{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "vXLA5InzXydn" }, "source": [ "##### Copyright 2019 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2024-02-07T12:12:28.232412Z", "iopub.status.busy": "2024-02-07T12:12:28.232174Z", "iopub.status.idle": "2024-02-07T12:12:28.236244Z", "shell.execute_reply": "2024-02-07T12:12:28.235618Z" }, "id": "RuRlpLL-X0R_" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "1mLJmVotXs64" }, "source": [ "# Fine-tuning a BERT model" ] }, { "cell_type": "markdown", "metadata": { "id": "hYEwGTeCXnnX" }, "source": [ "\n", " \n", " \n", " \n", " \n", " \n", "
\n", " View on TensorFlow.org\n", " \n", " Run in Google Colab\n", " \n", " View source on GitHub\n", " \n", " Download notebook\n", " \n", " See TF Hub model\n", "
" ] }, { "cell_type": "markdown", "metadata": { "id": "YN2ACivEPxgD" }, "source": [ "This tutorial demonstrates how to fine-tune a [Bidirectional Encoder Representations from Transformers (BERT)](https://arxiv.org/abs/1810.04805) (Devlin et al., 2018) model using [TensorFlow Model Garden](https://github.com/tensorflow/models).\n", "\n", "You can also find the pre-trained BERT model used in this tutorial on [TensorFlow Hub (TF Hub)](https://tensorflow.org/hub). For concrete examples of how to use the models from TF Hub, refer to the [Solve Glue tasks using BERT](https://www.tensorflow.org/text/tutorials/bert_glue) tutorial. If you're just trying to fine-tune a model, the TF Hub tutorial is a good starting point.\n", "\n", "On the other hand, if you're interested in deeper customization, follow this tutorial. It shows how to do a lot of things manually, so you can learn how you can customize the workflow from data preprocessing to training, exporting and saving the model." ] }, { "cell_type": "markdown", "metadata": { "id": "s2d9S2CSSO1z" }, "source": [ "## Setup" ] }, { "cell_type": "markdown", "metadata": { "id": "69de3375e32a" }, "source": [ "### Install pip packages" ] }, { "cell_type": "markdown", "metadata": { "id": "fsACVQpVSifi" }, "source": [ "Start by installing the TensorFlow Text and Model Garden pip packages.\n", "\n", "* `tf-models-official` is the TensorFlow Model Garden package. Note that it may not include the latest changes in the `tensorflow_models` GitHub repo. To include the latest changes, you may install `tf-models-nightly`, which is the nightly Model Garden package created daily automatically.\n", "* pip will install all models and dependencies automatically." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:12:28.240129Z", "iopub.status.busy": "2024-02-07T12:12:28.239601Z", "iopub.status.idle": "2024-02-07T12:12:32.840545Z", "shell.execute_reply": "2024-02-07T12:12:32.839313Z" }, "id": "sE6XUxLOf1s-" }, "outputs": [], "source": [ "!pip install -q opencv-python" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:12:32.845710Z", "iopub.status.busy": "2024-02-07T12:12:32.844921Z", "iopub.status.idle": "2024-02-07T12:13:06.283056Z", "shell.execute_reply": "2024-02-07T12:13:06.282161Z" }, "id": "yic2y7_o-BCC" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n", "tensorflow-datasets 4.9.3 requires protobuf>=3.20, but you have protobuf 3.19.6 which is incompatible.\r\n", "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.19.6 which is incompatible.\u001b[0m\u001b[31m\r\n", "\u001b[0m" ] } ], "source": [ "!pip install -q -U \"tensorflow-text==2.11.*\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:06.287149Z", "iopub.status.busy": "2024-02-07T12:13:06.286884Z", "iopub.status.idle": "2024-02-07T12:13:36.948473Z", "shell.execute_reply": "2024-02-07T12:13:36.947448Z" }, "id": "NvNr2svBM-p3" }, "outputs": [], "source": [ "!pip install -q tf-models-official" ] }, { "cell_type": "markdown", "metadata": { "id": "U-7qPCjWUAyy" }, "source": [ "### Import libraries" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:36.952887Z", "iopub.status.busy": "2024-02-07T12:13:36.952621Z", "iopub.status.idle": "2024-02-07T12:13:41.523517Z", "shell.execute_reply": "2024-02-07T12:13:41.522766Z" }, "id": "lXsXev5MNr20" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-02-07 12:13:37.890233: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "2024-02-07 12:13:37.890282: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "2024-02-07 12:13:37.891884: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" ] } ], "source": [ "import os\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import tensorflow as tf\n", "import tensorflow_models as tfm\n", "import tensorflow_hub as hub\n", "import tensorflow_datasets as tfds\n", "tfds.disable_progress_bar()" ] }, { "cell_type": "markdown", "metadata": { "id": "mbanlzTvJBsz" }, "source": [ "### Resources" ] }, { "cell_type": "markdown", "metadata": { "id": "PpW0x8TpR8DT" }, "source": [ "The following directory contains the BERT model's configuration, vocabulary, and a pre-trained checkpoint used in this tutorial:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:41.528377Z", "iopub.status.busy": "2024-02-07T12:13:41.527624Z", "iopub.status.idle": "2024-02-07T12:13:41.631488Z", "shell.execute_reply": "2024-02-07T12:13:41.630812Z" }, "id": "vzRHOLciR8eq" }, "outputs": [ { "data": { "text/plain": [ "['bert_config.json',\n", " 'bert_model.ckpt.data-00000-of-00001',\n", " 'bert_model.ckpt.index',\n", " 'vocab.txt']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gs_folder_bert = \"gs://cloud-tpu-checkpoints/bert/v3/uncased_L-12_H-768_A-12\"\n", "tf.io.gfile.listdir(gs_folder_bert)" ] }, { "cell_type": "markdown", "metadata": { "id": "Qv6abtRvH4xO" }, "source": [ "## Load and preprocess the dataset\n", "\n", "This example uses the GLUE (General Language Understanding Evaluation) MRPC (Microsoft Research Paraphrase Corpus) [dataset from TensorFlow Datasets (TFDS)](https://www.tensorflow.org/datasets/catalog/glue#gluemrpc).\n", "\n", "This dataset is not set up such that it can be directly fed into the BERT model. The following section handles the necessary preprocessing." ] }, { "cell_type": "markdown", "metadata": { "id": "28DvUhC1YUiB" }, "source": [ "### Get the dataset from TensorFlow Datasets\n", "\n", "The GLUE MRPC (Dolan and Brockett, 2005) dataset is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent. It has the following attributes:\n", "\n", "* Number of labels: 2\n", "* Size of training dataset: 3668\n", "* Size of evaluation dataset: 408\n", "* Maximum sequence length of training and evaluation dataset: 128\n", "\n", "Begin by loading the MRPC dataset from TFDS:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:41.635832Z", "iopub.status.busy": "2024-02-07T12:13:41.635005Z", "iopub.status.idle": "2024-02-07T12:13:44.893961Z", "shell.execute_reply": "2024-02-07T12:13:44.893260Z" }, "id": "Ijikx5OsH9AT" }, "outputs": [], "source": [ "batch_size=32\n", "glue, info = tfds.load('glue/mrpc',\n", " with_info=True,\n", " batch_size=32)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:44.897642Z", "iopub.status.busy": "2024-02-07T12:13:44.897382Z", "iopub.status.idle": "2024-02-07T12:13:44.902040Z", "shell.execute_reply": "2024-02-07T12:13:44.901459Z" }, "id": "QcMTJU4N7VX-" }, "outputs": [ { "data": { "text/plain": [ "{'train': <_PrefetchDataset element_spec={'idx': TensorSpec(shape=(None,), dtype=tf.int32, name=None), 'label': TensorSpec(shape=(None,), dtype=tf.int64, name=None), 'sentence1': TensorSpec(shape=(None,), dtype=tf.string, name=None), 'sentence2': TensorSpec(shape=(None,), dtype=tf.string, name=None)}>,\n", " 'validation': <_PrefetchDataset element_spec={'idx': TensorSpec(shape=(None,), dtype=tf.int32, name=None), 'label': TensorSpec(shape=(None,), dtype=tf.int64, name=None), 'sentence1': TensorSpec(shape=(None,), dtype=tf.string, name=None), 'sentence2': TensorSpec(shape=(None,), dtype=tf.string, name=None)}>,\n", " 'test': <_PrefetchDataset element_spec={'idx': TensorSpec(shape=(None,), dtype=tf.int32, name=None), 'label': TensorSpec(shape=(None,), dtype=tf.int64, name=None), 'sentence1': TensorSpec(shape=(None,), dtype=tf.string, name=None), 'sentence2': TensorSpec(shape=(None,), dtype=tf.string, name=None)}>}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "glue" ] }, { "cell_type": "markdown", "metadata": { "id": "ZgBg2r2nYT-K" }, "source": [ "The `info` object describes the dataset and its features:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:44.904906Z", "iopub.status.busy": "2024-02-07T12:13:44.904684Z", "iopub.status.idle": "2024-02-07T12:13:44.909060Z", "shell.execute_reply": "2024-02-07T12:13:44.908422Z" }, "id": "IQrHxv7W7jH5" }, "outputs": [ { "data": { "text/plain": [ "FeaturesDict({\n", " 'idx': int32,\n", " 'label': ClassLabel(shape=(), dtype=int64, num_classes=2),\n", " 'sentence1': Text(shape=(), dtype=string),\n", " 'sentence2': Text(shape=(), dtype=string),\n", "})" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "info.features" ] }, { "cell_type": "markdown", "metadata": { "id": "vhsVWYNxazz5" }, "source": [ "The two classes are:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:44.912253Z", "iopub.status.busy": "2024-02-07T12:13:44.911757Z", "iopub.status.idle": "2024-02-07T12:13:44.915657Z", "shell.execute_reply": "2024-02-07T12:13:44.915122Z" }, "id": "n0gfc_VTayfQ" }, "outputs": [ { "data": { "text/plain": [ "['not_equivalent', 'equivalent']" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "info.features['label'].names" ] }, { "cell_type": "markdown", "metadata": { "id": "38zJcap6xkbC" }, "source": [ "Here is one example from the training set:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:44.918648Z", "iopub.status.busy": "2024-02-07T12:13:44.918219Z", "iopub.status.idle": "2024-02-07T12:13:45.180191Z", "shell.execute_reply": "2024-02-07T12:13:45.179495Z" }, "id": "xON_i6SkwApW" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "idx : 1680\n", "label : 0\n", "sentence1: b'The identical rovers will act as robotic geologists , searching for evidence of past water .'\n", "sentence2: b'The rovers act as robotic geologists , moving on six wheels .'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2024-02-07 12:13:45.153482: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n" ] } ], "source": [ "example_batch = next(iter(glue['train']))\n", "\n", "for key, value in example_batch.items():\n", " print(f\"{key:9s}: {value[0].numpy()}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "R9vEWgKA4SxV" }, "source": [ "### Preprocess the data\n", "\n", "The keys `\"sentence1\"` and `\"sentence2\"` in the GLUE MRPC dataset contain two input sentences for each example.\n", "\n", "Because the BERT model from the Model Garden doesn't take raw text as input, two things need to happen first:\n", "\n", "1. The text needs to be _tokenized_ (split into word pieces) and converted to _indices_.\n", "2. Then, the _indices_ need to be packed into the format that the model expects." ] }, { "cell_type": "markdown", "metadata": { "id": "9fbTyfJpNr7x" }, "source": [ "#### The BERT tokenizer" ] }, { "cell_type": "markdown", "metadata": { "id": "wqeN54S61ZKQ" }, "source": [ "To fine tune a pre-trained language model from the Model Garden, such as BERT, you need to make sure that you're using exactly the same tokenization, vocabulary, and index mapping as used during training.\n", "\n", "The following code rebuilds the tokenizer that was used by the base model using the Model Garden's `tfm.nlp.layers.FastWordpieceBertTokenizer` layer:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:45.184631Z", "iopub.status.busy": "2024-02-07T12:13:45.184057Z", "iopub.status.idle": "2024-02-07T12:13:45.634447Z", "shell.execute_reply": "2024-02-07T12:13:45.633732Z" }, "id": "-DK4q5wEBmlB" }, "outputs": [], "source": [ "tokenizer = tfm.nlp.layers.FastWordpieceBertTokenizer(\n", " vocab_file=os.path.join(gs_folder_bert, \"vocab.txt\"),\n", " lower_case=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "zYHDSquU2lDU" }, "source": [ "Let's tokenize a test sentence:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:45.638481Z", "iopub.status.busy": "2024-02-07T12:13:45.638159Z", "iopub.status.idle": "2024-02-07T12:13:45.877755Z", "shell.execute_reply": "2024-02-07T12:13:45.877025Z" }, "id": "L_OfOYPg853R" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tokens = tokenizer(tf.constant([\"Hello TensorFlow!\"]))\n", "tokens" ] }, { "cell_type": "markdown", "metadata": { "id": "MfjaaMYy5Gt8" }, "source": [ "Learn more about the tokenization process in the [Subword tokenization](https://www.tensorflow.org/text/guide/subwords_tokenizer) and [Tokenizing with TensorFlow Text](https://www.tensorflow.org/text/guide/tokenizers) guides." ] }, { "cell_type": "markdown", "metadata": { "id": "wd1b09OO5GJl" }, "source": [ "#### Pack the inputs" ] }, { "cell_type": "markdown", "metadata": { "id": "62UTWLQd9-LB" }, "source": [ "TensorFlow Model Garden's BERT model doesn't just take the tokenized strings as input. It also expects these to be packed into a particular format. `tfm.nlp.layers.BertPackInputs` layer can handle the conversion from _a list of tokenized sentences_ to the input format expected by the Model Garden's BERT model.\n", "\n", "`tfm.nlp.layers.BertPackInputs` packs the two input sentences (per example in the MRCP dataset) concatenated together. This input is expected to start with a `[CLS]` \"This is a classification problem\" token, and each sentence should end with a `[SEP]` \"Separator\" token.\n", "\n", "Therefore, the `tfm.nlp.layers.BertPackInputs` layer's constructor takes the `tokenizer`'s special tokens as an argument. It also needs to know the indices of the tokenizer's special tokens." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:45.881552Z", "iopub.status.busy": "2024-02-07T12:13:45.881085Z", "iopub.status.idle": "2024-02-07T12:13:45.885633Z", "shell.execute_reply": "2024-02-07T12:13:45.884965Z" }, "id": "5iroDlrFDRcF" }, "outputs": [ { "data": { "text/plain": [ "{'vocab_size': 30522,\n", " 'start_of_sequence_id': 101,\n", " 'end_of_segment_id': 102,\n", " 'padding_id': 0,\n", " 'mask_id': 103}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "special = tokenizer.get_special_tokens_dict()\n", "special" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:45.888534Z", "iopub.status.busy": "2024-02-07T12:13:45.888251Z", "iopub.status.idle": "2024-02-07T12:13:45.892343Z", "shell.execute_reply": "2024-02-07T12:13:45.891684Z" }, "id": "b71HarkuG92H" }, "outputs": [], "source": [ "max_seq_length = 128\n", "\n", "packer = tfm.nlp.layers.BertPackInputs(\n", " seq_length=max_seq_length,\n", " special_tokens_dict = tokenizer.get_special_tokens_dict())" ] }, { "cell_type": "markdown", "metadata": { "id": "CZlSZbYd6liN" }, "source": [ "The `packer` takes a list of tokenized sentences as input. For example:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:45.895554Z", "iopub.status.busy": "2024-02-07T12:13:45.894987Z", "iopub.status.idle": "2024-02-07T12:13:45.937244Z", "shell.execute_reply": "2024-02-07T12:13:45.936642Z" }, "id": "27dU_VkJHc9S" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sentences1 = [\"hello tensorflow\"]\n", "tok1 = tokenizer(sentences1)\n", "tok1" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:45.940066Z", "iopub.status.busy": "2024-02-07T12:13:45.939835Z", "iopub.status.idle": "2024-02-07T12:13:45.980105Z", "shell.execute_reply": "2024-02-07T12:13:45.979495Z" }, "id": "LURHmNOSHnWN" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sentences2 = [\"goodbye tensorflow\"]\n", "tok2 = tokenizer(sentences2)\n", "tok2" ] }, { "cell_type": "markdown", "metadata": { "id": "r8bvB8gI8BqP" }, "source": [ "Then, it returns a dictionary containing three outputs:\n", "\n", "- `input_word_ids`: The tokenized sentences packed together.\n", "- `input_mask`: The mask indicating which locations are valid in the other outputs.\n", "- `input_type_ids`: Indicating which sentence each token belongs to." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:45.983066Z", "iopub.status.busy": "2024-02-07T12:13:45.982833Z", "iopub.status.idle": "2024-02-07T12:13:46.085843Z", "shell.execute_reply": "2024-02-07T12:13:46.085126Z" }, "id": "YsIDTOMJHrUQ" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "input_word_ids : [[ 101 7592 23435 12314 102 9119 23435 12314 102 0 0 0]]\n", "input_mask : [[1 1 1 1 1 1 1 1 1 0 0 0]]\n", "input_type_ids : [[0 0 0 0 0 1 1 1 1 0 0 0]]\n" ] } ], "source": [ "packed = packer([tok1, tok2])\n", "\n", "for key, tensor in packed.items():\n", " print(f\"{key:15s}: {tensor[:, :12]}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "red4tRcq74Qc" }, "source": [ "#### Put it all together\n", "\n", "Combine these two parts into a `keras.layers.Layer` that can be attached to your model:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:46.089178Z", "iopub.status.busy": "2024-02-07T12:13:46.088913Z", "iopub.status.idle": "2024-02-07T12:13:46.093778Z", "shell.execute_reply": "2024-02-07T12:13:46.093166Z" }, "id": "9Qtz-tv-6nz6" }, "outputs": [], "source": [ "class BertInputProcessor(tf.keras.layers.Layer):\n", " def __init__(self, tokenizer, packer):\n", " super().__init__()\n", " self.tokenizer = tokenizer\n", " self.packer = packer\n", "\n", " def call(self, inputs):\n", " tok1 = self.tokenizer(inputs['sentence1'])\n", " tok2 = self.tokenizer(inputs['sentence2'])\n", "\n", " packed = self.packer([tok1, tok2])\n", "\n", " if 'label' in inputs:\n", " return packed, inputs['label']\n", " else:\n", " return packed" ] }, { "cell_type": "markdown", "metadata": { "id": "rdy9wp499btU" }, "source": [ "But for now just apply it to the dataset using `Dataset.map`, since the dataset you loaded from TFDS is a `tf.data.Dataset` object:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:46.097059Z", "iopub.status.busy": "2024-02-07T12:13:46.096405Z", "iopub.status.idle": "2024-02-07T12:13:46.101741Z", "shell.execute_reply": "2024-02-07T12:13:46.101118Z" }, "id": "qmyh76AL7VAs" }, "outputs": [], "source": [ "bert_inputs_processor = BertInputProcessor(tokenizer, packer)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:46.104444Z", "iopub.status.busy": "2024-02-07T12:13:46.104206Z", "iopub.status.idle": "2024-02-07T12:13:48.928393Z", "shell.execute_reply": "2024-02-07T12:13:48.927565Z" }, "id": "B8SSCtDe9MCk" }, "outputs": [], "source": [ "glue_train = glue['train'].map(bert_inputs_processor).prefetch(1)" ] }, { "cell_type": "markdown", "metadata": { "id": "KXpiDosO9rkY" }, "source": [ "Here is an example batch from the processed dataset:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:48.932993Z", "iopub.status.busy": "2024-02-07T12:13:48.932436Z", "iopub.status.idle": "2024-02-07T12:13:49.762394Z", "shell.execute_reply": "2024-02-07T12:13:49.761624Z" }, "id": "ffNvDE6t9rP-" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-02-07 12:13:49.744645: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n" ] } ], "source": [ "example_inputs, example_labels = next(iter(glue_train))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:49.765762Z", "iopub.status.busy": "2024-02-07T12:13:49.765482Z", "iopub.status.idle": "2024-02-07T12:13:49.771346Z", "shell.execute_reply": "2024-02-07T12:13:49.770763Z" }, "id": "5sxtTuUi-bXt" }, "outputs": [ { "data": { "text/plain": [ "{'input_word_ids': ,\n", " 'input_mask': ,\n", " 'input_type_ids': }" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example_inputs" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:49.774349Z", "iopub.status.busy": "2024-02-07T12:13:49.774105Z", "iopub.status.idle": "2024-02-07T12:13:49.778545Z", "shell.execute_reply": "2024-02-07T12:13:49.777900Z" }, "id": "wP4z_-9a-dFk" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example_labels" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:49.781358Z", "iopub.status.busy": "2024-02-07T12:13:49.781127Z", "iopub.status.idle": "2024-02-07T12:13:49.785087Z", "shell.execute_reply": "2024-02-07T12:13:49.784434Z" }, "id": "jyjTdGpFhO_1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "input_word_ids shape: (32, 128)\n", "input_mask shape: (32, 128)\n", "input_type_ids shape: (32, 128)\n", "labels shape: (32,)\n" ] } ], "source": [ "for key, value in example_inputs.items():\n", " print(f'{key:15s} shape: {value.shape}')\n", "\n", "print(f'{\"labels\":15s} shape: {example_labels.shape}')" ] }, { "cell_type": "markdown", "metadata": { "id": "mkGHN_FK-50U" }, "source": [ "The `input_word_ids` contain the token IDs:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:49.788167Z", "iopub.status.busy": "2024-02-07T12:13:49.787937Z", "iopub.status.idle": "2024-02-07T12:13:49.807029Z", "shell.execute_reply": "2024-02-07T12:13:49.806351Z" }, "id": "eGL1_ktWLcgF" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.pcolormesh(example_inputs['input_word_ids'])" ] }, { "cell_type": "markdown", "metadata": { "id": "ulNZ4U96-8JZ" }, "source": [ "The mask allows the model to cleanly differentiate between the content and the padding. The mask has the same shape as the `input_word_ids`, and contains a `1` anywhere the `input_word_ids` is not padding." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:49.810372Z", "iopub.status.busy": "2024-02-07T12:13:49.809791Z", "iopub.status.idle": "2024-02-07T12:13:49.815225Z", "shell.execute_reply": "2024-02-07T12:13:49.814701Z" }, "id": "zB7mW7DGK3rW" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.pcolormesh(example_inputs['input_mask'])" ] }, { "cell_type": "markdown", "metadata": { "id": "rxLenwAvCkBf" }, "source": [ "The \"input type\" also has the same shape, but inside the non-padded region, contains a `0` or a `1` indicating which sentence the token is a part of." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:49.818418Z", "iopub.status.busy": "2024-02-07T12:13:49.817863Z", "iopub.status.idle": "2024-02-07T12:13:49.823430Z", "shell.execute_reply": "2024-02-07T12:13:49.822871Z" }, "id": "2CetH_5C9P2m" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.pcolormesh(example_inputs['input_type_ids'])" ] }, { "cell_type": "markdown", "metadata": { "id": "pxHHeyei_sb9" }, "source": [ "Apply the same preprocessing to the validation and test subsets of the GLUE MRPC dataset:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:49.826653Z", "iopub.status.busy": "2024-02-07T12:13:49.826194Z", "iopub.status.idle": "2024-02-07T12:13:52.214690Z", "shell.execute_reply": "2024-02-07T12:13:52.213887Z" }, "id": "yuLKxf6zHxw-" }, "outputs": [], "source": [ "glue_validation = glue['validation'].map(bert_inputs_processor).prefetch(1)\n", "glue_test = glue['test'].map(bert_inputs_processor).prefetch(1)" ] }, { "cell_type": "markdown", "metadata": { "id": "FSwymsbkbLDA" }, "source": [ "## Build, train and export the model" ] }, { "cell_type": "markdown", "metadata": { "id": "bxxO3pJCEM9p" }, "source": [ "Now that you have formatted the data as expected, you can start working on building and training the model." ] }, { "cell_type": "markdown", "metadata": { "id": "Efrj3Cn1kLAp" }, "source": [ "### Build the model\n" ] }, { "cell_type": "markdown", "metadata": { "id": "xxpOY5r2Ayq6" }, "source": [ "The first step is to download the configuration file—`config_dict`—for the pre-trained BERT model:\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:52.219461Z", "iopub.status.busy": "2024-02-07T12:13:52.218750Z", "iopub.status.idle": "2024-02-07T12:13:52.427713Z", "shell.execute_reply": "2024-02-07T12:13:52.427057Z" }, "id": "v7ap0BONSJuz" }, "outputs": [ { "data": { "text/plain": [ "{'attention_probs_dropout_prob': 0.1,\n", " 'hidden_act': 'gelu',\n", " 'hidden_dropout_prob': 0.1,\n", " 'hidden_size': 768,\n", " 'initializer_range': 0.02,\n", " 'intermediate_size': 3072,\n", " 'max_position_embeddings': 512,\n", " 'num_attention_heads': 12,\n", " 'num_hidden_layers': 12,\n", " 'type_vocab_size': 2,\n", " 'vocab_size': 30522}" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import json\n", "\n", "bert_config_file = os.path.join(gs_folder_bert, \"bert_config.json\")\n", "config_dict = json.loads(tf.io.gfile.GFile(bert_config_file).read())\n", "config_dict" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:52.430935Z", "iopub.status.busy": "2024-02-07T12:13:52.430658Z", "iopub.status.idle": "2024-02-07T12:13:52.442793Z", "shell.execute_reply": "2024-02-07T12:13:52.442138Z" }, "id": "pKaEaKJSX85J" }, "outputs": [], "source": [ "encoder_config = tfm.nlp.encoders.EncoderConfig({\n", " 'type':'bert',\n", " 'bert': config_dict\n", "})" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:52.445861Z", "iopub.status.busy": "2024-02-07T12:13:52.445619Z", "iopub.status.idle": "2024-02-07T12:13:54.565534Z", "shell.execute_reply": "2024-02-07T12:13:54.564867Z" }, "id": "LbgzWukNSqOS" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bert_encoder = tfm.nlp.encoders.build_encoder(encoder_config)\n", "bert_encoder" ] }, { "cell_type": "markdown", "metadata": { "id": "96ldxDSwkVkj" }, "source": [ "The configuration file defines the core BERT model from the Model Garden, which is a Keras model that predicts the outputs of `num_classes` from the inputs with maximum sequence length `max_seq_length`." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:54.568700Z", "iopub.status.busy": "2024-02-07T12:13:54.568453Z", "iopub.status.idle": "2024-02-07T12:13:55.354398Z", "shell.execute_reply": "2024-02-07T12:13:55.353639Z" }, "id": "cH682__U0FBv" }, "outputs": [], "source": [ "bert_classifier = tfm.nlp.models.BertClassifier(network=bert_encoder, num_classes=2)" ] }, { "cell_type": "markdown", "metadata": { "id": "sFmVG4SKZAw8" }, "source": [ "Run it on a test batch of data 10 examples from the training set. The output is the logits for the two classes:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:55.358116Z", "iopub.status.busy": "2024-02-07T12:13:55.357861Z", "iopub.status.idle": "2024-02-07T12:13:57.385426Z", "shell.execute_reply": "2024-02-07T12:13:57.384532Z" }, "id": "VTjgPbp4ZDKo" }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.08335936, 1.1473498 ],\n", " [ 1.3190541 , 1.3408866 ],\n", " [ 0.19908446, 0.7913456 ],\n", " [ 0.48186374, 1.2114024 ],\n", " [ 0.9708527 , 0.7837988 ],\n", " [ 0.25541633, 0.76591694],\n", " [ 1.3683597 , 1.0795705 ],\n", " [ 0.11288509, 1.1301354 ],\n", " [-0.02536219, 0.4678782 ],\n", " [ 0.9831672 , 0.538211 ]], dtype=float32)" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bert_classifier(\n", " example_inputs, training=True).numpy()[:10]" ] }, { "cell_type": "markdown", "metadata": { "id": "Q0NTdwZsQK8n" }, "source": [ "The `TransformerEncoder` in the center of the classifier above **is** the `bert_encoder`.\n", "\n", "If you inspect the encoder, notice the stack of `Transformer` layers connected to those same three inputs:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:57.389185Z", "iopub.status.busy": "2024-02-07T12:13:57.388547Z", "iopub.status.idle": "2024-02-07T12:13:57.599083Z", "shell.execute_reply": "2024-02-07T12:13:57.598191Z" }, "id": "8L__-erBwLIQ" }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.keras.utils.plot_model(bert_encoder, show_shapes=True, dpi=48)" ] }, { "cell_type": "markdown", "metadata": { "id": "mKAvkQc3heSy" }, "source": [ "### Restore the encoder weights\n", "\n", "When built, the encoder is randomly initialized. Restore the encoder's weights from the checkpoint:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:13:57.604564Z", "iopub.status.busy": "2024-02-07T12:13:57.603838Z", "iopub.status.idle": "2024-02-07T12:14:03.634178Z", "shell.execute_reply": "2024-02-07T12:14:03.633499Z" }, "id": "97Ll2Gichd_Y" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "checkpoint = tf.train.Checkpoint(encoder=bert_encoder)\n", "checkpoint.read(\n", " os.path.join(gs_folder_bert, 'bert_model.ckpt')).assert_consumed()" ] }, { "cell_type": "markdown", "metadata": { "id": "2oHOql35k3Dd" }, "source": [ "Note: The pre-trained `TransformerEncoder` is also available on [TensorFlow Hub](https://tensorflow.org/hub). Go to the [TF Hub appendix](#hub_bert) for details." ] }, { "cell_type": "markdown", "metadata": { "id": "115caFLMk-_l" }, "source": [ "### Set up the optimizer\n", "\n", "BERT typically uses the Adam optimizer with weight decay—[AdamW](https://arxiv.org/abs/1711.05101) (`tf.keras.optimizers.experimental.AdamW`).\n", "It also employs a learning rate schedule that first warms up from 0 and then decays to 0:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:14:03.637664Z", "iopub.status.busy": "2024-02-07T12:14:03.637165Z", "iopub.status.idle": "2024-02-07T12:14:03.641482Z", "shell.execute_reply": "2024-02-07T12:14:03.640804Z" }, "id": "c0jBycPDtkxR" }, "outputs": [], "source": [ "# Set up epochs and steps\n", "epochs = 5\n", "batch_size = 32\n", "eval_batch_size = 32\n", "\n", "train_data_size = info.splits['train'].num_examples\n", "steps_per_epoch = int(train_data_size / batch_size)\n", "num_train_steps = steps_per_epoch * epochs\n", "warmup_steps = int(0.1 * num_train_steps)\n", "initial_learning_rate=2e-5" ] }, { "cell_type": "markdown", "metadata": { "id": "GFankgHK0Rvh" }, "source": [ "Linear decay from `initial_learning_rate` to zero over `num_train_steps`." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:14:03.644667Z", "iopub.status.busy": "2024-02-07T12:14:03.644204Z", "iopub.status.idle": "2024-02-07T12:14:03.647429Z", "shell.execute_reply": "2024-02-07T12:14:03.646874Z" }, "id": "qWSyT8P2j4mV" }, "outputs": [], "source": [ "linear_decay = tf.keras.optimizers.schedules.PolynomialDecay(\n", " initial_learning_rate=initial_learning_rate,\n", " end_learning_rate=0,\n", " decay_steps=num_train_steps)" ] }, { "cell_type": "markdown", "metadata": { "id": "anZPZPAP0Y3n" }, "source": [ "Warmup to that value over `warmup_steps`:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:14:03.650510Z", "iopub.status.busy": "2024-02-07T12:14:03.649925Z", "iopub.status.idle": "2024-02-07T12:14:03.977727Z", "shell.execute_reply": "2024-02-07T12:14:03.976930Z" }, "id": "z_AsVCiRkoN1" }, "outputs": [], "source": [ "warmup_schedule = tfm.optimization.lr_schedule.LinearWarmup(\n", " warmup_learning_rate = 0,\n", " after_warmup_lr_sched = linear_decay,\n", " warmup_steps = warmup_steps\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "arfbaK6t0kH_" }, "source": [ "The overall schedule looks like this:" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:14:03.981948Z", "iopub.status.busy": "2024-02-07T12:14:03.981455Z", "iopub.status.idle": "2024-02-07T12:14:06.390315Z", "shell.execute_reply": "2024-02-07T12:14:06.389585Z" }, "id": "rYZGunhqbGUZ" }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Learning rate')" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = tf.linspace(0, num_train_steps, 1001)\n", "y = [warmup_schedule(xi) for xi in x]\n", "plt.plot(x,y)\n", "plt.xlabel('Train step')\n", "plt.ylabel('Learning rate')" ] }, { "cell_type": "markdown", "metadata": { "id": "bjsmG_fm0opn" }, "source": [ "Use `tf.keras.optimizers.experimental.AdamW` to instantiate the optimizer with that schedule:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:14:06.394147Z", "iopub.status.busy": "2024-02-07T12:14:06.393411Z", "iopub.status.idle": "2024-02-07T12:14:06.404175Z", "shell.execute_reply": "2024-02-07T12:14:06.403499Z" }, "id": "R8pTNuKIw1dA" }, "outputs": [], "source": [ "optimizer = tf.keras.optimizers.experimental.Adam(\n", " learning_rate = warmup_schedule)" ] }, { "cell_type": "markdown", "metadata": { "id": "78FEUOOEkoP0" }, "source": [ "### Train the model" ] }, { "cell_type": "markdown", "metadata": { "id": "OTNcA0O0nSq9" }, "source": [ "Set the metric as accuracy and the loss as sparse categorical cross-entropy. Then, compile and train the BERT classifier:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:14:06.407748Z", "iopub.status.busy": "2024-02-07T12:14:06.407284Z", "iopub.status.idle": "2024-02-07T12:14:06.439234Z", "shell.execute_reply": "2024-02-07T12:14:06.438664Z" }, "id": "d5FeL0b6j7ky" }, "outputs": [], "source": [ "metrics = [tf.keras.metrics.SparseCategoricalAccuracy('accuracy', dtype=tf.float32)]\n", "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n", "\n", "bert_classifier.compile(\n", " optimizer=optimizer,\n", " loss=loss,\n", " metrics=metrics)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:14:06.442600Z", "iopub.status.busy": "2024-02-07T12:14:06.442111Z", "iopub.status.idle": "2024-02-07T12:14:12.441137Z", "shell.execute_reply": "2024-02-07T12:14:12.440404Z" }, "id": "CsrylctIj_Xy" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/13 [=>............................] - 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ETA: 0s - loss: 0.7210 - accuracy: 0.6191" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "115/115 [==============================] - 131s 858ms/step - loss: 0.7210 - accuracy: 0.6191 - val_loss: 0.5249 - val_accuracy: 0.7426\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 2/5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/115 [..............................] - ETA: 1:31 - loss: 0.6034 - accuracy: 0.6250" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2/115 [..............................] - ETA: 1:35 - loss: 0.6939 - accuracy: 0.5312" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3/115 [..............................] - 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val_loss: 0.4766 - val_accuracy: 0.8064\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 3/5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/115 [..............................] - ETA: 1:31 - loss: 0.3891 - accuracy: 0.8438" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2/115 [..............................] - ETA: 1:35 - loss: 0.3922 - accuracy: 0.7969" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3/115 [..............................] - ETA: 1:34 - loss: 0.4364 - accuracy: 0.8125" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4/115 [>.............................] - 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101s 878ms/step - loss: 0.2006 - accuracy: 0.9278 - val_loss: 0.4783 - val_accuracy: 0.8358\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 5/5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/115 [..............................] - ETA: 1:32 - loss: 0.0652 - accuracy: 1.0000" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 2/115 [..............................] - ETA: 1:36 - loss: 0.1590 - accuracy: 0.9375" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 3/115 [..............................] - ETA: 1:34 - loss: 0.1829 - accuracy: 0.9271" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 4/115 [>.............................] - 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This does not match.']\n", " }" ] }, { "cell_type": "markdown", "metadata": { "id": "7ynJibkBRTJF" }, "source": [ "The model should report class `1` \"match\" for the first example and class `0` \"no-match\" for the second:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:09.381136Z", "iopub.status.busy": "2024-02-07T12:23:09.380893Z", "iopub.status.idle": "2024-02-07T12:23:09.620562Z", "shell.execute_reply": "2024-02-07T12:23:09.619973Z" }, "id": "umo0ttrgRYIM" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ex_packed = bert_inputs_processor(my_examples)\n", "my_logits = bert_classifier(ex_packed, training=False)\n", "\n", "result_cls_ids = tf.argmax(my_logits)\n", "result_cls_ids" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:09.623925Z", "iopub.status.busy": "2024-02-07T12:23:09.623422Z", "iopub.status.idle": "2024-02-07T12:23:09.629242Z", "shell.execute_reply": "2024-02-07T12:23:09.628644Z" }, "id": "HNdmOEHKT7e8" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.gather(tf.constant(info.features['label'].names), result_cls_ids)" ] }, { "cell_type": "markdown", "metadata": { "id": "fVo_AnT0l26j" }, "source": [ "### Export the model\n", "\n", "Often the goal of training a model is to _use_ it for something outside of the Python process that created it. You can do this by exporting the model using `tf.saved_model`. (Learn more in the [Using the SavedModel format](https://www.tensorflow.org/guide/saved_model) guide and the [Save and load a model using a distribution strategy](https://www.tensorflow.org/tutorials/distribute/save_and_load) tutorial.)\n", "\n", "First, build a wrapper class to export the model. This wrapper does two things:\n", "\n", "- First, it packages `bert_inputs_processor` and `bert_classifier` together into a single `tf.Module`, so you can export all the functionalities.\n", "- Second, it defines a `tf.function` that implements the end-to-end execution of the model.\n", "\n", "Setting the `input_signature` argument of `tf.function` lets you define a fixed signature for the `tf.function`. This can be less surprising than the default automatic retracing behavior." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:09.632400Z", "iopub.status.busy": "2024-02-07T12:23:09.632156Z", "iopub.status.idle": "2024-02-07T12:23:09.637959Z", "shell.execute_reply": "2024-02-07T12:23:09.637362Z" }, "id": "78h83mlt9wpY" }, "outputs": [], "source": [ "class ExportModel(tf.Module):\n", " def __init__(self, input_processor, classifier):\n", " self.input_processor = input_processor\n", " self.classifier = classifier\n", "\n", " @tf.function(input_signature=[{\n", " 'sentence1': tf.TensorSpec(shape=[None], dtype=tf.string),\n", " 'sentence2': tf.TensorSpec(shape=[None], dtype=tf.string)}])\n", " def __call__(self, inputs):\n", " packed = self.input_processor(inputs)\n", " logits = self.classifier(packed, training=False)\n", " result_cls_ids = tf.argmax(logits)\n", " return {\n", " 'logits': logits,\n", " 'class_id': result_cls_ids,\n", " 'class': tf.gather(\n", " tf.constant(info.features['label'].names),\n", " result_cls_ids)\n", " }" ] }, { "cell_type": "markdown", "metadata": { "id": "qnxysGUfIgFQ" }, "source": [ "Create an instance of this exported model and save it:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:09.641032Z", "iopub.status.busy": "2024-02-07T12:23:09.640794Z", "iopub.status.idle": "2024-02-07T12:23:09.644136Z", "shell.execute_reply": "2024-02-07T12:23:09.643543Z" }, "id": "TmHW9DEFUZ0X" }, "outputs": [], "source": [ "export_model = ExportModel(bert_inputs_processor, bert_classifier)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:09.647121Z", "iopub.status.busy": "2024-02-07T12:23:09.646880Z", "iopub.status.idle": "2024-02-07T12:23:33.065711Z", "shell.execute_reply": "2024-02-07T12:23:33.064931Z" }, "id": "Nl5x6nElZqkP" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpxj846i17_saved_model/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpxj846i17_saved_model/assets\n" ] } ], "source": [ "import tempfile\n", "export_dir=tempfile.mkdtemp(suffix='_saved_model')\n", "tf.saved_model.save(export_model, export_dir=export_dir,\n", " signatures={'serving_default': export_model.__call__})" ] }, { "cell_type": "markdown", "metadata": { "id": "Pd8B5dy-ImDJ" }, "source": [ "Reload the model and compare the results to the original:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:33.078686Z", "iopub.status.busy": "2024-02-07T12:23:33.078245Z", "iopub.status.idle": "2024-02-07T12:23:36.100232Z", "shell.execute_reply": "2024-02-07T12:23:36.099442Z" }, "id": "9cAhHySVXHD5" }, "outputs": [], "source": [ "original_logits = export_model(my_examples)['logits']" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:36.104085Z", "iopub.status.busy": "2024-02-07T12:23:36.103804Z", "iopub.status.idle": "2024-02-07T12:23:44.030731Z", "shell.execute_reply": "2024-02-07T12:23:44.029879Z" }, "id": "H9cAcYwfW2fy" }, "outputs": [], "source": [ "reloaded = tf.saved_model.load(export_dir)\n", "reloaded_logits = reloaded(my_examples)['logits']" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:44.034708Z", "iopub.status.busy": "2024-02-07T12:23:44.034413Z", "iopub.status.idle": "2024-02-07T12:23:44.039645Z", "shell.execute_reply": "2024-02-07T12:23:44.038952Z" }, "id": "y_ACvKPsVUXC" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[-2.7769644 2.3126464]\n", " [ 1.4339567 -1.1664971]]\n", "\n", "[[-2.7769644 2.3126464]\n", " [ 1.4339567 -1.1664971]]\n" ] } ], "source": [ "# The results are identical:\n", "print(original_logits.numpy())\n", "print()\n", "print(reloaded_logits.numpy())" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:44.042686Z", "iopub.status.busy": "2024-02-07T12:23:44.042430Z", "iopub.status.idle": "2024-02-07T12:23:44.047857Z", "shell.execute_reply": "2024-02-07T12:23:44.047054Z" }, "id": "lBlPP20dXPFR" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0\n" ] } ], "source": [ "print(np.mean(abs(original_logits - reloaded_logits)))" ] }, { "cell_type": "markdown", "metadata": { "id": "CPsg7dZwfBM2" }, "source": [ "Congratulations! You've used `tensorflow_models` to build a BERT-classifier, train it, and export it for later use." ] }, { "cell_type": "markdown", "metadata": { "id": "eQceYqRFT_Eg" }, "source": [ "## Optional: BERT on TF Hub" ] }, { "cell_type": "markdown", "metadata": { "id": "QbklKt-w_CiI" }, "source": [ "\n", "\n", "\n", "You can get the BERT model off the shelf from [TF Hub](https://tfhub.dev/). There are [many versions available along with their input preprocessors](https://tfhub.dev/google/collections/bert/1).\n", "\n", "This example uses [a small version of BERT from TF Hub](https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-128_A-2/2) that was pre-trained using the English Wikipedia and BooksCorpus datasets, similar to the [original implementation](https://arxiv.org/abs/1908.08962) (Turc et al., 2019).\n", "\n", "Start by importing TF Hub:" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:44.051185Z", "iopub.status.busy": "2024-02-07T12:23:44.050923Z", "iopub.status.idle": "2024-02-07T12:23:44.054078Z", "shell.execute_reply": "2024-02-07T12:23:44.053416Z" }, "id": "GDWrHm0BGpbX" }, "outputs": [], "source": [ "import tensorflow_hub as hub" ] }, { "cell_type": "markdown", "metadata": { "id": "f02f38f83ac4" }, "source": [ "Select the input preprocessor and the model from TF Hub and wrap them as `hub.KerasLayer` layers:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:44.057113Z", "iopub.status.busy": "2024-02-07T12:23:44.056852Z", "iopub.status.idle": "2024-02-07T12:23:47.779099Z", "shell.execute_reply": "2024-02-07T12:23:47.778420Z" }, "id": "lo6479At4sP1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Hub encoder has 39 trainable variables\n" ] } ], "source": [ "# Always make sure you use the right preprocessor.\n", "hub_preprocessor = hub.KerasLayer(\n", " \"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3\")\n", "\n", "# This is a really small BERT.\n", "hub_encoder = hub.KerasLayer(f\"https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-128_A-2/2\",\n", " trainable=True)\n", "\n", "print(f\"The Hub encoder has {len(hub_encoder.trainable_variables)} trainable variables\")" ] }, { "cell_type": "markdown", "metadata": { "id": "iTzF574wivQv" }, "source": [ "Test run the preprocessor on a batch of data:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:47.782583Z", "iopub.status.busy": "2024-02-07T12:23:47.782319Z", "iopub.status.idle": "2024-02-07T12:23:48.264906Z", "shell.execute_reply": "2024-02-07T12:23:48.264231Z" }, "id": "GOASSKR5R3-N" }, "outputs": [ { "data": { "text/plain": [ "{'input_type_ids': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32),\n", " 'input_word_ids': array([ 101, 7592, 23435, 12314, 999, 102, 0, 0, 0,\n", " 0], dtype=int32),\n", " 'input_mask': array([1, 1, 1, 1, 1, 1, 0, 0, 0, 0], dtype=int32)}" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hub_inputs = hub_preprocessor(['Hello TensorFlow!'])\n", "{key: value[0, :10].numpy() for key, value in hub_inputs.items()} " ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:48.268250Z", "iopub.status.busy": "2024-02-07T12:23:48.267990Z", "iopub.status.idle": "2024-02-07T12:23:48.417225Z", "shell.execute_reply": "2024-02-07T12:23:48.416535Z" }, "id": "XEcYrCR45Uwo" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Pooled output shape: (1, 128)\n", "Sequence output shape: (1, 128, 128)\n" ] } ], "source": [ "result = hub_encoder(\n", " inputs=hub_inputs,\n", " training=False,\n", ")\n", "\n", "print(\"Pooled output shape:\", result['pooled_output'].shape)\n", "print(\"Sequence output shape:\", result['sequence_output'].shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "cjojn8SmLSRI" }, "source": [ "At this point, it would be simple to add a classification head yourself.\n", "\n", "The Model Garden `tfm.nlp.models.BertClassifier` class can also build a classifier onto the TF Hub encoder:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:48.420603Z", "iopub.status.busy": "2024-02-07T12:23:48.420340Z", "iopub.status.idle": "2024-02-07T12:23:49.129052Z", "shell.execute_reply": "2024-02-07T12:23:49.128372Z" }, "id": "9nTDaApyLR70" }, "outputs": [], "source": [ "hub_classifier = tfm.nlp.models.BertClassifier(\n", " bert_encoder,\n", " num_classes=2,\n", " dropout_rate=0.1,\n", " initializer=tf.keras.initializers.TruncatedNormal(\n", " stddev=0.02))" ] }, { "cell_type": "markdown", "metadata": { "id": "xMJX3wV0_v7I" }, "source": [ "The one downside to loading this model from TF Hub is that the structure of internal Keras layers is not restored. This makes it more difficult to inspect or modify the model.\n", "\n", "The BERT encoder model—`hub_classifier`—is now a single layer." ] }, { "cell_type": "markdown", "metadata": { "id": "u_IqwXjRV1vd" }, "source": [ "For concrete examples of this approach, refer to [Solve Glue tasks using the BERT](https://www.tensorflow.org/text/tutorials/bert_glue)." ] }, { "cell_type": "markdown", "metadata": { "id": "ji3tdLz101km" }, "source": [ "## Optional: Optimizer `config`s\n", "\n", "The `tensorflow_models` package defines serializable `config` classes that describe how to build the live objects. Earlier in this tutorial, you built the optimizer manually.\n", "\n", "The configuration below describes an (almost) identical optimizer built by the `optimizer_factory.OptimizerFactory`:" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:49.133079Z", "iopub.status.busy": "2024-02-07T12:23:49.132822Z", "iopub.status.idle": "2024-02-07T12:23:49.147489Z", "shell.execute_reply": "2024-02-07T12:23:49.146857Z" }, "id": "Fdb9C1ontnH_" }, "outputs": [], "source": [ "optimization_config = tfm.optimization.OptimizationConfig(\n", " optimizer=tfm.optimization.OptimizerConfig(\n", " type = \"adam\"),\n", " learning_rate = tfm.optimization.LrConfig(\n", " type='polynomial',\n", " polynomial=tfm.optimization.PolynomialLrConfig(\n", " initial_learning_rate=2e-5,\n", " end_learning_rate=0.0,\n", " decay_steps=num_train_steps)),\n", " warmup = tfm.optimization.WarmupConfig(\n", " type='linear',\n", " linear=tfm.optimization.LinearWarmupConfig(warmup_steps=warmup_steps)\n", " ))\n", "\n", "\n", "fac = tfm.optimization.optimizer_factory.OptimizerFactory(optimization_config)\n", "lr = fac.build_learning_rate()\n", "optimizer = fac.build_optimizer(lr=lr)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:49.150545Z", "iopub.status.busy": "2024-02-07T12:23:49.150324Z", "iopub.status.idle": "2024-02-07T12:23:50.717684Z", "shell.execute_reply": "2024-02-07T12:23:50.716936Z" }, "id": "Rp7R1hBfv5HG" }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Learning rate')" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = tf.linspace(0, num_train_steps, 1001).numpy()\n", "y = [lr(xi) for xi in x]\n", "plt.plot(x,y)\n", "plt.xlabel('Train step')\n", "plt.ylabel('Learning rate')" ] }, { "cell_type": "markdown", "metadata": { "id": "ywn5miD_dnuh" }, "source": [ "The advantage of using `config` objects is that they don't contain any complicated TensorFlow objects, and can be easily serialized to JSON, and rebuilt. Here's the JSON for the above `tfm.optimization.OptimizationConfig`:" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:50.720850Z", "iopub.status.busy": "2024-02-07T12:23:50.720592Z", "iopub.status.idle": "2024-02-07T12:23:50.725792Z", "shell.execute_reply": "2024-02-07T12:23:50.725103Z" }, "id": "zo5RV5lud81Y" }, "outputs": [ { "data": { "text/plain": [ "{'optimizer': {'type': 'adam',\n", " 'adam': {'clipnorm': None,\n", " 'clipvalue': None,\n", " 'global_clipnorm': None,\n", " 'name': 'Adam',\n", " 'beta_1': 0.9,\n", " 'beta_2': 0.999,\n", " 'epsilon': 1e-07,\n", " 'amsgrad': False}},\n", " 'ema': None,\n", " 'learning_rate': {'type': 'polynomial',\n", " 'polynomial': {'name': 'PolynomialDecay',\n", " 'initial_learning_rate': 2e-05,\n", " 'decay_steps': 570,\n", " 'end_learning_rate': 0.0,\n", " 'power': 1.0,\n", " 'cycle': False,\n", " 'offset': 0}},\n", " 'warmup': {'type': 'linear',\n", " 'linear': {'name': 'linear', 'warmup_learning_rate': 0, 'warmup_steps': 57}}}" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "optimization_config = optimization_config.as_dict()\n", "optimization_config" ] }, { "cell_type": "markdown", "metadata": { "id": "Z6qPXPEhekkd" }, "source": [ "The `tfm.optimization.optimizer_factory.OptimizerFactory` can just as easily build the optimizer from the JSON dictionary:" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "execution": { "iopub.execute_input": "2024-02-07T12:23:50.728554Z", "iopub.status.busy": "2024-02-07T12:23:50.728325Z", "iopub.status.idle": "2024-02-07T12:23:50.741371Z", "shell.execute_reply": "2024-02-07T12:23:50.740786Z" }, "id": "p-bYrvfMYsxp" }, "outputs": [], "source": [ "fac = tfm.optimization.optimizer_factory.OptimizerFactory(\n", " tfm.optimization.OptimizationConfig(optimization_config))\n", "lr = fac.build_learning_rate()\n", "optimizer = fac.build_optimizer(lr=lr)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "name": "fine_tune_bert.ipynb", "private_outputs": true, "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 0 }