{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "h2tmtetBZsTE" }, "source": [ "##### Copyright 2023 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DsTOPNzMZsTT" }, "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": "YRlNzHiLN87u" }, "source": [ "# Train Perceiver model for down streaming tasks\n", "\n", "This tutorial demonstrates how to use [Perciever](https://arxiv.org/abs/2107.14795) model for down streaming tasks using Tensorflow Model Garden.\n", "\n", "[Tensorflow Model Garden](https://www.tensorflow.org/tfmodels) contains a collection of state-of-the-art models, implemented with TensorFlow's high-level APIs. The implementations demonstrate the best practices for modeling, letting users to take full advantage of TensorFlow for their research and product development." ] }, { "cell_type": "markdown", "metadata": { "id": "pRFcanY4y5lV" }, "source": [ "\n", "## Clone models repository" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IPIuUUMxy5Ei" }, "outputs": [], "source": [ "!git clone -q https://github.com/tensorflow/models.git" ] }, { "cell_type": "markdown", "metadata": { "id": "B7OUNfFxymIm" }, "source": [ "## Install necessary dependencies" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 97312, "status": "ok", "timestamp": 1689009983377, "user": { "displayName": "Siva Sravana Kumar Neeli", "userId": "06669604936988620923" }, "user_tz": 420 }, "id": "bpSKXUuQpVaW", "outputId": "7d0006a4-68bf-4aad-d49f-c93a115b32fe" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m524.1/524.1 MB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m86.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m95.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m440.8/440.8 kB\u001b[0m \u001b[31m44.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m241.2/241.2 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m175.1/175.1 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.6/43.6 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m35.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.9/118.9 kB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.6/17.6 MB\u001b[0m \u001b[31m84.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Building wheel for pyyaml (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[33m WARNING: The scripts f2py, f2py3 and f2py3.10 are installed in '/root/.local/bin' which is not on PATH.\n", " Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n", "\u001b[0m\u001b[33m WARNING: The script sacrebleu is installed in '/root/.local/bin' which is not on PATH.\n", " Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n", "\u001b[0m\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.\n", "numba 0.56.4 requires numpy\u003c1.24,\u003e=1.18, but you have numpy 1.25.1 which is incompatible.\n", "tensorflow 2.13.0 requires numpy\u003c=1.24.3,\u003e=1.22, but you have numpy 1.25.1 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.5/6.5 MB\u001b[0m \u001b[31m48.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.3/17.3 MB\u001b[0m \u001b[31m14.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h\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.\n", "numba 0.56.4 requires numpy\u003c1.24,\u003e=1.18, but you have numpy 1.24.3 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0m" ] } ], "source": [ "!pip install -q tensorflow==2.13.0\n", "!pip install -q -U tensorflow_datasets\n", "!pip install -q --user -r models/official/requirements.txt\n", "!pip install -q tensorflow-text==2.13.0" ] }, { "cell_type": "markdown", "metadata": { "id": "SJ50D6KvW6cK" }, "source": [ "**Note**: Please restart the runtime once libraries are installed" ] }, { "cell_type": "markdown", "metadata": { "id": "S2qSVWakysDC" }, "source": [ "## Please set the Python path with `os.environ` for models directory" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vnaE50DGAg5H" }, "outputs": [], "source": [ "import os\n", "os.environ['PYTHONPATH'] += \":/content/models\"\n", "\n", "import sys\n", "sys.path.append(\"/content/models\")" ] }, { "cell_type": "markdown", "metadata": { "id": "5BPkQZBCzMSm" }, "source": [ "## Import necessary libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 16820, "status": "ok", "timestamp": 1689010089176, "user": { "displayName": "Siva Sravana Kumar Neeli", "userId": "06669604936988620923" }, "user_tz": 420 }, "id": "moOdkRC01sdZ", "outputId": "ec50aab3-57c3-4b9e-dd9f-138338111e98" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.13.0\n" ] } ], "source": [ "import os\n", "import pprint\n", "import tensorflow as tf\n", "import tensorflow_datasets as tfds\n", "\n", "from IPython import display\n", "from official.core import task_factory\n", "from official.core import train_lib\n", "from official.core import train_utils\n", "from official.projects.perceiver.tasks import sentence_prediction\n", "from official.projects.perceiver.configs import perceiver as exp_cfg\n", "from official.nlp.modeling.layers import FastWordpieceBertTokenizer\n", "from official.nlp.modeling.layers import BertPackInputs\n", "\n", "\n", "pp = pprint.PrettyPrinter(indent=4) # Set Pretty Print Indentation\n", "print(tf.__version__) # Check the version of tensorflow used" ] }, { "cell_type": "markdown", "metadata": { "id": "ZZ4ZNn3ezW0c" }, "source": [ "## Download `glue/mrpc` dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 931, "referenced_widgets": [ "6c4cee474109471886f7dff896ff6d33", "a8e2948404bf4c54a7c3226e6d304dfa", "e364594b9e2446bab3c4768fb390a74b", "b23a6acacbbf4267b48a59db16e7c4e6", "eb67f79b4670428a9a350bc65a509907", "1a13e1780d5d4679abf7b6c818ef9479", "0f0adf4fa05745eebf6e5a968119cdea", "638768d270a24fb9850d7ce8a929ec59", "37bc2f8195c44cd2b024c5e3135ff390", "644a43e4d9e54fd3b93fdce77434c102", "14b3d59bfd394eb88c898cd0ee61ff53", "9b2125597d1c4f48b951ea64df65aaaf", "649a46c0be87434fbc8be9f18e68d41a", "12d14e22b9e641efba0adbf41ad6438d", "523c5f3f84d5423c8f71235ddfc8d5e1", "93fc8dc08be04e10aa1d888e819370d9", "bf689daf2c8e45068470aebd95336a3a", "a7e39eb98b3548ed874effd391f865cd", "d95fbc1de8354300b15afd398c79e336", "21648c51936d4bdf9ecd068db0b0f4c9", "d41ee155cbb0450980c04d22ac8c929e", "5876534149a64daaba4b897bff309963", "1377ec55510c471f957c5be3042ef36c", "b512af1a5c394753a730dd607333424a", "2d9b2bcd183e4a84b592f10e4b5b5661", "af45c55d868841118adfea916a722bee", "41bade4b10744d25bd5b40b6792b05f5", "d3e660d14c394f49868930d71a95384c", "b2c10377d6e34c76a9b89e41fb897f63", "5c6e53e251b04cb7b8a92f32108f9a02", "5e7cf7f3dc6e4335b58322fa7264ae97", "e7fd108139b3439786ac18511f2247de", "0572513797a24275812f60488e034892", "d8f3a76c74374d4ca022aba7b184714a", "aeab4fa4eead43c897db9ecb90e1dfb3", "466f51b0011d458baca0e9561c6204c6", "0a934c9980ed49e583a3f1a52c77695a", "e69d91b41ade449bb4a972b84997924a", "773ada8d21b34faf9999e358cd2dc372", "9d9a9bd520684f73ad60a927efaa40c0", "bd4d39f87d4c432398cf8d5ac43058d5", "40fd6b4a0c3b4e959ecef49478eabc32", "6d38ee13f6ab4cf8893b718d324301f4", "b56e6022dbc648629c65d596e69f12af", "58dad5720b0f404fb3b898dba90055ad", "4a1ebf7b14c44dae9345ca78b3a274b0", "200d6fb5129544cebc8f396874082879", "c84bc3497fdc4d36b4a15ad9e48c6875", "1e096cb57ae64aeb8d5ffc4c7522db29", "5a734cfa349d4a90848b19935f475c21", "f10ea5e8e2a0403ea5eca98da3c6354e", "d74ab8fa5be449e9a05068307f754ac8", "68f3b53a76e041cea3198ba56585e4d1", "5154599b265e42b2bfb1f54d9b665451", "2392d845775a48488d264dd0201b9063", "524dc0d66c4047c6a9d3304cc0a87f33", "228e8ce9e801480aae3afa14d2c4ffda", "692817128ce646dc83879a24395107f1", "8adde4af9df84c4893367261e3e57112", "b976b945c1694c1baeef4aaedcdb965e", "fa0162c12521438698ae678f6e63380e", "0cd494d8cbfd49fe825799a0c356a2d4", "ca8375042d1f46dabf50718e75faeb44", "04911865ef3a4e859308087a8c66e58a", "9dafefc83e444c91957ed6a9b2417c40", "5e40195e05924ed0a57ff7547720d654", "10a08d86c6c049c7b4b2231db6207526", "8c6a79d8c9ec49649204a4845043bf59", "0c0213ec610342c483655a43caab35df", "2d37688bd1cd4a729245dfb78a15b484", "fdec6c12ccae4f5c8595577657271a97", "2b0a72fa0ba2408c8f9cd22011513377", "07aa444924644d5fa4258dff62d47c18", "6330bb99a63942919f485e56ce93df21", "befb207161d64e4ba48e7c62295eb73c", "a1a243e0250b4d9a84b1a9f7c1dfb29b", "6b6e187de14b4833a69ed92fc2e51feb", "bc072d37e96a4e1d80817a3b9eefffa5", "2044e66365cd4beb9f33ebe873114ccd", "8caebb7b5a9a422288fb8f3e93d25fd8", "482f773e56244fccab80ecdd83dfe177", "053df80c139441fbbe46f34d93ef9961", "49e0e2bac1674e27b7989b0b5640223f", "e8bd8359b444412386d547807846e24b", "64afa7c458604b3d9a3a768e49b5d5ef", "b6b80c5a9618457dbc665bcb46f2cd74", "fcfbf00603934e1493583ffd47483f30", "cb1b27446b024b16b3bcb81109f20e18", "e3caa87a46f948038953eb53ee9eb49c", "b5070d07f199417eb5efa1c71d88f5a3", "cf8dd28cdcbf4bedba5556d808b5416e", "fc22d54cc8364265ae2a3b77fde7dfe1", "66f22cd19edb4f02a9a6c7b28eb9da59", "9f8d4c9b27b14a54aae171509c80b323", "0389b5e7bd2f4ad9a040f9a4d48af95c", "d476f68cac2c4c84a06ae7d9112f1ed5", "ded3625ff4454025a41bb449d3b59629", "271ab31a75424942a0f39e02885d80a5", "5139fd5ddd5141aa8ecfc436813a1c60" ] }, "executionInfo": { "elapsed": 5649, "status": "ok", "timestamp": 1689010130551, "user": { "displayName": "Siva Sravana Kumar Neeli", "userId": "06669604936988620923" }, "user_tz": 420 }, "id": "izlrDcj62Hlh", "outputId": "c4009825-4f49-41a3-d774-29166d1eb352" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading and preparing dataset 1.43 MiB (download: 1.43 MiB, generated: 1.74 MiB, total: 3.17 MiB) to /root/tensorflow_datasets/glue/mrpc/2.0.0...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6c4cee474109471886f7dff896ff6d33", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Dl Completed...: 0 url [00:00, ? url/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9b2125597d1c4f48b951ea64df65aaaf", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Dl Size...: 0 MiB [00:00, ? MiB/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1377ec55510c471f957c5be3042ef36c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating splits...: 0%| | 0/3 [00:00\u003c?, ? splits/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d8f3a76c74374d4ca022aba7b184714a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating train examples...: 0%| | 0/3668 [00:00\u003c?, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "58dad5720b0f404fb3b898dba90055ad", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Shuffling /root/tensorflow_datasets/glue/mrpc/2.0.0.incompleteGFRJBN/glue-train.tfrecord*...: 0%| |…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "524dc0d66c4047c6a9d3304cc0a87f33", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating validation examples...: 0%| | 0/408 [00:00\u003c?, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "10a08d86c6c049c7b4b2231db6207526", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Shuffling /root/tensorflow_datasets/glue/mrpc/2.0.0.incompleteGFRJBN/glue-validation.tfrecord*...: 0%| …" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bc072d37e96a4e1d80817a3b9eefffa5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating test examples...: 0%| | 0/1725 [00:00\u003c?, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e3caa87a46f948038953eb53ee9eb49c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Shuffling /root/tensorflow_datasets/glue/mrpc/2.0.0.incompleteGFRJBN/glue-test.tfrecord*...: 0%| | …" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Dataset glue downloaded and prepared to /root/tensorflow_datasets/glue/mrpc/2.0.0. Subsequent calls will reuse this data.\n" ] }, { "data": { "text/plain": [ "tfds.core.DatasetInfo(\n", " name='glue',\n", " full_name='glue/mrpc/2.0.0',\n", " description=\"\"\"\n", " GLUE, the General Language Understanding Evaluation benchmark\n", " (https://gluebenchmark.com/) is a collection of resources for training,\n", " evaluating, and analyzing natural language understanding systems.\n", " \"\"\",\n", " config_description=\"\"\"\n", " The Microsoft Research Paraphrase Corpus (Dolan \u0026 Brockett, 2005) is a corpus of\n", " sentence pairs automatically extracted from online news sources, with human annotations\n", " for whether the sentences in the pair are semantically equivalent.\n", " \"\"\",\n", " homepage='https://www.microsoft.com/en-us/download/details.aspx?id=52398',\n", " data_path=PosixGPath('/tmp/tmpyoq7f3i8tfds'),\n", " file_format=tfrecord,\n", " download_size=1.43 MiB,\n", " dataset_size=1.74 MiB,\n", " features=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", " }),\n", " supervised_keys=None,\n", " disable_shuffling=False,\n", " splits={\n", " 'test': \u003cSplitInfo num_examples=1725, num_shards=1\u003e,\n", " 'train': \u003cSplitInfo num_examples=3668, num_shards=1\u003e,\n", " 'validation': \u003cSplitInfo num_examples=408, num_shards=1\u003e,\n", " },\n", " citation=\"\"\"@inproceedings{dolan2005automatically,\n", " title={Automatically constructing a corpus of sentential paraphrases},\n", " author={Dolan, William B and Brockett, Chris},\n", " booktitle={Proceedings of the Third International Workshop on Paraphrasing (IWP2005)},\n", " year={2005}\n", " }\n", " @inproceedings{wang2019glue,\n", " title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n", " author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n", " note={In the Proceedings of ICLR.},\n", " year={2019}\n", " }\n", " \n", " Note that each GLUE dataset has its own citation. Please see the source to see\n", " the correct citation for each contained dataset.\"\"\",\n", ")" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tfds_name = 'glue/mrpc'\n", "ds,ds_info = tfds.load(tfds_name,\n", " with_info=True)\n", "ds_info" ] }, { "cell_type": "markdown", "metadata": { "id": "ZXjm5M2Tzey6" }, "source": [ "## Download bert base checkpoint for vocab file" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 10341, "status": "ok", "timestamp": 1689010145419, "user": { "displayName": "Siva Sravana Kumar Neeli", "userId": "06669604936988620923" }, "user_tz": 420 }, "id": "xR3t8TY-voKQ", "outputId": "945fac79-c397-4157-d33b-8adc6a9edb18" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2023-07-10 17:28:55-- https://storage.googleapis.com/tf_model_garden/nlp/bert/v3/uncased_L-12_H-768_A-12.tar.gz\n", "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.196.128, 173.194.215.128, 173.194.216.128, ...\n", "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.196.128|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 405351325 (387M) [application/octet-stream]\n", "Saving to: ‘./uncased_L-12_H-768_A-12.tar.gz’\n", "\n", "./uncased_L-12_H-76 100%[===================\u003e] 386.57M 120MB/s in 3.2s \n", "\n", "2023-07-10 17:28:58 (120 MB/s) - ‘./uncased_L-12_H-768_A-12.tar.gz’ saved [405351325/405351325]\n", "\n", "uncased_L-12_H-768_A-12/\n", "uncased_L-12_H-768_A-12/vocab.txt\n", "uncased_L-12_H-768_A-12/bert_model.ckpt.index\n", "uncased_L-12_H-768_A-12/bert_model.ckpt.data-00000-of-00001\n", "uncased_L-12_H-768_A-12/params.yaml\n", "uncased_L-12_H-768_A-12/bert_config.json\n" ] } ], "source": [ "!wget https://storage.googleapis.com/tf_model_garden/nlp/bert/v3/uncased_L-12_H-768_A-12.tar.gz -O ./uncased_L-12_H-768_A-12.tar.gz\n", "!tar -zxvf ./uncased_L-12_H-768_A-12.tar.gz -C ./\n", "!rm ./uncased_L-12_H-768_A-12.tar.gz" ] }, { "cell_type": "markdown", "metadata": { "id": "ILW4p_gVP4pg" }, "source": [ "## Configure the perceiver model for custom dataset training" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cAlksY6nzdQY" }, "outputs": [], "source": [ "gs_folder_bert = \"./uncased_L-12_H-768_A-12\"" ] }, { "cell_type": "markdown", "metadata": { "id": "AzCEMctzz5N4" }, "source": [ "### Load the registered configuration" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "qS4-ZWxFBH7K" }, "outputs": [], "source": [ "exp_config = exp_cfg.exp_factory.get_exp_config('perceiver/word_piece_raw_sentence_prediction')" ] }, { "cell_type": "markdown", "metadata": { "id": "4c5gFEvwPd7_" }, "source": [ "### Change the parameters required to train the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "m6-OhvAUyfXT" }, "outputs": [], "source": [ "BATCH_SIZE = 8\n", "epochs = 5\n", "vocab_file = './uncased_L-12_H-768_A-12/vocab.txt'\n", "\n", "\n", "train_data_size = ds_info.splits['train'].num_examples\n", "validation_data_size = ds_info.splits['validation'].num_examples\n", "steps_per_epoch = int(train_data_size / BATCH_SIZE)\n", "num_train_steps = steps_per_epoch * epochs\n", "validation_steps = int(validation_data_size / BATCH_SIZE)\n", "warmup_steps = int(0.1 * num_train_steps)\n", "initial_learning_rate = 2e-5\n", "\n", "\n", "exp_config.runtime.num_gpus = 1\n", "exp_config.runtime.enable_xla = False\n", "exp_config.runtime.mixed_precision_dtype = 'mixed_bfloat16'\n", "exp_config.task.model.num_classes = 2\n", "\n", "exp_config.task.train_data.tfds_name = 'glue/mrpc'\n", "exp_config.task.train_data.tfds_split = 'train'\n", "exp_config.task.train_data.text_fields = ['sentence1', 'sentence2']\n", "exp_config.task.train_data.global_batch_size = BATCH_SIZE\n", "exp_config.task.train_data.lower_case = True\n", "exp_config.task.train_data.tokenization = 'WordPiece'\n", "exp_config.task.train_data.vocab_file = vocab_file\n", "\n", "exp_config.task.validation_data.tfds_name = 'glue/mrpc'\n", "exp_config.task.validation_data.tfds_split = 'validation'\n", "exp_config.task.validation_data.text_fields = ['sentence1', 'sentence2']\n", "exp_config.task.validation_data.global_batch_size = BATCH_SIZE\n", "exp_config.task.validation_data.lower_case = True\n", "exp_config.task.validation_data.tokenization = 'WordPiece'\n", "exp_config.task.validation_data.vocab_file = vocab_file\n", "\n", "exp_config.trainer.checkpoint_interval = steps_per_epoch\n", "exp_config.trainer.optimizer_config.learning_rate.polynomial.initial_learning_rate = initial_learning_rate\n", "exp_config.trainer.optimizer_config.learning_rate.polynomial.decay_steps = num_train_steps\n", "exp_config.trainer.optimizer_config.warmup.polynomial.warmup_steps = warmup_steps\n", "exp_config.trainer.steps_per_loop = steps_per_epoch\n", "exp_config.trainer.summary_interval = steps_per_epoch\n", "exp_config.trainer.train_steps = num_train_steps\n", "exp_config.trainer.validation_interval = steps_per_epoch\n", "exp_config.trainer.validation_steps = validation_steps" ] }, { "cell_type": "markdown", "metadata": { "id": "LYP-_WCHnDHd" }, "source": [ "### Detect the hardware" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 166, "status": "ok", "timestamp": 1689010193979, "user": { "displayName": "Siva Sravana Kumar Neeli", "userId": "06669604936988620923" }, "user_tz": 420 }, "id": "tjp6Ekql21_U", "outputId": "72c0c030-0c1a-4edc-e023-82c98b4b0de2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running on single GPU /device:GPU:0\n", "Number of accelerators: 1\n" ] } ], "source": [ "try:\n", " tpu_resolver = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection\n", "except ValueError:\n", " tpu_resolver = None\n", " gpus = tf.config.experimental.list_logical_devices(\"GPU\")\n", "\n", "# Select appropriate distribution strategy\n", "if tpu_resolver:\n", " tf.config.experimental_connect_to_cluster(tpu_resolver)\n", " tf.tpu.experimental.initialize_tpu_system(tpu_resolver)\n", " distribution_strategy = tf.distribute.experimental.TPUStrategy(tpu_resolver)\n", " print('Running on TPU ', tpu_resolver.cluster_spec().as_dict()['worker'])\n", "elif len(gpus) \u003e 1:\n", " distribution_strategy = tf.distribute.MirroredStrategy([gpu.name for gpu in gpus])\n", " print('Running on multiple GPUs ', [gpu.name for gpu in gpus])\n", "elif len(gpus) == 1:\n", " distribution_strategy = tf.distribute.get_strategy() # default strategy that works on CPU and single GPU\n", " print('Running on single GPU ', gpus[0].name)\n", "else:\n", " distribution_strategy = tf.distribute.get_strategy() # default strategy that works on CPU and single GPU\n", " print('Running on CPU')\n", "\n", "print(\"Number of accelerators: \", distribution_strategy.num_replicas_in_sync)" ] }, { "cell_type": "markdown", "metadata": { "id": "GxMDFCQhPjSW" }, "source": [ "### Print the modified configuration." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 500 }, "executionInfo": { "elapsed": 376, "status": "ok", "timestamp": 1689010197252, "user": { "displayName": "Siva Sravana Kumar Neeli", "userId": "06669604936988620923" }, "user_tz": 420 }, "id": "pep0gabI24_R", "outputId": "eaadce22-2a36-4fbe-e089-ba2fdc547d26" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{ 'runtime': { 'all_reduce_alg': None,\n", " 'batchnorm_spatial_persistent': False,\n", " 'dataset_num_private_threads': None,\n", " 'default_shard_dim': -1,\n", " 'distribution_strategy': 'mirrored',\n", " 'enable_xla': False,\n", " 'gpu_thread_mode': None,\n", " 'loss_scale': None,\n", " 'mixed_precision_dtype': 'mixed_bfloat16',\n", " 'num_cores_per_replica': 1,\n", " 'num_gpus': 1,\n", " 'num_packs': 1,\n", " 'per_gpu_thread_count': 0,\n", " 'run_eagerly': False,\n", " 'task_index': -1,\n", " 'tpu': None,\n", " 'tpu_enable_xla_dynamic_padder': None,\n", " 'use_tpu_mp_strategy': False,\n", " 'worker_hosts': None},\n", " 'task': { 'allow_image_summary': False,\n", " 'differential_privacy_config': None,\n", " 'hub_module_url': '',\n", " 'init_checkpoint': '',\n", " 'init_cls_pooler': False,\n", " 'metric_type': 'accuracy',\n", " 'model': { 'decoder': { 'd_latents': 1280,\n", " 'd_model': 768,\n", " 'decoder': { 'num_heads': 8,\n", " 'output_last_dim': 768,\n", " 'qk_last_dim': 256,\n", " 'use_query_residual': False,\n", " 'v_last_dim': 768},\n", " 'output_index_dim': 1,\n", " 'position_encoding_intializer_stddev': 0.02,\n", " 'z_index_dim': 256},\n", " 'encoder': { 'd_latents': 1280,\n", " 'd_model': 768,\n", " 'embedding_initializer_stddev': 0.02,\n", " 'embedding_width': 768,\n", " 'encoder': { 'att_init_scale': 1.0,\n", " 'cross_attention_num_heads': 8,\n", " 'cross_attention_widening_factor': 1,\n", " 'dense_init_scale': 1.0,\n", " 'dropout_attn_prob': 0.0,\n", " 'dropout_prob': 0.0,\n", " 'norm_epsilon': 1e-05,\n", " 'num_blocks': 1,\n", " 'num_self_attends_per_block': 26,\n", " 'qk_last_dim': 256,\n", " 'self_attention_num_heads': 8,\n", " 'self_attention_widening_factor': 1,\n", " 'v_last_dim': 1280},\n", " 'input_position_encoding_intializer_stddev': 0.02,\n", " 'max_seq_len': 512,\n", " 'vocab_size': 30522,\n", " 'z_index_dim': 256,\n", " 'z_pos_enc_init_scale': 0.02},\n", " 'num_classes': 2,\n", " 'use_encoder_pooler': False},\n", " 'name': None,\n", " 'train_data': { 'apply_tf_data_service_before_batching': False,\n", " 'autotune_algorithm': None,\n", " 'block_length': 1,\n", " 'cache': False,\n", " 'cycle_length': None,\n", " 'deterministic': None,\n", " 'drop_remainder': True,\n", " 'enable_shared_tf_data_service_between_parallel_trainers': False,\n", " 'enable_tf_data_service': False,\n", " 'file_type': 'tfrecord',\n", " 'global_batch_size': 8,\n", " 'include_example_id': False,\n", " 'input_path': '',\n", " 'is_training': True,\n", " 'label_field': 'label',\n", " 'label_type': 'int',\n", " 'lower_case': True,\n", " 'prefetch_buffer_size': None,\n", " 'preprocessing_hub_module_url': '',\n", " 'seed': None,\n", " 'seq_length': 128,\n", " 'sharding': True,\n", " 'shuffle_buffer_size': 100,\n", " 'text_fields': ['sentence1', 'sentence2'],\n", " 'tf_data_service_address': None,\n", " 'tf_data_service_job_name': None,\n", " 'tfds_as_supervised': False,\n", " 'tfds_data_dir': '',\n", " 'tfds_name': 'glue/mrpc',\n", " 'tfds_skip_decoding_feature': '',\n", " 'tfds_split': 'train',\n", " 'tokenization': 'WordPiece',\n", " 'trainer_id': None,\n", " 'vocab_file': './uncased_L-12_H-768_A-12/vocab.txt'},\n", " 'validation_data': { 'apply_tf_data_service_before_batching': False,\n", " 'autotune_algorithm': None,\n", " 'block_length': 1,\n", " 'cache': False,\n", " 'cycle_length': None,\n", " 'deterministic': None,\n", " 'drop_remainder': True,\n", " 'enable_shared_tf_data_service_between_parallel_trainers': False,\n", " 'enable_tf_data_service': False,\n", " 'file_type': 'tfrecord',\n", " 'global_batch_size': 8,\n", " 'include_example_id': False,\n", " 'input_path': '',\n", " 'is_training': True,\n", " 'label_field': 'label',\n", " 'label_type': 'int',\n", " 'lower_case': True,\n", " 'prefetch_buffer_size': None,\n", " 'preprocessing_hub_module_url': '',\n", " 'seed': None,\n", " 'seq_length': 128,\n", " 'sharding': True,\n", " 'shuffle_buffer_size': 100,\n", " 'text_fields': [ 'sentence1',\n", " 'sentence2'],\n", " 'tf_data_service_address': None,\n", " 'tf_data_service_job_name': None,\n", " 'tfds_as_supervised': False,\n", " 'tfds_data_dir': '',\n", " 'tfds_name': 'glue/mrpc',\n", " 'tfds_skip_decoding_feature': '',\n", " 'tfds_split': 'validation',\n", " 'tokenization': 'WordPiece',\n", " 'trainer_id': None,\n", " 'vocab_file': './uncased_L-12_H-768_A-12/vocab.txt'}},\n", " 'trainer': { 'allow_tpu_summary': False,\n", " 'best_checkpoint_eval_metric': '',\n", " 'best_checkpoint_export_subdir': '',\n", " 'best_checkpoint_metric_comp': 'higher',\n", " 'checkpoint_interval': 458,\n", " 'continuous_eval_timeout': 3600,\n", " 'eval_tf_function': True,\n", " 'eval_tf_while_loop': False,\n", " 'loss_upper_bound': 1000000.0,\n", " 'max_to_keep': 5,\n", " 'optimizer_config': { 'ema': None,\n", " 'learning_rate': { 'polynomial': { 'cycle': False,\n", " 'decay_steps': 2290,\n", " 'end_learning_rate': 0.0,\n", " 'initial_learning_rate': 2e-05,\n", " 'name': 'PolynomialDecay',\n", " 'offset': 0,\n", " 'power': 1.0},\n", " 'type': 'polynomial'},\n", " 'optimizer': { 'lamb': { 'beta_1': 0.9,\n", " 'beta_2': 0.999,\n", " 'clipnorm': None,\n", " 'clipvalue': None,\n", " 'epsilon': 1e-06,\n", " 'exclude_from_layer_adaptation': None,\n", " 'exclude_from_weight_decay': [ 'LayerNorm',\n", " 'layer_norm',\n", " 'bias'],\n", " 'global_clipnorm': None,\n", " 'name': 'LAMB',\n", " 'weight_decay_rate': 0.01},\n", " 'type': 'lamb'},\n", " 'warmup': { 'linear': { 'name': 'linear',\n", " 'warmup_learning_rate': 0.0,\n", " 'warmup_steps': 200},\n", " 'type': 'linear'}},\n", " 'preemption_on_demand_checkpoint': True,\n", " 'recovery_begin_steps': 0,\n", " 'recovery_max_trials': 0,\n", " 'steps_per_loop': 458,\n", " 'summary_interval': 458,\n", " 'train_steps': 2290,\n", " 'train_tf_function': True,\n", " 'train_tf_while_loop': True,\n", " 'validation_interval': 458,\n", " 'validation_steps': 51,\n", " 'validation_summary_subdir': 'validation'}}\n" ] }, { "data": { "application/javascript": [ "google.colab.output.setIframeHeight(\"500px\");" ], "text/plain": [ "\u003cIPython.core.display.Javascript object\u003e" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pp.pprint(exp_config.as_dict())\n", "display.Javascript('google.colab.output.setIframeHeight(\"500px\");')" ] }, { "cell_type": "markdown", "metadata": { "id": "aivSkEcMQI6c" }, "source": [ "## Create the `Task` object (`tfm.core.base_task.Task`) from the `config_definitions.TaskConfig`.\n", "\n", "The `Task` object has all the methods necessary for building the dataset, building the model, and running training \u0026 evaluation. These methods are driven by `tfm.core.train_lib.run_experiment`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ybBYjDU72754" }, "outputs": [], "source": [ "model_dir = './trained_model/'\n", "\n", "with distribution_strategy.scope():\n", " task = task_factory.get_task(exp_config.task, logging_dir=model_dir)" ] }, { "cell_type": "markdown", "metadata": { "id": "uQvshzY-QoSi" }, "source": [ "## Train and Evaluate the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 2882433, "status": "ok", "timestamp": 1689013083414, "user": { "displayName": "Siva Sravana Kumar Neeli", "userId": "06669604936988620923" }, "user_tz": 420 }, "id": "6zS8rSCqgHBz", "outputId": "05e314d0-fcca-4d3c-9d11-5822444f7b02" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "restoring or initializing model...\n", "train | step: 0 | training until step 458...\n", "train | step: 458 | steps/sec: 0.8 | output: \n", " {'auc': 0.72914344,\n", " 'cls_accuracy': 0.66348255,\n", " 'learning_rate': 1.6e-05,\n", " 'training_loss': 0.63586426}\n", "saved checkpoint to ./trained_model/ckpt-458.\n", " eval | step: 458 | running 51 steps of evaluation...\n", " eval | step: 458 | steps/sec: 2.2 | eval time: 23.1 sec | output: \n", " {'auc': 0.7645757,\n", " 'cls_accuracy': 0.67401963,\n", " 'steps_per_second': 2.2112783063481327,\n", " 'validation_loss': 0.6187927}\n", "train | step: 458 | training until step 916...\n", "train | step: 916 | steps/sec: 0.8 | output: \n", " {'auc': 0.8580948,\n", " 'cls_accuracy': 0.7363537,\n", " 'learning_rate': 1.2e-05,\n", " 'training_loss': 0.5320095}\n", "saved checkpoint to ./trained_model/ckpt-916.\n", " eval | step: 916 | running 51 steps of evaluation...\n", " eval | step: 916 | steps/sec: 2.9 | eval time: 17.8 sec | output: \n", " {'auc': 0.800959,\n", " 'cls_accuracy': 0.6960784,\n", " 'steps_per_second': 2.861756377583562,\n", " 'validation_loss': 0.5854072}\n", "train | step: 916 | training until step 1374...\n", "train | step: 1374 | steps/sec: 0.8 | output: \n", " {'auc': 0.95597136,\n", " 'cls_accuracy': 0.83406115,\n", " 'learning_rate': 7.999999e-06,\n", " 'training_loss': 0.38442203}\n", "saved checkpoint to ./trained_model/ckpt-1374.\n", " eval | step: 1374 | running 51 steps of evaluation...\n", " eval | step: 1374 | steps/sec: 2.9 | eval time: 17.8 sec | output: \n", " {'auc': 0.7954676,\n", " 'cls_accuracy': 0.6495098,\n", " 'steps_per_second': 2.861158673104126,\n", " 'validation_loss': 0.6343756}\n", "train | step: 1374 | training until step 1832...\n", "train | step: 1832 | steps/sec: 0.8 | output: \n", " {'auc': 0.9869786,\n", " 'cls_accuracy': 0.91784936,\n", " 'learning_rate': 4e-06,\n", " 'training_loss': 0.26613}\n", "saved checkpoint to ./trained_model/ckpt-1832.\n", " eval | step: 1832 | running 51 steps of evaluation...\n", " eval | step: 1832 | steps/sec: 2.8 | eval time: 18.1 sec | output: \n", " {'auc': 0.7897566,\n", " 'cls_accuracy': 0.65686274,\n", " 'steps_per_second': 2.8241461717336493,\n", " 'validation_loss': 0.66516936}\n", "train | step: 1832 | training until step 2290...\n", "train | step: 2290 | steps/sec: 0.8 | output: \n", " {'auc': 0.9962825,\n", " 'cls_accuracy': 0.9585153,\n", " 'learning_rate': 0.0,\n", " 'training_loss': 0.18938588}\n", "saved checkpoint to ./trained_model/ckpt-2290.\n", " eval | step: 2290 | running 51 steps of evaluation...\n", " eval | step: 2290 | steps/sec: 2.4 | eval time: 21.0 sec | output: \n", " {'auc': 0.7945166,\n", " 'cls_accuracy': 0.6764706,\n", " 'steps_per_second': 2.4306418804782743,\n", " 'validation_loss': 0.6767317}\n" ] } ], "source": [ "model, eval_logs = train_lib.run_experiment(\n", " distribution_strategy=distribution_strategy,\n", " task=task,\n", " mode='train_and_eval',\n", " params=exp_config,\n", " model_dir=model_dir)" ] }, { "cell_type": "markdown", "metadata": { "id": "weZwyXKYQsVA" }, "source": [ "## Testing the trained model" ] }, { "cell_type": "markdown", "metadata": { "id": "ZVZ33VldQ4vL" }, "source": [ "### Helper functions for pre-processing test data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "O-q-hYpQ6nQB" }, "outputs": [], "source": [ "tokenizer = FastWordpieceBertTokenizer(\n", " vocab_file=vocab_file,\n", " lower_case=exp_config.task.train_data.lower_case\n", ")\n", "\n", "packer = BertPackInputs(\n", " seq_length=exp_config.task.train_data.seq_length,\n", " special_tokens_dict=tokenizer.get_special_tokens_dict()\n", ")\n", "\n", "\n", "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": "Xu4OqanvROFz" }, "source": [ "### Pre-process test data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TYsP7Soq7off" }, "outputs": [], "source": [ "bert_inputs_processor = BertInputProcessor(\n", " tokenizer=tokenizer,\n", " packer=packer\n", ")\n", "test_ds = ds['test'].batch(\n", " 1).map(bert_inputs_processor)" ] }, { "cell_type": "markdown", "metadata": { "id": "w_I68G51RSHO" }, "source": [ "### Get the predictions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 10328, "status": "ok", "timestamp": 1689013095845, "user": { "displayName": "Siva Sravana Kumar Neeli", "userId": "06669604936988620923" }, "user_tz": 420 }, "id": "zDABiqAn29oi", "outputId": "2ef6b773-1302-4659-e6bf-45552e4fae7f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sentence 1:[b'Shares in BA were down 1.5 percent at 168 pence by 1420 GMT , off a low of 164p , in a slightly stronger overall London market .']\n", "Sentence 2:[b'Shares in BA were down three percent at 165-1 / 4 pence by 0933 GMT , off a low of 164 pence , in a stronger market .']\n", "1/1 [==============================] - 4s 4s/step\n", "Prediction: 1\n", "Sentence 1:[b'The South Korean Agriculture and Forestry Ministry also said it would throw out or send back all Canadian beef currently in store .']\n", "Sentence 2:[b'The South Korean Agriculture and Forestry Ministry said it would scrap or return all Canadian beef in store .']\n", "1/1 [==============================] - 0s 103ms/step\n", "Prediction: 1\n", "Sentence 1:[b'\" New Yorkers didn \\'t embrace these units like they could have , \" said Matthew Daus , chairman of the commission .']\n", "Sentence 2:[b'\" New Yorkers didn \\'t embrace these units like they could have , \" Matthew W. Daus , the commission \\'s chairman , said yesterday .']\n", "1/1 [==============================] - 0s 95ms/step\n", "Prediction: 1\n", "Sentence 1:[b'\" I really liked him and I still do , \" Cohen Alon told the Herald yesterday .']\n", "Sentence 2:[b'And I really liked him , and I still do .']\n", "1/1 [==============================] - 0s 96ms/step\n", "Prediction: 0\n", "Sentence 1:[b'Tight media controls and the remote location of the northern village where the fighting broke out made it impossible to confirm what happened .']\n", "Sentence 2:[b'Tight media controls and the remote location of the clash made it impossible to confirm what happened .']\n", "1/1 [==============================] - 0s 94ms/step\n", "Prediction: 1\n", "Sentence 1:[b'She had been critically ill after May 7 surgery to replace a heart valve .']\n", "Sentence 2:[b'She had been critically ill since having surgery at Baptist Hospital on May 7 to replace a heart valve .']\n", "1/1 [==============================] - 0s 97ms/step\n", "Prediction: 1\n", "Sentence 1:[b'\" In fact , I was physically sick several times at this stage , because he looked so desperate , \" she said .']\n", "Sentence 2:[b'Speaking about the day before he was found dead , she said : \" I was physically sick several times at this stage because he looked so desperate .']\n", "1/1 [==============================] - 0s 101ms/step\n", "Prediction: 0\n", "Sentence 1:[b'The technology-laced Nasdaq Composite Index added 11.98 points , or 0.72 percent , to 1,680.42 .']\n", "Sentence 2:[b\"The broader Standard \u0026 Poor 's 500 Index .SPX was off 1.07 points , or 0.11 percent , at 1,010.59 .\"]\n", "1/1 [==============================] - 0s 99ms/step\n", "Prediction: 0\n" ] } ], "source": [ "for record in ds['test'].batch(1).take(8):\n", " print(f\"Sentence 1:{record['sentence1'].numpy()}\")\n", " print(f\"Sentence 2:{record['sentence2'].numpy()}\")\n", " processed_rec = bert_inputs_processor(record)\n", " prediction = tf.argmax(\n", " model.predict(processed_rec[0]),\n", " axis=1)\n", " print(f\"Prediction: {prediction[0]}\")" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "last_runtime": { "build_target": "//learning/grp/tools/ml_python:ml_notebook", "kind": "private" }, "provenance": [ { "file_id": "1ODIzioYI5DjPOT7-4EbfalaDhD0WXCHE", "timestamp": 1689019241476 } ], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0389b5e7bd2f4ad9a040f9a4d48af95c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", 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