_question_answer.ipynb",
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"provenance": [],
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"collapsed_sections": [],
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"toc_visible": true
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "h2q27gKz1H20",
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"colab_type": "text"
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"id": "h2q27gKz1H20"
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},
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"source": [
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"##### Copyright 2020 The TensorFlow Authors."
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@ -27,12 +11,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"cellView": "form",
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"id": "TUfAcER1oUS6",
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"colab_type": "code",
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"colab": {}
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"id": "TUfAcER1oUS6"
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},
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"outputs": [],
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"source": [
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"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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"# you may not use this file except in compliance with the License.\n",
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@ -45,15 +29,12 @@
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"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
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"# See the License for the specific language governing permissions and\n",
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"# limitations under the License."
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],
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"execution_count": null,
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"outputs": []
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Gb7qyhNL1yWt",
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"colab_type": "text"
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"id": "Gb7qyhNL1yWt"
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},
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"source": [
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"# Question Answer with TensorFlow Lite Model Maker"
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@ -62,31 +43,29 @@
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Fw5Y7snSuG51",
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"colab_type": "text"
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"id": "Fw5Y7snSuG51"
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},
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"source": [
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"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
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" <td>\n",
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" <a target=\"_blank\" href=\"https://www.tensorflow.org/lite/tutorials/model_maker_question_answer\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
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" </td>\n",
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" <td>\n",
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" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
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" </td>\n",
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" <td>\n",
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" <a target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
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" </td>\n",
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" <td>\n",
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" <a href=\"https://storage.googleapis.com/tensorflow_docs/tensorflow/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
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" </td>\n",
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"</table>"
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"\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
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" \u003ctd\u003e\n",
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" \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/lite/tutorials/model_maker_question_answer\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
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" \u003c/td\u003e\n",
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" \u003ctd\u003e\n",
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" \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
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" \u003c/td\u003e\n",
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" \u003ctd\u003e\n",
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" \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
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" \u003c/td\u003e\n",
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" \u003ctd\u003e\n",
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" \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/tensorflow/tensorflow/lite/g3doc/tutorials/model_maker_question_answer.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
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" \u003c/td\u003e\n",
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"\u003c/table\u003e"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "sr3q-gvm3cI8",
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"colab_type": "text"
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"id": "sr3q-gvm3cI8"
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},
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"source": [
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"The TensorFlow Lite Model Maker library simplifies the process of adapting and converting a TensorFlow model to particular input data when deploying this model for on-device ML applications.\n",
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "UxEHFTk755qw",
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"colab_type": "text"
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"id": "UxEHFTk755qw"
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},
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"source": [
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"# Introduction to Question Answer Task"
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "cFbKTCF25-SG",
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"colab_type": "text"
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"id": "cFbKTCF25-SG"
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},
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"source": [
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"The supported task in this library is extractive question answer task, which means given a passage and a question, the answer is the span in the passage. The image below shows an example for question answer.\n",
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"\n",
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"\n",
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"<p align=\"center\"><img src=\"https://storage.googleapis.com/download.tensorflow.org/models/tflite/screenshots/model_maker_squad_showcase.png\" width=\"500\"></p>\n",
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"\u003cp align=\"center\"\u003e\u003cimg src=\"https://storage.googleapis.com/download.tensorflow.org/models/tflite/screenshots/model_maker_squad_showcase.png\" width=\"500\"\u003e\u003c/p\u003e\n",
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"\n",
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"<p align=\"center\">\n",
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" <em>Answers are spans in the passage (image credit: <a href=\"https://rajpurkar.github.io/mlx/qa-and-squad/\">SQuAD blog</a>) </em>\n",
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"</p>\n",
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"\u003cp align=\"center\"\u003e\n",
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" \u003cem\u003eAnswers are spans in the passage (image credit: \u003ca href=\"https://rajpurkar.github.io/mlx/qa-and-squad/\"\u003eSQuAD blog\u003c/a\u003e) \u003c/em\u003e\n",
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"\u003c/p\u003e\n",
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"\n",
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"As for the model of question answer task, the inputs should be the passage and question pair that are already preprocessed, the outputs should be the start logits and end logits for each token in the passage.\n",
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"The size of input could be set and adjusted according to the length of passage and question."
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "gb7P4WQta8Ub",
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"colab_type": "text"
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"id": "gb7P4WQta8Ub"
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},
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"source": [
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"## End-to-End Overview\n"
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "w7cIHjIfbDlG",
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"colab_type": "text"
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"id": "w7cIHjIfbDlG"
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},
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"source": [
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"The following code snippet demonstrates how to get the model within a few lines of code. The overall process includes 5 steps: (1) choose a model, (2) load data, (3) retrain the model, (4) evaluate, and (5) export it to TensorFlow Lite format."
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "xQPdlxZBYuZG",
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"colab_type": "text"
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"id": "xQPdlxZBYuZG"
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},
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"source": [
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"```python\n",
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"# Gets the evaluation result.\n",
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"metric = model.evaluate(validation_data)\n",
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"\n",
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"# Exports the model to the TensorFlow Lite format in the export directory.\n",
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"# Exports the model to the TensorFlow Lite format with metadata in the export directory.\n",
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"model.export(export_dir)\n",
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"```"
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]
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "exScAdvBbNEi",
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"colab_type": "text"
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"id": "exScAdvBbNEi"
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},
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"source": [
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"The following sections explain the code in more detail."
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "bcLF2PKkSbV3",
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"colab_type": "text"
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"id": "bcLF2PKkSbV3"
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},
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"source": [
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"## Prerequisites\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "qhl8lqVamEty",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000
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},
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"id": "qhl8lqVamEty",
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"outputId": "f2ef33ec-ad6b-4b45-9c50-6d65118e80da"
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},
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"source": [
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"!pip install tflite-model-maker"
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],
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"execution_count": 1,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/33/d4/61c47ae889b490b9c5f07f4f61bdc057c158a1a1979c375fa019d647a19e/tf_nightly-2.4.0.dev20200914-cp36-cp36m-manylinux2010_x86_64.whl (390.1MB)\n",
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"\u001b[?25hCollecting tflite-support==0.1.0rc3.dev2\n",
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"Requirement already satisfied: python-dateutil\u003e=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas\u003e=0.22.0-\u003etf-models-nightly-\u003etflite-model-maker) (2.8.1)\n",
|
||||
"Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle\u003e=1.3.9-\u003etf-models-nightly-\u003etflite-model-maker) (2020.6.20)\n",
|
||||
"Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle\u003e=1.3.9-\u003etf-models-nightly-\u003etflite-model-maker) (4.0.1)\n",
|
||||
"Requirement already satisfied: slugify in /usr/local/lib/python3.6/dist-packages (from kaggle\u003e=1.3.9-\u003etf-models-nightly-\u003etflite-model-maker) (0.0.1)\n",
|
||||
"Requirement already satisfied: urllib3\u003c1.25,\u003e=1.21.1 in /usr/local/lib/python3.6/dist-packages (from kaggle\u003e=1.3.9-\u003etf-models-nightly-\u003etflite-model-maker) (1.24.3)\n",
|
||||
"Requirement already satisfied: pyasn1\u003e=0.1.7 in /usr/local/lib/python3.6/dist-packages (from oauth2client-\u003etf-models-nightly-\u003etflite-model-maker) (0.4.8)\n",
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||||
"Requirement already satisfied: rsa\u003e=3.1.4 in /usr/local/lib/python3.6/dist-packages (from oauth2client-\u003etf-models-nightly-\u003etflite-model-maker) (4.6)\n",
|
||||
"Requirement already satisfied: httplib2\u003e=0.9.1 in /usr/local/lib/python3.6/dist-packages (from oauth2client-\u003etf-models-nightly-\u003etflite-model-maker) (0.17.4)\n",
|
||||
"Requirement already satisfied: pyasn1-modules\u003e=0.0.5 in /usr/local/lib/python3.6/dist-packages (from oauth2client-\u003etf-models-nightly-\u003etflite-model-maker) (0.2.8)\n",
|
||||
"Requirement already satisfied: kiwisolver\u003e=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib-\u003etf-models-nightly-\u003etflite-model-maker) (1.2.0)\n",
|
||||
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,\u003e=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib-\u003etf-models-nightly-\u003etflite-model-maker) (2.4.7)\n",
|
||||
"Requirement already satisfied: cycler\u003e=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib-\u003etf-models-nightly-\u003etflite-model-maker) (0.10.0)\n",
|
||||
"Requirement already satisfied: Keras\u003e=2.2.4 in /usr/local/lib/python3.6/dist-packages (from seqeval-\u003etf-models-nightly-\u003etflite-model-maker) (2.4.3)\n",
|
||||
"Requirement already satisfied: google-auth-httplib2\u003e=0.0.3 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client\u003e=1.6.7-\u003etf-models-nightly-\u003etflite-model-maker) (0.0.4)\n",
|
||||
"Requirement already satisfied: uritemplate\u003c4dev,\u003e=3.0.0 in /usr/local/lib/python3.6/dist-packages (from google-api-python-client\u003e=1.6.7-\u003etf-models-nightly-\u003etflite-model-maker) (3.0.1)\n",
|
||||
"Requirement already satisfied: typeguard in /usr/local/lib/python3.6/dist-packages (from tensorflow-addons-\u003etf-models-nightly-\u003etflite-model-maker) (2.7.1)\n",
|
||||
"Requirement already satisfied: google-cloud-core\u003c2.0dev,\u003e=1.0.3 in /usr/local/lib/python3.6/dist-packages (from google-cloud-bigquery\u003e=0.31.0-\u003etf-models-nightly-\u003etflite-model-maker) (1.0.3)\n",
|
||||
"Requirement already satisfied: google-resumable-media!=0.4.0,\u003c0.5.0dev,\u003e=0.3.1 in /usr/local/lib/python3.6/dist-packages (from google-cloud-bigquery\u003e=0.31.0-\u003etf-models-nightly-\u003etflite-model-maker) (0.4.1)\n",
|
||||
"Requirement already satisfied: googleapis-common-protos\u003c2,\u003e=1.52.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-metadata-\u003etensorflow-datasets\u003e=2.1.0-\u003etflite-model-maker) (1.52.0)\n",
|
||||
"Requirement already satisfied: chardet\u003c4,\u003e=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests\u003e=2.19.0-\u003etensorflow-datasets\u003e=2.1.0-\u003etflite-model-maker) (3.0.4)\n",
|
||||
"Requirement already satisfied: idna\u003c3,\u003e=2.5 in /usr/local/lib/python3.6/dist-packages (from requests\u003e=2.19.0-\u003etensorflow-datasets\u003e=2.1.0-\u003etflite-model-maker) (2.10)\n",
|
||||
"Requirement already satisfied: requests-oauthlib\u003e=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib\u003c0.5,\u003e=0.4.1-\u003etb-nightly\u003c3.0.0a0,\u003e=2.4.0a0-\u003etf-nightly-\u003etflite-model-maker) (1.3.0)\n",
|
||||
"Requirement already satisfied: cachetools\u003c5.0,\u003e=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth\u003c2,\u003e=1.6.3-\u003etb-nightly\u003c3.0.0a0,\u003e=2.4.0a0-\u003etf-nightly-\u003etflite-model-maker) (4.1.1)\n",
|
||||
"Requirement already satisfied: importlib-metadata; python_version \u003c \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown\u003e=2.6.8-\u003etb-nightly\u003c3.0.0a0,\u003e=2.4.0a0-\u003etf-nightly-\u003etflite-model-maker) (1.7.0)\n",
|
||||
"Requirement already satisfied: text-unidecode\u003e=1.3 in /usr/local/lib/python3.6/dist-packages (from python-slugify-\u003ekaggle\u003e=1.3.9-\u003etf-models-nightly-\u003etflite-model-maker) (1.3)\n",
|
||||
"Requirement already satisfied: google-api-core\u003c2.0.0dev,\u003e=1.14.0 in /usr/local/lib/python3.6/dist-packages (from google-cloud-core\u003c2.0dev,\u003e=1.0.3-\u003egoogle-cloud-bigquery\u003e=0.31.0-\u003etf-models-nightly-\u003etflite-model-maker) (1.16.0)\n",
|
||||
"Requirement already satisfied: oauthlib\u003e=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib\u003e=0.7.0-\u003egoogle-auth-oauthlib\u003c0.5,\u003e=0.4.1-\u003etb-nightly\u003c3.0.0a0,\u003e=2.4.0a0-\u003etf-nightly-\u003etflite-model-maker) (3.1.0)\n",
|
||||
"Requirement already satisfied: zipp\u003e=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version \u003c \"3.8\"-\u003emarkdown\u003e=2.6.8-\u003etb-nightly\u003c3.0.0a0,\u003e=2.4.0a0-\u003etf-nightly-\u003etflite-model-maker) (3.1.0)\n",
|
||||
"Building wheels for collected packages: fire, pyyaml, seqeval, py-cpuinfo\n",
|
||||
" Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
|
||||
" Created wheel for fire: filename=fire-0.3.1-py2.py3-none-any.whl size=111005 sha256=f0b82e6b31e21d6db3591478a37188c727533acefe415b16b456c85ef9bef47c\n",
|
||||
@ -350,16 +319,17 @@
|
||||
" Uninstalling PyYAML-3.13:\n",
|
||||
" Successfully uninstalled PyYAML-3.13\n",
|
||||
"Successfully installed fire-0.3.1 flatbuffers-1.12 opencv-python-headless-4.4.0.42 py-cpuinfo-7.0.0 pybind11-2.5.0 pyyaml-5.3.1 sentencepiece-0.1.91 seqeval-0.0.12 tb-nightly-2.4.0a20200914 tensorflow-model-optimization-0.5.0 tf-estimator-nightly-2.4.0.dev2020091401 tf-models-nightly-2.3.0.dev20200914 tf-nightly-2.4.0.dev20200914 tf-slim-1.1.0 tflite-model-maker-0.1.2 tflite-support-0.1.0rc3.dev2\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!pip install tflite-model-maker"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "l6lRhVK9Q_0U",
|
||||
"colab_type": "text"
|
||||
"id": "l6lRhVK9Q_0U"
|
||||
},
|
||||
"source": [
|
||||
"Import the required packages."
|
||||
@ -367,11 +337,11 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "XtxiUeZEiXpt",
|
||||
"colab_type": "code",
|
||||
"colab": {}
|
||||
"id": "XtxiUeZEiXpt"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
@ -380,18 +350,16 @@
|
||||
"assert tf.__version__.startswith('2')\n",
|
||||
"\n",
|
||||
"from tflite_model_maker import configs\n",
|
||||
"from tflite_model_maker import ExportFormat\n",
|
||||
"from tflite_model_maker import model_spec\n",
|
||||
"from tflite_model_maker import question_answer\n",
|
||||
"from tflite_model_maker import QuestionAnswerDataLoader"
|
||||
],
|
||||
"execution_count": 3,
|
||||
"outputs": []
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "l65ctmtW7_FF",
|
||||
"colab_type": "text"
|
||||
"id": "l65ctmtW7_FF"
|
||||
},
|
||||
"source": [
|
||||
"The \"End-to-End Overview\" demonstrates a simple end-to-end example. The following sections walk through the example step by step to show more detail."
|
||||
@ -400,8 +368,7 @@
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "kJ_B8fMDOhMR",
|
||||
"colab_type": "text"
|
||||
"id": "kJ_B8fMDOhMR"
|
||||
},
|
||||
"source": [
|
||||
"## Choose a model_spec that represents a model for question answer\n",
|
||||
@ -419,22 +386,19 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "vEAWuZQ1PFiX",
|
||||
"colab_type": "code",
|
||||
"colab": {}
|
||||
"id": "vEAWuZQ1PFiX"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"spec = model_spec.get('mobilebert_qa_squad')"
|
||||
],
|
||||
"execution_count": 4,
|
||||
"outputs": []
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "ygEncJxtl-nQ",
|
||||
"colab_type": "text"
|
||||
"id": "ygEncJxtl-nQ"
|
||||
},
|
||||
"source": [
|
||||
"## Load Input Data Specific to an On-device ML App and Preprocess the Data\n",
|
||||
@ -450,15 +414,27 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "7tOfUr2KlgpU",
|
||||
"colab_type": "code",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 85
|
||||
},
|
||||
"id": "7tOfUr2KlgpU",
|
||||
"outputId": "a5d42181-82ea-47a2-f364-825701e4a1f8"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Downloading data from https://storage.googleapis.com/download.tensorflow.org/models/tflite/dataset/triviaqa-web-train-8000.json\n",
|
||||
"32571392/32570663 [==============================] - 1s 0us/step\n",
|
||||
"Downloading data from https://storage.googleapis.com/download.tensorflow.org/models/tflite/dataset/triviaqa-verified-web-dev.json\n",
|
||||
"1171456/1167744 [==============================] - 0s 0us/step\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"train_data_path = tf.keras.utils.get_file(\n",
|
||||
" fname='triviaqa-web-train-8000.json',\n",
|
||||
@ -466,31 +442,17 @@
|
||||
"validation_data_path = tf.keras.utils.get_file(\n",
|
||||
" fname='triviaqa-verified-web-dev.json',\n",
|
||||
" origin='https://storage.googleapis.com/download.tensorflow.org/models/tflite/dataset/triviaqa-verified-web-dev.json')"
|
||||
],
|
||||
"execution_count": 5,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Downloading data from https://storage.googleapis.com/download.tensorflow.org/models/tflite/dataset/triviaqa-web-train-8000.json\n",
|
||||
"32571392/32570663 [==============================] - 1s 0us/step\n",
|
||||
"Downloading data from https://storage.googleapis.com/download.tensorflow.org/models/tflite/dataset/triviaqa-verified-web-dev.json\n",
|
||||
"1171456/1167744 [==============================] - 0s 0us/step\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "UfZk8GNr_1nc",
|
||||
"colab_type": "text"
|
||||
"id": "UfZk8GNr_1nc"
|
||||
},
|
||||
"source": [
|
||||
"You can also train the MobileBERT model with your own dataset. If you are running this notebook on Colab, upload your data by using the left sidebar.\n",
|
||||
"\n",
|
||||
"<img src=\"https://storage.googleapis.com/download.tensorflow.org/models/tflite/screenshots/model_maker_question_answer.png\" alt=\"Upload File\" width=\"800\" hspace=\"100\">\n",
|
||||
"\u003cimg src=\"https://storage.googleapis.com/download.tensorflow.org/models/tflite/screenshots/model_maker_question_answer.png\" alt=\"Upload File\" width=\"800\" hspace=\"100\"\u003e\n",
|
||||
"\n",
|
||||
"If you prefer not to upload your data to the cloud, you can also run the library offline by following the [guide](https://github.com/tensorflow/examples/tree/master/tensorflow_examples/lite/model_maker)."
|
||||
]
|
||||
@ -498,8 +460,7 @@
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "E051HBUM5owi",
|
||||
"colab_type": "text"
|
||||
"id": "E051HBUM5owi"
|
||||
},
|
||||
"source": [
|
||||
"Use the `QuestionAnswerDataLoader.from_squad` method to load and preprocess the [SQuAD format](https://rajpurkar.github.io/SQuAD-explorer/) data according to a specific `model_spec`. You can use either SQuAD2.0 or SQuAD1.1 formats. Setting parameter `version_2_with_negative` as `True` means the formats is SQuAD2.0. Otherwise, the format is SQuAD1.1. By default, `version_2_with_negative` is `False`."
|
||||
@ -507,23 +468,20 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "I_fOlZsklmlL",
|
||||
"colab_type": "code",
|
||||
"colab": {}
|
||||
"id": "I_fOlZsklmlL"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_data = QuestionAnswerDataLoader.from_squad(train_data_path, spec, is_training=True)\n",
|
||||
"validation_data = QuestionAnswerDataLoader.from_squad(validation_data_path, spec, is_training=False)"
|
||||
],
|
||||
"execution_count": 6,
|
||||
"outputs": []
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "AWuoensX4vDA",
|
||||
"colab_type": "text"
|
||||
"id": "AWuoensX4vDA"
|
||||
},
|
||||
"source": [
|
||||
"## Customize the TensorFlow Model\n",
|
||||
@ -536,48 +494,46 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "TvYSUuJY3QxR",
|
||||
"colab_type": "code",
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/",
|
||||
"height": 68
|
||||
},
|
||||
"id": "TvYSUuJY3QxR",
|
||||
"outputId": "67c38b57-3596-467b-c71b-b1a3980e68c7"
|
||||
},
|
||||
"source": [
|
||||
"model = question_answer.create(train_data, model_spec=spec)"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"INFO:tensorflow:Retraining the models...\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"INFO:tensorflow:Retraining the models...\n"
|
||||
],
|
||||
"name": "stderr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 1/2\n"
|
||||
],
|
||||
"name": "stdout"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"model = question_answer.create(train_data, model_spec=spec)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "0JKI-pNc8idH",
|
||||
"colab_type": "text"
|
||||
"id": "0JKI-pNc8idH"
|
||||
},
|
||||
"source": [
|
||||
"Have a look at the detailed model structure."
|
||||
@ -585,22 +541,19 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "gd7Hs8TF8n3H",
|
||||
"colab_type": "code",
|
||||
"colab": {}
|
||||
"id": "gd7Hs8TF8n3H"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.summary()"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "LP5FPk_tOxoZ",
|
||||
"colab_type": "text"
|
||||
"id": "LP5FPk_tOxoZ"
|
||||
},
|
||||
"source": [
|
||||
"## Evaluate the Customized Model\n",
|
||||
@ -610,22 +563,19 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "A8c2ZQ0J3Riy",
|
||||
"colab_type": "code",
|
||||
"colab": {}
|
||||
"id": "A8c2ZQ0J3Riy"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.evaluate(validation_data)"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "aeHoGAceO2xV",
|
||||
"colab_type": "text"
|
||||
"id": "aeHoGAceO2xV"
|
||||
},
|
||||
"source": [
|
||||
"## Export to TensorFlow Lite Model\n",
|
||||
@ -636,8 +586,7 @@
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "TwA2Z2pokQJc",
|
||||
"colab_type": "text"
|
||||
"id": "TwA2Z2pokQJc"
|
||||
},
|
||||
"source": [
|
||||
"Since MobileBERT is too big for on-device applications, use dynamic range quantization on the model to compress MobileBERT by 4x with the minimal loss of performance. First, define the quantization configuration:"
|
||||
@ -645,56 +594,75 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "1wBVTO8qkmum",
|
||||
"colab_type": "code",
|
||||
"colab": {}
|
||||
"id": "1wBVTO8qkmum"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"config = configs.QuantizationConfig.create_dynamic_range_quantization(optimizations=[tf.lite.Optimize.OPTIMIZE_FOR_LATENCY])\n",
|
||||
"config._experimental_new_quantizer = True"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "qea2YkEGkOTH",
|
||||
"colab_type": "text"
|
||||
"id": "qea2YkEGkOTH"
|
||||
},
|
||||
"source": [
|
||||
"Export the quantized TFLite model according to the quantization config and save the vocabulary to a vocab file. The default TFLite model filename is `model.tflite`, and the default vocab filename is `vocab`."
|
||||
"Export the quantized TFLite model according to the quantization config with [metadata](https://www.tensorflow.org/lite/convert/metadata). The default TFLite model filename is `model.tflite`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "Im6wA9lK3TQB",
|
||||
"colab_type": "code",
|
||||
"colab": {}
|
||||
"id": "Im6wA9lK3TQB"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.export(export_dir='.', quantization_config=config)"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "w12kvDdHJIGH",
|
||||
"colab_type": "text"
|
||||
},
|
||||
"source": [
|
||||
"You can use the TensorFlow Lite model file and vocab file in the [bert_qa](https://github.com/tensorflow/examples/tree/master/lite/examples/bert_qa/android) reference app by downloading it from the left sidebar on Colab."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "HZKYthlVrTos",
|
||||
"colab_type": "text"
|
||||
"id": "w12kvDdHJIGH"
|
||||
},
|
||||
"source": [
|
||||
"You can use the TensorFlow Lite model file in the [bert_qa](https://github.com/tensorflow/examples/tree/master/lite/examples/bert_qa/android) reference app using [BertQuestionAnswerer API](https://www.tensorflow.org/lite/inference_with_metadata/task_library/bert_question_answerer) in [TensorFlow Lite Task Library](https://www.tensorflow.org/lite/inference_with_metadata/task_library/overview) by downloading it from the left sidebar on Colab."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "VFnJPvq3VGh3"
|
||||
},
|
||||
"source": [
|
||||
"The allowed export formats can be one or a list of the following:\n",
|
||||
"\n",
|
||||
"* `ExportFormat.TFLITE`\n",
|
||||
"* `ExportFormat.VOCAB`\n",
|
||||
"* `ExportFormat.SAVED_MODEL`\n",
|
||||
"\n",
|
||||
"By default, it just exports TensorFlow Lite model with metadata. You can also selectively export different files. For instance, exporting only the vocab file as follows:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "ro2hz4kXVImY"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.export(export_dir='.', export_format=ExportFormat.VOCAB)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "HZKYthlVrTos"
|
||||
},
|
||||
"source": [
|
||||
"You can also evalute the tflite model with the `evaluate_tflite` method. This step is expected to take a long time."
|
||||
@ -702,22 +670,19 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "ochbq95ZrVFX",
|
||||
"colab_type": "code",
|
||||
"colab": {}
|
||||
"id": "ochbq95ZrVFX"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model.evaluate_tflite('model.tflite', validation_data)"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "EoWiA_zX8rxE",
|
||||
"colab_type": "text"
|
||||
"id": "EoWiA_zX8rxE"
|
||||
},
|
||||
"source": [
|
||||
"## Advanced Usage\n",
|
||||
@ -733,8 +698,7 @@
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "mwtiksguDfhl",
|
||||
"colab_type": "text"
|
||||
"id": "mwtiksguDfhl"
|
||||
},
|
||||
"source": [
|
||||
"### Adjust the model\n",
|
||||
@ -761,8 +725,7 @@
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "cAOd5_bzH9AQ",
|
||||
"colab_type": "text"
|
||||
"id": "cAOd5_bzH9AQ"
|
||||
},
|
||||
"source": [
|
||||
"For example, you can train the model with a longer sequence length. If you change the model, you must first construct a new `model_spec`."
|
||||
@ -770,23 +733,20 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "e9WBN0UTQoMN",
|
||||
"colab_type": "code",
|
||||
"colab": {}
|
||||
"id": "e9WBN0UTQoMN"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"new_spec = model_spec.get('mobilebert_qa')\n",
|
||||
"new_spec.seq_len = 512"
|
||||
],
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "6LSTdghTP0Cv",
|
||||
"colab_type": "text"
|
||||
"id": "6LSTdghTP0Cv"
|
||||
},
|
||||
"source": [
|
||||
"The remaining steps are the same. Note that you must rerun both the `dataloader` and `create` parts as different model specs may have different preprocessing steps.\n"
|
||||
@ -795,8 +755,7 @@
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "LvQuy7RSDir3",
|
||||
"colab_type": "text"
|
||||
"id": "LvQuy7RSDir3"
|
||||
},
|
||||
"source": [
|
||||
"### Tune training hyperparameters\n",
|
||||
@ -815,8 +774,7 @@
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "Eq6B9lKMfhS6",
|
||||
"colab_type": "text"
|
||||
"id": "Eq6B9lKMfhS6"
|
||||
},
|
||||
"source": [
|
||||
"### Change the Model Architecture\n",
|
||||
@ -831,12 +789,26 @@
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"id": "L2d7yycrgu6L",
|
||||
"colab_type": "text"
|
||||
"id": "L2d7yycrgu6L"
|
||||
},
|
||||
"source": [
|
||||
"The remaining steps are the same."
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"accelerator": "GPU",
|
||||
"colab": {
|
||||
"collapsed_sections": [],
|
||||
"name": "model_maker_question_answer.ipynb",
|
||||
"provenance": [],
|
||||
"toc_visible": true
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"name": "python3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user