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Posted to github@beam.apache.org by GitBox <gi...@apache.org> on 2022/09/06 00:52:14 UTC
[GitHub] [beam] liferoad opened a new pull request, #23035: Add one NER example to use a spaCy model with RunInference
liferoad opened a new pull request, #23035:
URL: https://github.com/apache/beam/pull/23035
Users can bring any ML model to Beam RunInference besides Pytorch and sklearn. Here we add one example to show how to use a spaCy model inside Beam RunInference to perform NER with some text strings.
@rezarokni
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[GitHub] [beam] liferoad commented on a diff in pull request #23035: Add one NER example to use a spaCy model with RunInference
Posted by GitBox <gi...@apache.org>.
liferoad commented on code in PR #23035:
URL: https://github.com/apache/beam/pull/23035#discussion_r963833358
##########
examples/notebooks/beam-ml/run_custom_inference.ipynb:
##########
@@ -0,0 +1,575 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "C1rAsD2L-hSO",
+ "metadata": {
+ "cellView": "form",
+ "id": "C1rAsD2L-hSO"
+ },
+ "outputs": [],
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e",
+ "metadata": {
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e"
+ },
+ "source": [
+ "# Bring your own Machine Leanring (ML) model to Beam RunInference\n",
+ "\n",
+ "<button>\n",
+ " <a href=\"https://beam.apache.org/documentation/sdks/python-machine-learning/\">\n",
+ " <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+ " Beam RunInference\n",
+ " </a>\n",
+ "</button>\n",
+ "\n",
+ "In this notebook, we walk through a simple example to show how to customize your own ML model handler using\n",
+ "[ModelHandler](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler).\n",
+ "\n",
+ "Named-Entity Recognition (NER) is one of the most common tasks for Natural Language Processing (NLP), \n",
+ "which locates and classifies named entities in unstructured text into pre-defined labels such as person name, organization, date, etc. \n",
+ "In this example, we illustrate how to use the popular spaCy package to load a ML model and apply it inside a Beam pipeline.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd",
+ "metadata": {
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd"
+ },
+ "source": [
+ "## Package Dependencies\n",
+ "\n",
+ "The RunInference library is available in Apache Beam version <b>2.40</b> or later.\n",
+ "\n",
+ "`spaCy` and `pandas` need to be installed. Here, a small NER model (`en_core_web_sm`) is also installed but any valid spaCy model could be used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "outputId": "da04ccb9-0801-47f6-ec9e-e87f0ca4569f"
+ },
+ "outputs": [],
+ "source": [
+ "# uncomment these to install the required packages\n",
+ "# %pip install spacy pandas\n",
+ "# %pip install \"apache-beam[gcp, dataframe, interactive]\"\n",
+ "# !python -m spacy download en_core_web_sm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Let us play with spaCy first"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spaCy language\n",
+ "\n",
+ "import spacy\n",
+ "\n",
+ "nlp = spacy.load(\"en_core_web_sm\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# some text strings for fun\n",
+ "text_strings = [\n",
+ " \"The New York Times is an American daily newspaper based in New York City with a worldwide readership.\",\n",
+ " \"It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\"\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# check what entities spaCy can recognize\n",
+ "doc = nlp(text_strings[0])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times 0 18 ORG\n",
+ "American 25 33 NORP\n",
+ "daily 34 39 DATE\n",
+ "New York City 59 72 GPE\n"
+ ]
+ }
+ ],
+ "source": [
+ "for ent in doc.ents:\n",
+ " print(ent.text, ent.start_char, ent.end_char, ent.label_)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
+ "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " The New York Times\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
+ "</mark>\n",
+ " is an \n",
+ "<mark class=\"entity\" style=\"background: #c887fb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " American\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">NORP</span>\n",
+ "</mark>\n",
+ " \n",
+ "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " daily\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+ "</mark>\n",
+ " newspaper based in \n",
+ "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " New York City\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
+ "</mark>\n",
+ " with a worldwide readership.</div></span>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# visualize the results\n",
+ "from spacy import displacy\n",
+ "displacy.render(doc, style=\"ent\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">It was founded in \n",
+ "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " 1851\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+ "</mark>\n",
+ " by \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Henry Jarvis\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ " \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Raymond\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ " and \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " George Jones\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ ", and was initially published by \n",
+ "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Raymond, Jones & Company\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
+ "</mark>\n",
+ ".</div></span>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# another example\n",
+ "displacy.render(nlp(text_strings[1]), style=\"ent\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Now time to create our own `ModelHandler` to use spaCy for inference"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": "\n if (typeof window.interactive_beam_jquery == 'undefined') {\n var jqueryScript = document.createElement('script');\n jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n jqueryScript.type = 'text/javascript';\n jqueryScript.onload = function() {\n var datatableScript = document.createElement('script');\n datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n datatableScript.type = 'text/javascript';\n datatableScript.onload = function() {\n window.interactive_beam_jquery = jQuery.noConflict(true);\n window.interactive_beam_jquery(document).ready(function($){\n \n });\n }\n document.head.appendChild(datatableScript);\n };\n document.head.appendChild(jqueryScript);\n } else {\n window.interactive
_beam_jquery(document).ready(function($){\n \n });\n }"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times is an American daily newspaper based in New York City with a worldwide readership.\n",
+ "It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "import apache_beam as beam\n",
+ "from apache_beam.options.pipeline_options import PipelineOptions\n",
+ "\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "\n",
+ "pipeline = beam.Pipeline()\n",
+ "\n",
+ "# only print the results to check\n",
+ "with pipeline as p:\n",
+ " (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings)\n",
+ " | beam.Map(print)\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Now define SpacyModelHandler to load the model and perform the inference\n",
+ "\n",
+ "from apache_beam.ml.inference.base import RunInference\n",
+ "from apache_beam.ml.inference.base import ModelHandler\n",
+ "from apache_beam.ml.inference.base import PredictionResult\n",
+ "from spacy import Language\n",
+ "from typing import Any\n",
+ "from typing import Dict\n",
+ "from typing import Iterable\n",
+ "from typing import Optional\n",
+ "from typing import Sequence\n",
+ "\n",
+ "class SpacyModelHandler(ModelHandler[str,\n",
+ " PredictionResult,\n",
+ " Language]):\n",
+ " def __init__(\n",
+ " self,\n",
+ " model_name: str = \"en_core_web_sm\",\n",
+ " ):\n",
+ " \"\"\" Implementation of the ModelHandler interface for spaCy using text as input.\n",
+ "\n",
+ " Example Usage::\n",
+ "\n",
+ " pcoll | RunInference(SpacyModelHandler())\n",
+ "\n",
+ " Args:\n",
+ " model_name: The spaCy model name. Default is en_core_web_sm.\n",
+ " \"\"\"\n",
+ " self._model_name = model_name\n",
+ "\n",
+ " def load_model(self) -> Language:\n",
+ " \"\"\"Loads and initializes a model for processing.\"\"\"\n",
+ " return spacy.load(self._model_name)\n",
+ "\n",
+ " def run_inference(\n",
+ " self,\n",
+ " batch: Sequence[str],\n",
+ " model: Language,\n",
+ " inference_args: Optional[Dict[str, Any]] = None\n",
+ " ) -> Iterable[PredictionResult]:\n",
+ " \"\"\"Runs inferences on a batch of text strings.\n",
+ "\n",
+ " Args:\n",
+ " batch: A sequence of examples as text strings. \n",
+ " model: A spaCy language model\n",
+ " inference_args: Any additional arguments for an inference.\n",
+ "\n",
+ " Returns:\n",
+ " An Iterable of type PredictionResult.\n",
+ " \"\"\"\n",
+ " # loop each text string and use tuple to store the inference results\n",
+ " predictions = []\n",
+ " for one_text in batch:\n",
+ " doc = model(one_text)\n",
+ " predictions.append(\n",
+ " [(ent.text, ent.start_char, ent.end_char, ent.label_) for ent in doc.ents])\n",
+ " return [PredictionResult(x, y) for x, y in zip(batch, predictions)]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times is an American daily newspaper based in New York City with a worldwide readership.\n",
+ "It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\n",
+ "PredictionResult(example='The New York Times is an American daily newspaper based in New York City with a worldwide readership.', inference=[('The New York Times', 0, 18, 'ORG'), ('American', 25, 33, 'NORP'), ('daily', 34, 39, 'DATE'), ('New York City', 59, 72, 'GPE')])\n",
+ "PredictionResult(example='It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.', inference=[('1851', 18, 22, 'DATE'), ('Henry Jarvis', 26, 38, 'PERSON'), ('Raymond', 39, 46, 'PERSON'), ('George Jones', 51, 63, 'PERSON'), ('Raymond, Jones & Company', 96, 120, 'ORG')])\n"
+ ]
+ }
+ ],
+ "source": [
+ "# quick check to show the inference results are correct\n",
+ "with pipeline as p:\n",
+ " (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings)\n",
+ " | \"RunInferenceSpacy\" >> RunInference(SpacyModelHandler(\"en_core_web_sm\"))\n",
+ " | beam.Map(print)\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Use `KeyedModelHandler` to handle keyed data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# some text strings with keys, which are useful to distinguish examples\n",
+ "text_strings_with_keys = [\n",
+ " (\"example_0\", \"The New York Times is an American daily newspaper based in New York City with a worldwide readership.\"),\n",
+ " (\"example_1\", \"It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\")\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from apache_beam.runners.interactive.interactive_runner import InteractiveRunner\n",
+ "from apache_beam.ml.inference.base import KeyedModelHandler\n",
+ "from apache_beam.dataframe.convert import to_dataframe\n",
+ "\n",
+ "pipeline = beam.Pipeline(InteractiveRunner())\n",
+ "\n",
+ "keyed_spacy_model_handler = KeyedModelHandler(SpacyModelHandler(\"en_core_web_sm\"))\n",
+ "\n",
+ "# quick check to show the inference results are correct\n",
+ "with pipeline as p:\n",
+ " results = (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings_with_keys)\n",
+ " | \"RunInferenceSpacy\" >> RunInference(keyed_spacy_model_handler)\n",
+ " # Map to Row objects to generate a schema suitable for conversion\n",
+ " # to a dataframe.\n",
+ " | 'ToRows' >> beam.Map(lambda row: beam.Row(key=row[0], text=row[1][0], predictions=row[1][1]))\n",
Review Comment:
The idea is to show another way to quickly debug the pipeline using DataFrame besides print. This is why I did it differently for this part.
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[GitHub] [beam] liferoad commented on a diff in pull request #23035: Add one NER example to use a spaCy model with RunInference
Posted by GitBox <gi...@apache.org>.
liferoad commented on code in PR #23035:
URL: https://github.com/apache/beam/pull/23035#discussion_r964216653
##########
examples/notebooks/beam-ml/run_custom_inference.ipynb:
##########
@@ -0,0 +1,570 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "C1rAsD2L-hSO",
+ "metadata": {
+ "cellView": "form",
+ "id": "C1rAsD2L-hSO"
+ },
+ "outputs": [],
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e",
+ "metadata": {
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e"
+ },
+ "source": [
+ "# Bring your own Machine Leanring (ML) model to Beam RunInference\n",
+ "\n",
+ "<button>\n",
+ " <a href=\"https://beam.apache.org/documentation/sdks/python-machine-learning/\">\n",
+ " <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+ " Beam RunInference\n",
+ " </a>\n",
+ "</button>\n",
+ "\n",
+ "In this notebook, we walk through a simple example to show how to build your own ML model handler using\n",
+ "[ModelHandler](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler).\n",
+ "\n",
+ "Named-Entity Recognition (NER) is one of the most common tasks for Natural Language Processing (NLP), \n",
+ "which locates and classifies named entities in unstructured text into pre-defined labels such as person name, organization, date, etc. \n",
+ "In this example, we illustrate how to use the popular spaCy package to load a ML model and perform inference in a Beam pipeline using RunInference PTransform.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd",
+ "metadata": {
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd"
+ },
+ "source": [
+ "## Package Dependencies\n",
+ "\n",
+ "The RunInference library is available in Apache Beam version <b>2.40</b> or later.\n",
+ "\n",
+ "`spaCy` and `pandas` need to be installed. Here, a small NER model (`en_core_web_sm`) is also installed but any valid spaCy model could be used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "outputId": "da04ccb9-0801-47f6-ec9e-e87f0ca4569f"
+ },
+ "outputs": [],
+ "source": [
+ "# uncomment these to install the required packages\n",
+ "# %pip install spacy pandas\n",
+ "# %pip install \"apache-beam[gcp, dataframe, interactive]\"\n",
+ "# !python -m spacy download en_core_web_sm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Let us play with spaCy first"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spaCy language\n",
+ "\n",
+ "import spacy\n",
+ "\n",
+ "nlp = spacy.load(\"en_core_web_sm\")\n"
Review Comment:
Added more information. Done.
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[GitHub] [beam] AnandInguva commented on a diff in pull request #23035: Add one NER example to use a spaCy model with RunInference
Posted by GitBox <gi...@apache.org>.
AnandInguva commented on code in PR #23035:
URL: https://github.com/apache/beam/pull/23035#discussion_r963812120
##########
examples/notebooks/beam-ml/run_custom_inference.ipynb:
##########
@@ -0,0 +1,575 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "C1rAsD2L-hSO",
+ "metadata": {
+ "cellView": "form",
+ "id": "C1rAsD2L-hSO"
+ },
+ "outputs": [],
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e",
+ "metadata": {
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e"
+ },
+ "source": [
+ "# Bring your own Machine Leanring (ML) model to Beam RunInference\n",
+ "\n",
+ "<button>\n",
+ " <a href=\"https://beam.apache.org/documentation/sdks/python-machine-learning/\">\n",
+ " <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+ " Beam RunInference\n",
+ " </a>\n",
+ "</button>\n",
+ "\n",
+ "In this notebook, we walk through a simple example to show how to customize your own ML model handler using\n",
+ "[ModelHandler](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler).\n",
+ "\n",
+ "Named-Entity Recognition (NER) is one of the most common tasks for Natural Language Processing (NLP), \n",
+ "which locates and classifies named entities in unstructured text into pre-defined labels such as person name, organization, date, etc. \n",
+ "In this example, we illustrate how to use the popular spaCy package to load a ML model and apply it inside a Beam pipeline.\n"
Review Comment:
```suggestion
"In this example, we illustrate how to use the popular spaCy package to load a ML model and perform inference in a Beam pipeline using RunInference PTransform.\n"
```
##########
examples/notebooks/beam-ml/run_custom_inference.ipynb:
##########
@@ -0,0 +1,575 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "C1rAsD2L-hSO",
+ "metadata": {
+ "cellView": "form",
+ "id": "C1rAsD2L-hSO"
+ },
+ "outputs": [],
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e",
+ "metadata": {
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e"
+ },
+ "source": [
+ "# Bring your own Machine Leanring (ML) model to Beam RunInference\n",
+ "\n",
+ "<button>\n",
+ " <a href=\"https://beam.apache.org/documentation/sdks/python-machine-learning/\">\n",
+ " <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+ " Beam RunInference\n",
+ " </a>\n",
+ "</button>\n",
+ "\n",
+ "In this notebook, we walk through a simple example to show how to customize your own ML model handler using\n",
Review Comment:
```suggestion
"In this notebook, we walk through a simple example to show how to build your own ML model handler using\n",
```
--
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[GitHub] [beam] damccorm merged pull request #23035: Add one NER example to use a spaCy model with RunInference
Posted by GitBox <gi...@apache.org>.
damccorm merged PR #23035:
URL: https://github.com/apache/beam/pull/23035
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[GitHub] [beam] liferoad commented on a diff in pull request #23035: Add one NER example to use a spaCy model with RunInference
Posted by GitBox <gi...@apache.org>.
liferoad commented on code in PR #23035:
URL: https://github.com/apache/beam/pull/23035#discussion_r963841840
##########
examples/notebooks/beam-ml/run_custom_inference.ipynb:
##########
@@ -0,0 +1,575 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "C1rAsD2L-hSO",
+ "metadata": {
+ "cellView": "form",
+ "id": "C1rAsD2L-hSO"
+ },
+ "outputs": [],
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e",
+ "metadata": {
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e"
+ },
+ "source": [
+ "# Bring your own Machine Leanring (ML) model to Beam RunInference\n",
+ "\n",
+ "<button>\n",
+ " <a href=\"https://beam.apache.org/documentation/sdks/python-machine-learning/\">\n",
+ " <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+ " Beam RunInference\n",
+ " </a>\n",
+ "</button>\n",
+ "\n",
+ "In this notebook, we walk through a simple example to show how to customize your own ML model handler using\n",
+ "[ModelHandler](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler).\n",
+ "\n",
+ "Named-Entity Recognition (NER) is one of the most common tasks for Natural Language Processing (NLP), \n",
+ "which locates and classifies named entities in unstructured text into pre-defined labels such as person name, organization, date, etc. \n",
+ "In this example, we illustrate how to use the popular spaCy package to load a ML model and apply it inside a Beam pipeline.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd",
+ "metadata": {
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd"
+ },
+ "source": [
+ "## Package Dependencies\n",
+ "\n",
+ "The RunInference library is available in Apache Beam version <b>2.40</b> or later.\n",
+ "\n",
+ "`spaCy` and `pandas` need to be installed. Here, a small NER model (`en_core_web_sm`) is also installed but any valid spaCy model could be used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "outputId": "da04ccb9-0801-47f6-ec9e-e87f0ca4569f"
+ },
+ "outputs": [],
+ "source": [
+ "# uncomment these to install the required packages\n",
+ "# %pip install spacy pandas\n",
+ "# %pip install \"apache-beam[gcp, dataframe, interactive]\"\n",
+ "# !python -m spacy download en_core_web_sm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Let us play with spaCy first"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spaCy language\n",
+ "\n",
+ "import spacy\n",
+ "\n",
+ "nlp = spacy.load(\"en_core_web_sm\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# some text strings for fun\n",
+ "text_strings = [\n",
+ " \"The New York Times is an American daily newspaper based in New York City with a worldwide readership.\",\n",
+ " \"It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\"\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# check what entities spaCy can recognize\n",
+ "doc = nlp(text_strings[0])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times 0 18 ORG\n",
+ "American 25 33 NORP\n",
+ "daily 34 39 DATE\n",
+ "New York City 59 72 GPE\n"
+ ]
+ }
+ ],
+ "source": [
+ "for ent in doc.ents:\n",
+ " print(ent.text, ent.start_char, ent.end_char, ent.label_)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
+ "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " The New York Times\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
+ "</mark>\n",
+ " is an \n",
+ "<mark class=\"entity\" style=\"background: #c887fb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " American\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">NORP</span>\n",
+ "</mark>\n",
+ " \n",
+ "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " daily\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+ "</mark>\n",
+ " newspaper based in \n",
+ "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " New York City\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
+ "</mark>\n",
+ " with a worldwide readership.</div></span>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# visualize the results\n",
+ "from spacy import displacy\n",
+ "displacy.render(doc, style=\"ent\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">It was founded in \n",
+ "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " 1851\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+ "</mark>\n",
+ " by \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Henry Jarvis\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ " \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Raymond\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ " and \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " George Jones\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ ", and was initially published by \n",
+ "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Raymond, Jones & Company\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
+ "</mark>\n",
+ ".</div></span>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# another example\n",
+ "displacy.render(nlp(text_strings[1]), style=\"ent\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Now time to create our own `ModelHandler` to use spaCy for inference"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": "\n if (typeof window.interactive_beam_jquery == 'undefined') {\n var jqueryScript = document.createElement('script');\n jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n jqueryScript.type = 'text/javascript';\n jqueryScript.onload = function() {\n var datatableScript = document.createElement('script');\n datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n datatableScript.type = 'text/javascript';\n datatableScript.onload = function() {\n window.interactive_beam_jquery = jQuery.noConflict(true);\n window.interactive_beam_jquery(document).ready(function($){\n \n });\n }\n document.head.appendChild(datatableScript);\n };\n document.head.appendChild(jqueryScript);\n } else {\n window.interactive
_beam_jquery(document).ready(function($){\n \n });\n }"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times is an American daily newspaper based in New York City with a worldwide readership.\n",
+ "It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "import apache_beam as beam\n",
+ "from apache_beam.options.pipeline_options import PipelineOptions\n",
+ "\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "\n",
+ "pipeline = beam.Pipeline()\n",
+ "\n",
+ "# only print the results to check\n",
+ "with pipeline as p:\n",
+ " (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings)\n",
+ " | beam.Map(print)\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Now define SpacyModelHandler to load the model and perform the inference\n",
+ "\n",
+ "from apache_beam.ml.inference.base import RunInference\n",
+ "from apache_beam.ml.inference.base import ModelHandler\n",
+ "from apache_beam.ml.inference.base import PredictionResult\n",
+ "from spacy import Language\n",
+ "from typing import Any\n",
+ "from typing import Dict\n",
+ "from typing import Iterable\n",
+ "from typing import Optional\n",
+ "from typing import Sequence\n",
+ "\n",
+ "class SpacyModelHandler(ModelHandler[str,\n",
+ " PredictionResult,\n",
+ " Language]):\n",
+ " def __init__(\n",
+ " self,\n",
+ " model_name: str = \"en_core_web_sm\",\n",
+ " ):\n",
+ " \"\"\" Implementation of the ModelHandler interface for spaCy using text as input.\n",
+ "\n",
+ " Example Usage::\n",
+ "\n",
+ " pcoll | RunInference(SpacyModelHandler())\n",
+ "\n",
+ " Args:\n",
+ " model_name: The spaCy model name. Default is en_core_web_sm.\n",
+ " \"\"\"\n",
+ " self._model_name = model_name\n",
+ "\n",
+ " def load_model(self) -> Language:\n",
+ " \"\"\"Loads and initializes a model for processing.\"\"\"\n",
+ " return spacy.load(self._model_name)\n",
+ "\n",
+ " def run_inference(\n",
+ " self,\n",
+ " batch: Sequence[str],\n",
+ " model: Language,\n",
+ " inference_args: Optional[Dict[str, Any]] = None\n",
+ " ) -> Iterable[PredictionResult]:\n",
+ " \"\"\"Runs inferences on a batch of text strings.\n",
+ "\n",
+ " Args:\n",
+ " batch: A sequence of examples as text strings. \n",
+ " model: A spaCy language model\n",
+ " inference_args: Any additional arguments for an inference.\n",
+ "\n",
+ " Returns:\n",
+ " An Iterable of type PredictionResult.\n",
+ " \"\"\"\n",
+ " # loop each text string and use tuple to store the inference results\n",
+ " predictions = []\n",
+ " for one_text in batch:\n",
+ " doc = model(one_text)\n",
+ " predictions.append(\n",
+ " [(ent.text, ent.start_char, ent.end_char, ent.label_) for ent in doc.ents])\n",
+ " return [PredictionResult(x, y) for x, y in zip(batch, predictions)]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times is an American daily newspaper based in New York City with a worldwide readership.\n",
+ "It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\n",
+ "PredictionResult(example='The New York Times is an American daily newspaper based in New York City with a worldwide readership.', inference=[('The New York Times', 0, 18, 'ORG'), ('American', 25, 33, 'NORP'), ('daily', 34, 39, 'DATE'), ('New York City', 59, 72, 'GPE')])\n",
+ "PredictionResult(example='It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.', inference=[('1851', 18, 22, 'DATE'), ('Henry Jarvis', 26, 38, 'PERSON'), ('Raymond', 39, 46, 'PERSON'), ('George Jones', 51, 63, 'PERSON'), ('Raymond, Jones & Company', 96, 120, 'ORG')])\n"
+ ]
+ }
+ ],
+ "source": [
+ "# quick check to show the inference results are correct\n",
+ "with pipeline as p:\n",
+ " (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings)\n",
+ " | \"RunInferenceSpacy\" >> RunInference(SpacyModelHandler(\"en_core_web_sm\"))\n",
+ " | beam.Map(print)\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Use `KeyedModelHandler` to handle keyed data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# some text strings with keys, which are useful to distinguish examples\n",
+ "text_strings_with_keys = [\n",
+ " (\"example_0\", \"The New York Times is an American daily newspaper based in New York City with a worldwide readership.\"),\n",
+ " (\"example_1\", \"It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\")\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from apache_beam.runners.interactive.interactive_runner import InteractiveRunner\n",
+ "from apache_beam.ml.inference.base import KeyedModelHandler\n",
+ "from apache_beam.dataframe.convert import to_dataframe\n",
+ "\n",
+ "pipeline = beam.Pipeline(InteractiveRunner())\n",
+ "\n",
+ "keyed_spacy_model_handler = KeyedModelHandler(SpacyModelHandler(\"en_core_web_sm\"))\n",
+ "\n",
+ "# quick check to show the inference results are correct\n",
+ "with pipeline as p:\n",
+ " results = (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings_with_keys)\n",
+ " | \"RunInferenceSpacy\" >> RunInference(keyed_spacy_model_handler)\n",
+ " # Map to Row objects to generate a schema suitable for conversion\n",
+ " # to a dataframe.\n",
+ " | 'ToRows' >> beam.Map(lambda row: beam.Row(key=row[0], text=row[1][0], predictions=row[1][1]))\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css\" integrity=\"sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh\" crossorigin=\"anonymous\">\n",
+ " <div id=\"progress_indicator_25aaf10d571c025a28901bea46b94c93\">\n",
+ " <div class=\"spinner-border text-info\" role=\"status\"></div>\n",
+ " <span class=\"text-info\">Processing... collect</span>\n",
+ " </div>\n",
+ " "
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": "\n if (typeof window.interactive_beam_jquery == 'undefined') {\n var jqueryScript = document.createElement('script');\n jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n jqueryScript.type = 'text/javascript';\n jqueryScript.onload = function() {\n var datatableScript = document.createElement('script');\n datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n datatableScript.type = 'text/javascript';\n datatableScript.onload = function() {\n window.interactive_beam_jquery = jQuery.noConflict(true);\n window.interactive_beam_jquery(document).ready(function($){\n \n $(\"#progress_indicator_25aaf10d571c025a28901bea46b94c93\").remove();\n });\n }\n document.head.appendChild(datatableScript);\n };\n docum
ent.head.appendChild(jqueryScript);\n } else {\n window.interactive_beam_jquery(document).ready(function($){\n \n $(\"#progress_indicator_25aaf10d571c025a28901bea46b94c93\").remove();\n });\n }"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# convert results to a pandas dataframe\n",
+ "import apache_beam.runners.interactive.interactive_beam as ib\n",
+ "\n",
+ "beam_df = to_dataframe(results)\n",
+ "df = ib.collect(beam_df)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>key</th>\n",
+ " <th>text</th>\n",
+ " <th>predictions</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>example_0</td>\n",
+ " <td>The New York Times is an American daily newspa...</td>\n",
+ " <td>[(The New York Times, 0, 18, ORG), (American, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>example_1</td>\n",
+ " <td>It was founded in 1851 by Henry Jarvis Raymond...</td>\n",
+ " <td>[(1851, 18, 22, DATE), (Henry Jarvis, 26, 38, ...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " key text \\\n",
+ "0 example_0 The New York Times is an American daily newspa... \n",
+ "0 example_1 It was founded in 1851 by Henry Jarvis Raymond... \n",
+ "\n",
+ " predictions \n",
+ "0 [(The New York Times, 0, 18, ORG), (American, ... \n",
+ "0 [(1851, 18, 22, DATE), (Henry Jarvis, 26, 38, ... "
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "Beam RunInference",
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
Review Comment:
done
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[GitHub] [beam] rezarokni commented on a diff in pull request #23035: Add one NER example to use a spaCy model with RunInference
Posted by GitBox <gi...@apache.org>.
rezarokni commented on code in PR #23035:
URL: https://github.com/apache/beam/pull/23035#discussion_r963999768
##########
examples/notebooks/beam-ml/run_custom_inference.ipynb:
##########
@@ -0,0 +1,570 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "C1rAsD2L-hSO",
+ "metadata": {
+ "cellView": "form",
+ "id": "C1rAsD2L-hSO"
+ },
+ "outputs": [],
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e",
+ "metadata": {
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e"
+ },
+ "source": [
+ "# Bring your own Machine Leanring (ML) model to Beam RunInference\n",
+ "\n",
+ "<button>\n",
+ " <a href=\"https://beam.apache.org/documentation/sdks/python-machine-learning/\">\n",
+ " <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+ " Beam RunInference\n",
+ " </a>\n",
+ "</button>\n",
+ "\n",
+ "In this notebook, we walk through a simple example to show how to build your own ML model handler using\n",
+ "[ModelHandler](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler).\n",
+ "\n",
+ "Named-Entity Recognition (NER) is one of the most common tasks for Natural Language Processing (NLP), \n",
+ "which locates and classifies named entities in unstructured text into pre-defined labels such as person name, organization, date, etc. \n",
+ "In this example, we illustrate how to use the popular spaCy package to load a ML model and perform inference in a Beam pipeline using RunInference PTransform.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd",
+ "metadata": {
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd"
+ },
+ "source": [
+ "## Package Dependencies\n",
+ "\n",
+ "The RunInference library is available in Apache Beam version <b>2.40</b> or later.\n",
+ "\n",
+ "`spaCy` and `pandas` need to be installed. Here, a small NER model (`en_core_web_sm`) is also installed but any valid spaCy model could be used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "outputId": "da04ccb9-0801-47f6-ec9e-e87f0ca4569f"
+ },
+ "outputs": [],
+ "source": [
+ "# uncomment these to install the required packages\n",
+ "# %pip install spacy pandas\n",
+ "# %pip install \"apache-beam[gcp, dataframe, interactive]\"\n",
+ "# !python -m spacy download en_core_web_sm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Let us play with spaCy first"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spaCy language\n",
+ "\n",
+ "import spacy\n",
+ "\n",
+ "nlp = spacy.load(\"en_core_web_sm\")\n"
Review Comment:
Please add documentation that explicitly calls out what the load() method is doing under the covers. Which I believes is:
Per Python process ( of which a worker may have many ) downloads the file to a temporary location and then loads it into memory.
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[GitHub] [beam] AnandInguva commented on a diff in pull request #23035: Add one NER example to use a spaCy model with RunInference
Posted by GitBox <gi...@apache.org>.
AnandInguva commented on code in PR #23035:
URL: https://github.com/apache/beam/pull/23035#discussion_r965007040
##########
examples/notebooks/beam-ml/run_custom_inference.ipynb:
##########
@@ -0,0 +1,575 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "C1rAsD2L-hSO",
+ "metadata": {
+ "cellView": "form",
+ "id": "C1rAsD2L-hSO"
+ },
+ "outputs": [],
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e",
+ "metadata": {
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e"
+ },
+ "source": [
+ "# Bring your own Machine Leanring (ML) model to Beam RunInference\n",
+ "\n",
+ "<button>\n",
+ " <a href=\"https://beam.apache.org/documentation/sdks/python-machine-learning/\">\n",
+ " <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+ " Beam RunInference\n",
+ " </a>\n",
+ "</button>\n",
+ "\n",
+ "In this notebook, we walk through a simple example to show how to customize your own ML model handler using\n",
+ "[ModelHandler](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler).\n",
+ "\n",
+ "Named-Entity Recognition (NER) is one of the most common tasks for Natural Language Processing (NLP), \n",
+ "which locates and classifies named entities in unstructured text into pre-defined labels such as person name, organization, date, etc. \n",
+ "In this example, we illustrate how to use the popular spaCy package to load a ML model and apply it inside a Beam pipeline.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd",
+ "metadata": {
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd"
+ },
+ "source": [
+ "## Package Dependencies\n",
+ "\n",
+ "The RunInference library is available in Apache Beam version <b>2.40</b> or later.\n",
+ "\n",
+ "`spaCy` and `pandas` need to be installed. Here, a small NER model (`en_core_web_sm`) is also installed but any valid spaCy model could be used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "outputId": "da04ccb9-0801-47f6-ec9e-e87f0ca4569f"
+ },
+ "outputs": [],
+ "source": [
+ "# uncomment these to install the required packages\n",
+ "# %pip install spacy pandas\n",
+ "# %pip install \"apache-beam[gcp, dataframe, interactive]\"\n",
+ "# !python -m spacy download en_core_web_sm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Let us play with spaCy first"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spaCy language\n",
+ "\n",
+ "import spacy\n",
+ "\n",
+ "nlp = spacy.load(\"en_core_web_sm\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# some text strings for fun\n",
+ "text_strings = [\n",
+ " \"The New York Times is an American daily newspaper based in New York City with a worldwide readership.\",\n",
+ " \"It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\"\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# check what entities spaCy can recognize\n",
+ "doc = nlp(text_strings[0])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times 0 18 ORG\n",
+ "American 25 33 NORP\n",
+ "daily 34 39 DATE\n",
+ "New York City 59 72 GPE\n"
+ ]
+ }
+ ],
+ "source": [
+ "for ent in doc.ents:\n",
+ " print(ent.text, ent.start_char, ent.end_char, ent.label_)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
+ "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " The New York Times\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
+ "</mark>\n",
+ " is an \n",
+ "<mark class=\"entity\" style=\"background: #c887fb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " American\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">NORP</span>\n",
+ "</mark>\n",
+ " \n",
+ "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " daily\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+ "</mark>\n",
+ " newspaper based in \n",
+ "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " New York City\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
+ "</mark>\n",
+ " with a worldwide readership.</div></span>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# visualize the results\n",
+ "from spacy import displacy\n",
+ "displacy.render(doc, style=\"ent\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">It was founded in \n",
+ "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " 1851\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+ "</mark>\n",
+ " by \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Henry Jarvis\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ " \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Raymond\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ " and \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " George Jones\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ ", and was initially published by \n",
+ "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Raymond, Jones & Company\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
+ "</mark>\n",
+ ".</div></span>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# another example\n",
+ "displacy.render(nlp(text_strings[1]), style=\"ent\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Now time to create our own `ModelHandler` to use spaCy for inference"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": "\n if (typeof window.interactive_beam_jquery == 'undefined') {\n var jqueryScript = document.createElement('script');\n jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n jqueryScript.type = 'text/javascript';\n jqueryScript.onload = function() {\n var datatableScript = document.createElement('script');\n datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n datatableScript.type = 'text/javascript';\n datatableScript.onload = function() {\n window.interactive_beam_jquery = jQuery.noConflict(true);\n window.interactive_beam_jquery(document).ready(function($){\n \n });\n }\n document.head.appendChild(datatableScript);\n };\n document.head.appendChild(jqueryScript);\n } else {\n window.interactive
_beam_jquery(document).ready(function($){\n \n });\n }"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times is an American daily newspaper based in New York City with a worldwide readership.\n",
+ "It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "import apache_beam as beam\n",
+ "from apache_beam.options.pipeline_options import PipelineOptions\n",
+ "\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "\n",
+ "pipeline = beam.Pipeline()\n",
+ "\n",
+ "# only print the results to check\n",
+ "with pipeline as p:\n",
+ " (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings)\n",
+ " | beam.Map(print)\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Now define SpacyModelHandler to load the model and perform the inference\n",
+ "\n",
+ "from apache_beam.ml.inference.base import RunInference\n",
+ "from apache_beam.ml.inference.base import ModelHandler\n",
+ "from apache_beam.ml.inference.base import PredictionResult\n",
+ "from spacy import Language\n",
+ "from typing import Any\n",
+ "from typing import Dict\n",
+ "from typing import Iterable\n",
+ "from typing import Optional\n",
+ "from typing import Sequence\n",
+ "\n",
+ "class SpacyModelHandler(ModelHandler[str,\n",
+ " PredictionResult,\n",
+ " Language]):\n",
+ " def __init__(\n",
+ " self,\n",
+ " model_name: str = \"en_core_web_sm\",\n",
+ " ):\n",
+ " \"\"\" Implementation of the ModelHandler interface for spaCy using text as input.\n",
+ "\n",
+ " Example Usage::\n",
+ "\n",
+ " pcoll | RunInference(SpacyModelHandler())\n",
+ "\n",
+ " Args:\n",
+ " model_name: The spaCy model name. Default is en_core_web_sm.\n",
+ " \"\"\"\n",
+ " self._model_name = model_name\n",
+ "\n",
+ " def load_model(self) -> Language:\n",
+ " \"\"\"Loads and initializes a model for processing.\"\"\"\n",
+ " return spacy.load(self._model_name)\n",
+ "\n",
+ " def run_inference(\n",
+ " self,\n",
+ " batch: Sequence[str],\n",
+ " model: Language,\n",
+ " inference_args: Optional[Dict[str, Any]] = None\n",
+ " ) -> Iterable[PredictionResult]:\n",
+ " \"\"\"Runs inferences on a batch of text strings.\n",
+ "\n",
+ " Args:\n",
+ " batch: A sequence of examples as text strings. \n",
+ " model: A spaCy language model\n",
+ " inference_args: Any additional arguments for an inference.\n",
+ "\n",
+ " Returns:\n",
+ " An Iterable of type PredictionResult.\n",
+ " \"\"\"\n",
+ " # loop each text string and use tuple to store the inference results\n",
+ " predictions = []\n",
+ " for one_text in batch:\n",
+ " doc = model(one_text)\n",
+ " predictions.append(\n",
+ " [(ent.text, ent.start_char, ent.end_char, ent.label_) for ent in doc.ents])\n",
+ " return [PredictionResult(x, y) for x, y in zip(batch, predictions)]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times is an American daily newspaper based in New York City with a worldwide readership.\n",
+ "It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\n",
+ "PredictionResult(example='The New York Times is an American daily newspaper based in New York City with a worldwide readership.', inference=[('The New York Times', 0, 18, 'ORG'), ('American', 25, 33, 'NORP'), ('daily', 34, 39, 'DATE'), ('New York City', 59, 72, 'GPE')])\n",
+ "PredictionResult(example='It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.', inference=[('1851', 18, 22, 'DATE'), ('Henry Jarvis', 26, 38, 'PERSON'), ('Raymond', 39, 46, 'PERSON'), ('George Jones', 51, 63, 'PERSON'), ('Raymond, Jones & Company', 96, 120, 'ORG')])\n"
+ ]
+ }
+ ],
+ "source": [
+ "# quick check to show the inference results are correct\n",
+ "with pipeline as p:\n",
+ " (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings)\n",
+ " | \"RunInferenceSpacy\" >> RunInference(SpacyModelHandler(\"en_core_web_sm\"))\n",
+ " | beam.Map(print)\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Use `KeyedModelHandler` to handle keyed data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# some text strings with keys, which are useful to distinguish examples\n",
+ "text_strings_with_keys = [\n",
+ " (\"example_0\", \"The New York Times is an American daily newspaper based in New York City with a worldwide readership.\"),\n",
+ " (\"example_1\", \"It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\")\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from apache_beam.runners.interactive.interactive_runner import InteractiveRunner\n",
+ "from apache_beam.ml.inference.base import KeyedModelHandler\n",
+ "from apache_beam.dataframe.convert import to_dataframe\n",
+ "\n",
+ "pipeline = beam.Pipeline(InteractiveRunner())\n",
+ "\n",
+ "keyed_spacy_model_handler = KeyedModelHandler(SpacyModelHandler(\"en_core_web_sm\"))\n",
+ "\n",
+ "# quick check to show the inference results are correct\n",
+ "with pipeline as p:\n",
+ " results = (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings_with_keys)\n",
+ " | \"RunInferenceSpacy\" >> RunInference(keyed_spacy_model_handler)\n",
+ " # Map to Row objects to generate a schema suitable for conversion\n",
+ " # to a dataframe.\n",
+ " | 'ToRows' >> beam.Map(lambda row: beam.Row(key=row[0], text=row[1][0], predictions=row[1][1]))\n",
Review Comment:
I guess that is fine. I thought it would be good to have the print statement same across the cells.
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[GitHub] [beam] liferoad commented on pull request #23035: Add one NER example to use a spaCy model with RunInference
Posted by GitBox <gi...@apache.org>.
liferoad commented on PR #23035:
URL: https://github.com/apache/beam/pull/23035#issuecomment-1239693766
Can someone merge this PR if it looks good?
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[GitHub] [beam] AnandInguva commented on a diff in pull request #23035: Add one NER example to use a spaCy model with RunInference
Posted by GitBox <gi...@apache.org>.
AnandInguva commented on code in PR #23035:
URL: https://github.com/apache/beam/pull/23035#discussion_r963796642
##########
examples/notebooks/beam-ml/run_custom_inference.ipynb:
##########
@@ -0,0 +1,575 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "C1rAsD2L-hSO",
+ "metadata": {
+ "cellView": "form",
+ "id": "C1rAsD2L-hSO"
+ },
+ "outputs": [],
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e",
+ "metadata": {
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e"
+ },
+ "source": [
+ "# Bring your own Machine Leanring (ML) model to Beam RunInference\n",
+ "\n",
+ "<button>\n",
+ " <a href=\"https://beam.apache.org/documentation/sdks/python-machine-learning/\">\n",
+ " <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+ " Beam RunInference\n",
+ " </a>\n",
+ "</button>\n",
+ "\n",
+ "In this notebook, we walk through a simple example to show how to customize your own ML model handler using\n",
+ "[ModelHandler](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler).\n",
+ "\n",
+ "Named-Entity Recognition (NER) is one of the most common tasks for Natural Language Processing (NLP), \n",
+ "which locates and classifies named entities in unstructured text into pre-defined labels such as person name, organization, date, etc. \n",
+ "In this example, we illustrate how to use the popular spaCy package to load a ML model and apply it inside a Beam pipeline.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd",
+ "metadata": {
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd"
+ },
+ "source": [
+ "## Package Dependencies\n",
+ "\n",
+ "The RunInference library is available in Apache Beam version <b>2.40</b> or later.\n",
+ "\n",
+ "`spaCy` and `pandas` need to be installed. Here, a small NER model (`en_core_web_sm`) is also installed but any valid spaCy model could be used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "outputId": "da04ccb9-0801-47f6-ec9e-e87f0ca4569f"
+ },
+ "outputs": [],
+ "source": [
+ "# uncomment these to install the required packages\n",
+ "# %pip install spacy pandas\n",
+ "# %pip install \"apache-beam[gcp, dataframe, interactive]\"\n",
+ "# !python -m spacy download en_core_web_sm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Let us play with spaCy first"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spaCy language\n",
+ "\n",
+ "import spacy\n",
+ "\n",
+ "nlp = spacy.load(\"en_core_web_sm\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# some text strings for fun\n",
+ "text_strings = [\n",
+ " \"The New York Times is an American daily newspaper based in New York City with a worldwide readership.\",\n",
+ " \"It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\"\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# check what entities spaCy can recognize\n",
+ "doc = nlp(text_strings[0])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times 0 18 ORG\n",
+ "American 25 33 NORP\n",
+ "daily 34 39 DATE\n",
+ "New York City 59 72 GPE\n"
+ ]
+ }
+ ],
+ "source": [
+ "for ent in doc.ents:\n",
+ " print(ent.text, ent.start_char, ent.end_char, ent.label_)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
+ "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " The New York Times\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
+ "</mark>\n",
+ " is an \n",
+ "<mark class=\"entity\" style=\"background: #c887fb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " American\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">NORP</span>\n",
+ "</mark>\n",
+ " \n",
+ "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " daily\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+ "</mark>\n",
+ " newspaper based in \n",
+ "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " New York City\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
+ "</mark>\n",
+ " with a worldwide readership.</div></span>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# visualize the results\n",
+ "from spacy import displacy\n",
+ "displacy.render(doc, style=\"ent\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">It was founded in \n",
+ "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " 1851\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+ "</mark>\n",
+ " by \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Henry Jarvis\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ " \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Raymond\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ " and \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " George Jones\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ ", and was initially published by \n",
+ "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Raymond, Jones & Company\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
+ "</mark>\n",
+ ".</div></span>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# another example\n",
+ "displacy.render(nlp(text_strings[1]), style=\"ent\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Now time to create our own `ModelHandler` to use spaCy for inference"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": "\n if (typeof window.interactive_beam_jquery == 'undefined') {\n var jqueryScript = document.createElement('script');\n jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n jqueryScript.type = 'text/javascript';\n jqueryScript.onload = function() {\n var datatableScript = document.createElement('script');\n datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n datatableScript.type = 'text/javascript';\n datatableScript.onload = function() {\n window.interactive_beam_jquery = jQuery.noConflict(true);\n window.interactive_beam_jquery(document).ready(function($){\n \n });\n }\n document.head.appendChild(datatableScript);\n };\n document.head.appendChild(jqueryScript);\n } else {\n window.interactive
_beam_jquery(document).ready(function($){\n \n });\n }"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times is an American daily newspaper based in New York City with a worldwide readership.\n",
+ "It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "import apache_beam as beam\n",
+ "from apache_beam.options.pipeline_options import PipelineOptions\n",
+ "\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "\n",
+ "pipeline = beam.Pipeline()\n",
+ "\n",
+ "# only print the results to check\n",
+ "with pipeline as p:\n",
+ " (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings)\n",
+ " | beam.Map(print)\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Now define SpacyModelHandler to load the model and perform the inference\n",
+ "\n",
+ "from apache_beam.ml.inference.base import RunInference\n",
+ "from apache_beam.ml.inference.base import ModelHandler\n",
+ "from apache_beam.ml.inference.base import PredictionResult\n",
+ "from spacy import Language\n",
+ "from typing import Any\n",
+ "from typing import Dict\n",
+ "from typing import Iterable\n",
+ "from typing import Optional\n",
+ "from typing import Sequence\n",
+ "\n",
+ "class SpacyModelHandler(ModelHandler[str,\n",
+ " PredictionResult,\n",
+ " Language]):\n",
+ " def __init__(\n",
+ " self,\n",
+ " model_name: str = \"en_core_web_sm\",\n",
+ " ):\n",
+ " \"\"\" Implementation of the ModelHandler interface for spaCy using text as input.\n",
+ "\n",
+ " Example Usage::\n",
+ "\n",
+ " pcoll | RunInference(SpacyModelHandler())\n",
+ "\n",
+ " Args:\n",
+ " model_name: The spaCy model name. Default is en_core_web_sm.\n",
+ " \"\"\"\n",
+ " self._model_name = model_name\n",
+ "\n",
+ " def load_model(self) -> Language:\n",
+ " \"\"\"Loads and initializes a model for processing.\"\"\"\n",
+ " return spacy.load(self._model_name)\n",
+ "\n",
+ " def run_inference(\n",
+ " self,\n",
+ " batch: Sequence[str],\n",
+ " model: Language,\n",
+ " inference_args: Optional[Dict[str, Any]] = None\n",
+ " ) -> Iterable[PredictionResult]:\n",
+ " \"\"\"Runs inferences on a batch of text strings.\n",
+ "\n",
+ " Args:\n",
+ " batch: A sequence of examples as text strings. \n",
+ " model: A spaCy language model\n",
+ " inference_args: Any additional arguments for an inference.\n",
+ "\n",
+ " Returns:\n",
+ " An Iterable of type PredictionResult.\n",
+ " \"\"\"\n",
+ " # loop each text string and use tuple to store the inference results\n",
+ " predictions = []\n",
+ " for one_text in batch:\n",
+ " doc = model(one_text)\n",
+ " predictions.append(\n",
+ " [(ent.text, ent.start_char, ent.end_char, ent.label_) for ent in doc.ents])\n",
+ " return [PredictionResult(x, y) for x, y in zip(batch, predictions)]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times is an American daily newspaper based in New York City with a worldwide readership.\n",
+ "It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\n",
+ "PredictionResult(example='The New York Times is an American daily newspaper based in New York City with a worldwide readership.', inference=[('The New York Times', 0, 18, 'ORG'), ('American', 25, 33, 'NORP'), ('daily', 34, 39, 'DATE'), ('New York City', 59, 72, 'GPE')])\n",
+ "PredictionResult(example='It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.', inference=[('1851', 18, 22, 'DATE'), ('Henry Jarvis', 26, 38, 'PERSON'), ('Raymond', 39, 46, 'PERSON'), ('George Jones', 51, 63, 'PERSON'), ('Raymond, Jones & Company', 96, 120, 'ORG')])\n"
+ ]
+ }
+ ],
+ "source": [
+ "# quick check to show the inference results are correct\n",
+ "with pipeline as p:\n",
+ " (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings)\n",
+ " | \"RunInferenceSpacy\" >> RunInference(SpacyModelHandler(\"en_core_web_sm\"))\n",
+ " | beam.Map(print)\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Use `KeyedModelHandler` to handle keyed data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# some text strings with keys, which are useful to distinguish examples\n",
+ "text_strings_with_keys = [\n",
+ " (\"example_0\", \"The New York Times is an American daily newspaper based in New York City with a worldwide readership.\"),\n",
+ " (\"example_1\", \"It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\")\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from apache_beam.runners.interactive.interactive_runner import InteractiveRunner\n",
+ "from apache_beam.ml.inference.base import KeyedModelHandler\n",
+ "from apache_beam.dataframe.convert import to_dataframe\n",
+ "\n",
+ "pipeline = beam.Pipeline(InteractiveRunner())\n",
+ "\n",
+ "keyed_spacy_model_handler = KeyedModelHandler(SpacyModelHandler(\"en_core_web_sm\"))\n",
+ "\n",
+ "# quick check to show the inference results are correct\n",
+ "with pipeline as p:\n",
+ " results = (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings_with_keys)\n",
+ " | \"RunInferenceSpacy\" >> RunInference(keyed_spacy_model_handler)\n",
+ " # Map to Row objects to generate a schema suitable for conversion\n",
+ " # to a dataframe.\n",
+ " | 'ToRows' >> beam.Map(lambda row: beam.Row(key=row[0], text=row[1][0], predictions=row[1][1]))\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <link rel=\"stylesheet\" href=\"https://stackpath.bootstrapcdn.com/bootstrap/4.4.1/css/bootstrap.min.css\" integrity=\"sha384-Vkoo8x4CGsO3+Hhxv8T/Q5PaXtkKtu6ug5TOeNV6gBiFeWPGFN9MuhOf23Q9Ifjh\" crossorigin=\"anonymous\">\n",
+ " <div id=\"progress_indicator_25aaf10d571c025a28901bea46b94c93\">\n",
+ " <div class=\"spinner-border text-info\" role=\"status\"></div>\n",
+ " <span class=\"text-info\">Processing... collect</span>\n",
+ " </div>\n",
+ " "
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": "\n if (typeof window.interactive_beam_jquery == 'undefined') {\n var jqueryScript = document.createElement('script');\n jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n jqueryScript.type = 'text/javascript';\n jqueryScript.onload = function() {\n var datatableScript = document.createElement('script');\n datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n datatableScript.type = 'text/javascript';\n datatableScript.onload = function() {\n window.interactive_beam_jquery = jQuery.noConflict(true);\n window.interactive_beam_jquery(document).ready(function($){\n \n $(\"#progress_indicator_25aaf10d571c025a28901bea46b94c93\").remove();\n });\n }\n document.head.appendChild(datatableScript);\n };\n docum
ent.head.appendChild(jqueryScript);\n } else {\n window.interactive_beam_jquery(document).ready(function($){\n \n $(\"#progress_indicator_25aaf10d571c025a28901bea46b94c93\").remove();\n });\n }"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# convert results to a pandas dataframe\n",
+ "import apache_beam.runners.interactive.interactive_beam as ib\n",
+ "\n",
+ "beam_df = to_dataframe(results)\n",
+ "df = ib.collect(beam_df)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>key</th>\n",
+ " <th>text</th>\n",
+ " <th>predictions</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>example_0</td>\n",
+ " <td>The New York Times is an American daily newspa...</td>\n",
+ " <td>[(The New York Times, 0, 18, ORG), (American, ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>example_1</td>\n",
+ " <td>It was founded in 1851 by Henry Jarvis Raymond...</td>\n",
+ " <td>[(1851, 18, 22, DATE), (Henry Jarvis, 26, 38, ...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " key text \\\n",
+ "0 example_0 The New York Times is an American daily newspa... \n",
+ "0 example_1 It was founded in 1851 by Henry Jarvis Raymond... \n",
+ "\n",
+ " predictions \n",
+ "0 [(The New York Times, 0, 18, ORG), (American, ... \n",
+ "0 [(1851, 18, 22, DATE), (Henry Jarvis, 26, 38, ... "
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "Beam RunInference",
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
Review Comment:
We can remove the `kernelspec` before merging.
##########
examples/notebooks/beam-ml/run_custom_inference.ipynb:
##########
@@ -0,0 +1,575 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "C1rAsD2L-hSO",
+ "metadata": {
+ "cellView": "form",
+ "id": "C1rAsD2L-hSO"
+ },
+ "outputs": [],
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF), Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n",
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e",
+ "metadata": {
+ "id": "b6f8f3af-744e-4eaa-8a30-6d03e8e4d21e"
+ },
+ "source": [
+ "# Bring your own Machine Leanring (ML) model to Beam RunInference\n",
+ "\n",
+ "<button>\n",
+ " <a href=\"https://beam.apache.org/documentation/sdks/python-machine-learning/\">\n",
+ " <img src=\"https://beam.apache.org/images/favicon.ico\" alt=\"Open the docs\" height=\"16\"/>\n",
+ " Beam RunInference\n",
+ " </a>\n",
+ "</button>\n",
+ "\n",
+ "In this notebook, we walk through a simple example to show how to customize your own ML model handler using\n",
+ "[ModelHandler](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelHandler).\n",
+ "\n",
+ "Named-Entity Recognition (NER) is one of the most common tasks for Natural Language Processing (NLP), \n",
+ "which locates and classifies named entities in unstructured text into pre-defined labels such as person name, organization, date, etc. \n",
+ "In this example, we illustrate how to use the popular spaCy package to load a ML model and apply it inside a Beam pipeline.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd",
+ "metadata": {
+ "id": "299af9bb-b2fc-405c-96e7-ee0a6ae24bdd"
+ },
+ "source": [
+ "## Package Dependencies\n",
+ "\n",
+ "The RunInference library is available in Apache Beam version <b>2.40</b> or later.\n",
+ "\n",
+ "`spaCy` and `pandas` need to be installed. Here, a small NER model (`en_core_web_sm`) is also installed but any valid spaCy model could be used."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7f841596-f217-46d2-b64e-1952db4de4cb",
+ "outputId": "da04ccb9-0801-47f6-ec9e-e87f0ca4569f"
+ },
+ "outputs": [],
+ "source": [
+ "# uncomment these to install the required packages\n",
+ "# %pip install spacy pandas\n",
+ "# %pip install \"apache-beam[gcp, dataframe, interactive]\"\n",
+ "# !python -m spacy download en_core_web_sm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Let us play with spaCy first"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# create a spaCy language\n",
+ "\n",
+ "import spacy\n",
+ "\n",
+ "nlp = spacy.load(\"en_core_web_sm\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# some text strings for fun\n",
+ "text_strings = [\n",
+ " \"The New York Times is an American daily newspaper based in New York City with a worldwide readership.\",\n",
+ " \"It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\"\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# check what entities spaCy can recognize\n",
+ "doc = nlp(text_strings[0])\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times 0 18 ORG\n",
+ "American 25 33 NORP\n",
+ "daily 34 39 DATE\n",
+ "New York City 59 72 GPE\n"
+ ]
+ }
+ ],
+ "source": [
+ "for ent in doc.ents:\n",
+ " print(ent.text, ent.start_char, ent.end_char, ent.label_)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
+ "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " The New York Times\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
+ "</mark>\n",
+ " is an \n",
+ "<mark class=\"entity\" style=\"background: #c887fb; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " American\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">NORP</span>\n",
+ "</mark>\n",
+ " \n",
+ "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " daily\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+ "</mark>\n",
+ " newspaper based in \n",
+ "<mark class=\"entity\" style=\"background: #feca74; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " New York City\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">GPE</span>\n",
+ "</mark>\n",
+ " with a worldwide readership.</div></span>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# visualize the results\n",
+ "from spacy import displacy\n",
+ "displacy.render(doc, style=\"ent\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">It was founded in \n",
+ "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " 1851\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
+ "</mark>\n",
+ " by \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Henry Jarvis\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ " \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Raymond\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ " and \n",
+ "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " George Jones\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
+ "</mark>\n",
+ ", and was initially published by \n",
+ "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
+ " Raymond, Jones & Company\n",
+ " <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
+ "</mark>\n",
+ ".</div></span>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# another example\n",
+ "displacy.render(nlp(text_strings[1]), style=\"ent\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Now time to create our own `ModelHandler` to use spaCy for inference"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": "\n if (typeof window.interactive_beam_jquery == 'undefined') {\n var jqueryScript = document.createElement('script');\n jqueryScript.src = 'https://code.jquery.com/jquery-3.4.1.slim.min.js';\n jqueryScript.type = 'text/javascript';\n jqueryScript.onload = function() {\n var datatableScript = document.createElement('script');\n datatableScript.src = 'https://cdn.datatables.net/1.10.20/js/jquery.dataTables.min.js';\n datatableScript.type = 'text/javascript';\n datatableScript.onload = function() {\n window.interactive_beam_jquery = jQuery.noConflict(true);\n window.interactive_beam_jquery(document).ready(function($){\n \n });\n }\n document.head.appendChild(datatableScript);\n };\n document.head.appendChild(jqueryScript);\n } else {\n window.interactive
_beam_jquery(document).ready(function($){\n \n });\n }"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times is an American daily newspaper based in New York City with a worldwide readership.\n",
+ "It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\n"
+ ]
+ }
+ ],
+ "source": [
+ "\n",
+ "import apache_beam as beam\n",
+ "from apache_beam.options.pipeline_options import PipelineOptions\n",
+ "\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "\n",
+ "\n",
+ "pipeline = beam.Pipeline()\n",
+ "\n",
+ "# only print the results to check\n",
+ "with pipeline as p:\n",
+ " (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings)\n",
+ " | beam.Map(print)\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Now define SpacyModelHandler to load the model and perform the inference\n",
+ "\n",
+ "from apache_beam.ml.inference.base import RunInference\n",
+ "from apache_beam.ml.inference.base import ModelHandler\n",
+ "from apache_beam.ml.inference.base import PredictionResult\n",
+ "from spacy import Language\n",
+ "from typing import Any\n",
+ "from typing import Dict\n",
+ "from typing import Iterable\n",
+ "from typing import Optional\n",
+ "from typing import Sequence\n",
+ "\n",
+ "class SpacyModelHandler(ModelHandler[str,\n",
+ " PredictionResult,\n",
+ " Language]):\n",
+ " def __init__(\n",
+ " self,\n",
+ " model_name: str = \"en_core_web_sm\",\n",
+ " ):\n",
+ " \"\"\" Implementation of the ModelHandler interface for spaCy using text as input.\n",
+ "\n",
+ " Example Usage::\n",
+ "\n",
+ " pcoll | RunInference(SpacyModelHandler())\n",
+ "\n",
+ " Args:\n",
+ " model_name: The spaCy model name. Default is en_core_web_sm.\n",
+ " \"\"\"\n",
+ " self._model_name = model_name\n",
+ "\n",
+ " def load_model(self) -> Language:\n",
+ " \"\"\"Loads and initializes a model for processing.\"\"\"\n",
+ " return spacy.load(self._model_name)\n",
+ "\n",
+ " def run_inference(\n",
+ " self,\n",
+ " batch: Sequence[str],\n",
+ " model: Language,\n",
+ " inference_args: Optional[Dict[str, Any]] = None\n",
+ " ) -> Iterable[PredictionResult]:\n",
+ " \"\"\"Runs inferences on a batch of text strings.\n",
+ "\n",
+ " Args:\n",
+ " batch: A sequence of examples as text strings. \n",
+ " model: A spaCy language model\n",
+ " inference_args: Any additional arguments for an inference.\n",
+ "\n",
+ " Returns:\n",
+ " An Iterable of type PredictionResult.\n",
+ " \"\"\"\n",
+ " # loop each text string and use tuple to store the inference results\n",
+ " predictions = []\n",
+ " for one_text in batch:\n",
+ " doc = model(one_text)\n",
+ " predictions.append(\n",
+ " [(ent.text, ent.start_char, ent.end_char, ent.label_) for ent in doc.ents])\n",
+ " return [PredictionResult(x, y) for x, y in zip(batch, predictions)]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The New York Times is an American daily newspaper based in New York City with a worldwide readership.\n",
+ "It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\n",
+ "PredictionResult(example='The New York Times is an American daily newspaper based in New York City with a worldwide readership.', inference=[('The New York Times', 0, 18, 'ORG'), ('American', 25, 33, 'NORP'), ('daily', 34, 39, 'DATE'), ('New York City', 59, 72, 'GPE')])\n",
+ "PredictionResult(example='It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.', inference=[('1851', 18, 22, 'DATE'), ('Henry Jarvis', 26, 38, 'PERSON'), ('Raymond', 39, 46, 'PERSON'), ('George Jones', 51, 63, 'PERSON'), ('Raymond, Jones & Company', 96, 120, 'ORG')])\n"
+ ]
+ }
+ ],
+ "source": [
+ "# quick check to show the inference results are correct\n",
+ "with pipeline as p:\n",
+ " (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings)\n",
+ " | \"RunInferenceSpacy\" >> RunInference(SpacyModelHandler(\"en_core_web_sm\"))\n",
+ " | beam.Map(print)\n",
+ " )\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Use `KeyedModelHandler` to handle keyed data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# some text strings with keys, which are useful to distinguish examples\n",
+ "text_strings_with_keys = [\n",
+ " (\"example_0\", \"The New York Times is an American daily newspaper based in New York City with a worldwide readership.\"),\n",
+ " (\"example_1\", \"It was founded in 1851 by Henry Jarvis Raymond and George Jones, and was initially published by Raymond, Jones & Company.\")\n",
+ "]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from apache_beam.runners.interactive.interactive_runner import InteractiveRunner\n",
+ "from apache_beam.ml.inference.base import KeyedModelHandler\n",
+ "from apache_beam.dataframe.convert import to_dataframe\n",
+ "\n",
+ "pipeline = beam.Pipeline(InteractiveRunner())\n",
+ "\n",
+ "keyed_spacy_model_handler = KeyedModelHandler(SpacyModelHandler(\"en_core_web_sm\"))\n",
+ "\n",
+ "# quick check to show the inference results are correct\n",
+ "with pipeline as p:\n",
+ " results = (p \n",
+ " | \"CreateSentences\" >> beam.Create(text_strings_with_keys)\n",
+ " | \"RunInferenceSpacy\" >> RunInference(keyed_spacy_model_handler)\n",
+ " # Map to Row objects to generate a schema suitable for conversion\n",
+ " # to a dataframe.\n",
+ " | 'ToRows' >> beam.Map(lambda row: beam.Row(key=row[0], text=row[1][0], predictions=row[1][1]))\n",
Review Comment:
Can we have `beam.Map(print)` here to print the pipeline output? just to match how we print in the other cells. Also, we. could remove the next cells of creating `df` and printing it, making this more simpler.
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