Re: [PR] Add notebooks for text embeddings [beam]

[via GitHub]

damccorm commented on code in PR #29893:
URL: https://github.com/apache/beam/pull/29893#discussion_r1439774797


##########
examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb:
##########
@@ -0,0 +1,456 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "UmEFwsNs1OES"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate Text Embeddings by using Hugging Face Hub models\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\";
 />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\";
 />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ],
+      "metadata": {
+        "id": "ZUSiAR62SgO8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "## Text Embeddings\n",
+        "\n",
+        "Text embeddings are a way of representing text as numerical vectors. 
This allows computers to understand and process text data, which is essential 
for many natural language processing (NLP) tasks.\n",
+        "\n",
+        "### Uses of text embeddings\n",
+        "By converting text into numerical vectors, text embeddings make it 
possible for computers to process and analyze text data. This enables a wide 
range of NLP tasks, including:\n",

Review Comment:
   (optional): This has some redundancy with the previous sentence (outside of 
this header). Maybe just remove this line and say `Some NLP tasks that use text 
embeddings include:`



##########
examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb:
##########
@@ -0,0 +1,456 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "UmEFwsNs1OES"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate Text Embeddings by using Hugging Face Hub models\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\";
 />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\";
 />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ],
+      "metadata": {
+        "id": "ZUSiAR62SgO8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "## Text Embeddings\n",
+        "\n",
+        "Text embeddings are a way of representing text as numerical vectors. 
This allows computers to understand and process text data, which is essential 
for many natural language processing (NLP) tasks.\n",
+        "\n",
+        "### Uses of text embeddings\n",
+        "By converting text into numerical vectors, text embeddings make it 
possible for computers to process and analyze text data. This enables a wide 
range of NLP tasks, including:\n",
+        "\n",
+        "* Semantic search: Finding documents or passages that are relevant to 
a query, even if the query doesn't use the exact same words as the 
documents.\n",
+        "* Text classification: Categorzing text data into different classes, 
such as spam or not spam, or positive sentiment or negative sentiment.\n",
+        "* Machine translation: Translating text from one language to another 
while preserving the meaning.\n",
+        "* Text summarization: Creating shorter summaries of longer pieces of 
text.\n",
+        "\n",
+        "In this notebook, we will use Apache Beam's `MLTransform` to 
embeddings on the text data.\n",
+        "\n",
+        "Hugging Face's 
[`SentenceTransformers`](https://huggingface.co/sentence-transformers) 
framework uses Python to generate sentence, text, and image embeddings.\n",
+        "\n",
+        "To generate text embeddings that use Hugging Face models and 
`MLTransform`, use `SentenceTransformerEmbeddings` to specify the model 
configuration.\n",
+        "\n",
+        "To use `SentenceTransformerEmbeddings`, first install the `the 
sentence-transformers` package."

Review Comment:
   This probably belongs in the next section. Maybe something like:
   
   ```
   Install Apache Beam and the dependencies needed to work with Hugging Face 
embeddings. This includes the `sentence-transformers package` which is required 
to use the `SentenceTransformerEmbeddings` module.
   ```



##########
examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb:
##########
@@ -0,0 +1,456 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "UmEFwsNs1OES"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate Text Embeddings by using Hugging Face Hub models\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\";
 />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\";
 />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ],
+      "metadata": {
+        "id": "ZUSiAR62SgO8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "## Text Embeddings\n",
+        "\n",
+        "Text embeddings are a way of representing text as numerical vectors. 
This allows computers to understand and process text data, which is essential 
for many natural language processing (NLP) tasks.\n",
+        "\n",
+        "### Uses of text embeddings\n",
+        "By converting text into numerical vectors, text embeddings make it 
possible for computers to process and analyze text data. This enables a wide 
range of NLP tasks, including:\n",
+        "\n",
+        "* Semantic search: Finding documents or passages that are relevant to 
a query, even if the query doesn't use the exact same words as the 
documents.\n",
+        "* Text classification: Categorzing text data into different classes, 
such as spam or not spam, or positive sentiment or negative sentiment.\n",
+        "* Machine translation: Translating text from one language to another 
while preserving the meaning.\n",
+        "* Text summarization: Creating shorter summaries of longer pieces of 
text.\n",
+        "\n",
+        "In this notebook, we will use Apache Beam's `MLTransform` to 
embeddings on the text data.\n",
+        "\n",
+        "Hugging Face's 
[`SentenceTransformers`](https://huggingface.co/sentence-transformers) 
framework uses Python to generate sentence, text, and image embeddings.\n",
+        "\n",
+        "To generate text embeddings that use Hugging Face models and 
`MLTransform`, use `SentenceTransformerEmbeddings` to specify the model 
configuration.\n",
+        "\n",
+        "To use `SentenceTransformerEmbeddings`, first install the `the 
sentence-transformers` package."
+      ],
+      "metadata": {
+        "id": "yvVIEhF01ZWq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Install dependencies\n",
+        " Install Apache Beam and the dependencies needed to work with Hugging 
Face embeddings."
+      ],
+      "metadata": {
+        "id": "jqYXaBJ821Zs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "! git clone https://github.com/apache/beam.git\n";,
+        "! cd beam/sdks/python\n",
+        "! pip install beam/sdks/python\n",
+        "! pip install sentence-transformers"
+      ],
+      "metadata": {
+        "id": "shzCUrZI1XhF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tempfile\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.ml.transforms.base import MLTransform\n",
+        "from apache_beam.ml.transforms.embeddings.huggingface import 
SentenceTransformerEmbeddings"
+      ],
+      "metadata": {
+        "id": "jVxSi2jS3M3b"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Use MLTransform in write mode\n",
+        "\n",
+        "In `write` mode, `MLTransform` saves the transforms and their 
attributes to an artifact location. These transforms are used when you run 
`MLTransform` in `read` mode.\n",
+        "\n",
+        "For more information about using `MLTransform`, see [Preprocess data 
with MLTransform](https://beam.apache.org/documentation/ml/preprocess-data/) in 
the Apache Beam documentation."
+      ],
+      "metadata": {
+        "id": "kXDM8C7d3nPV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To generate text embeddings with `MLTransform`, the following 
pipeline uses the model `sentence-transformers/all-MiniLM-L6-v2` and the text 
inputs from the Hugging Face blog [Getting Started With 
Embeddings](https://huggingface.co/blog/getting-started-with-embeddings)."

Review Comment:
   I think the column piece might be less natural for people here, so maybe we 
could add a sentence that explains that MLTransform operates on columns 
specified in the `SentenceTransformerEmbeddings` column



##########
examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb:
##########
@@ -0,0 +1,456 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "UmEFwsNs1OES"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate Text Embeddings by using Hugging Face Hub models\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\";
 />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\";
 />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ],
+      "metadata": {
+        "id": "ZUSiAR62SgO8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "## Text Embeddings\n",
+        "\n",
+        "Text embeddings are a way of representing text as numerical vectors. 
This allows computers to understand and process text data, which is essential 
for many natural language processing (NLP) tasks.\n",
+        "\n",
+        "### Uses of text embeddings\n",
+        "By converting text into numerical vectors, text embeddings make it 
possible for computers to process and analyze text data. This enables a wide 
range of NLP tasks, including:\n",
+        "\n",
+        "* Semantic search: Finding documents or passages that are relevant to 
a query, even if the query doesn't use the exact same words as the 
documents.\n",
+        "* Text classification: Categorzing text data into different classes, 
such as spam or not spam, or positive sentiment or negative sentiment.\n",

Review Comment:
   ```suggestion
           "* Text classification: Categorizng text data into different 
classes, such as spam or not spam, or positive sentiment or negative 
sentiment.\n",
   ```



##########
examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb:
##########
@@ -0,0 +1,456 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "UmEFwsNs1OES"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate Text Embeddings by using Hugging Face Hub models\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\";
 />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\";
 />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ],
+      "metadata": {
+        "id": "ZUSiAR62SgO8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "## Text Embeddings\n",
+        "\n",
+        "Text embeddings are a way of representing text as numerical vectors. 
This allows computers to understand and process text data, which is essential 
for many natural language processing (NLP) tasks.\n",
+        "\n",
+        "### Uses of text embeddings\n",
+        "By converting text into numerical vectors, text embeddings make it 
possible for computers to process and analyze text data. This enables a wide 
range of NLP tasks, including:\n",
+        "\n",
+        "* Semantic search: Finding documents or passages that are relevant to 
a query, even if the query doesn't use the exact same words as the 
documents.\n",
+        "* Text classification: Categorzing text data into different classes, 
such as spam or not spam, or positive sentiment or negative sentiment.\n",
+        "* Machine translation: Translating text from one language to another 
while preserving the meaning.\n",
+        "* Text summarization: Creating shorter summaries of longer pieces of 
text.\n",
+        "\n",
+        "In this notebook, we will use Apache Beam's `MLTransform` to 
embeddings on the text data.\n",

Review Comment:
   ```suggestion
           "In this notebook, we will use Apache Beam's `MLTransform` to 
generate embeddings from text data.\n",
   ```



##########
examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb:
##########
@@ -0,0 +1,456 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "UmEFwsNs1OES"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate Text Embeddings by using Hugging Face Hub models\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\";
 />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\";
 />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ],
+      "metadata": {
+        "id": "ZUSiAR62SgO8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "## Text Embeddings\n",
+        "\n",
+        "Text embeddings are a way of representing text as numerical vectors. 
This allows computers to understand and process text data, which is essential 
for many natural language processing (NLP) tasks.\n",
+        "\n",
+        "### Uses of text embeddings\n",
+        "By converting text into numerical vectors, text embeddings make it 
possible for computers to process and analyze text data. This enables a wide 
range of NLP tasks, including:\n",
+        "\n",
+        "* Semantic search: Finding documents or passages that are relevant to 
a query, even if the query doesn't use the exact same words as the 
documents.\n",
+        "* Text classification: Categorzing text data into different classes, 
such as spam or not spam, or positive sentiment or negative sentiment.\n",
+        "* Machine translation: Translating text from one language to another 
while preserving the meaning.\n",
+        "* Text summarization: Creating shorter summaries of longer pieces of 
text.\n",
+        "\n",
+        "In this notebook, we will use Apache Beam's `MLTransform` to 
embeddings on the text data.\n",
+        "\n",
+        "Hugging Face's 
[`SentenceTransformers`](https://huggingface.co/sentence-transformers) 
framework uses Python to generate sentence, text, and image embeddings.\n",
+        "\n",
+        "To generate text embeddings that use Hugging Face models and 
`MLTransform`, use `SentenceTransformerEmbeddings` to specify the model 
configuration.\n",
+        "\n",
+        "To use `SentenceTransformerEmbeddings`, first install the `the 
sentence-transformers` package."
+      ],
+      "metadata": {
+        "id": "yvVIEhF01ZWq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Install dependencies\n",
+        " Install Apache Beam and the dependencies needed to work with Hugging 
Face embeddings."
+      ],
+      "metadata": {
+        "id": "jqYXaBJ821Zs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "! git clone https://github.com/apache/beam.git\n";,
+        "! cd beam/sdks/python\n",
+        "! pip install beam/sdks/python\n",
+        "! pip install sentence-transformers"
+      ],
+      "metadata": {
+        "id": "shzCUrZI1XhF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tempfile\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.ml.transforms.base import MLTransform\n",
+        "from apache_beam.ml.transforms.embeddings.huggingface import 
SentenceTransformerEmbeddings"
+      ],
+      "metadata": {
+        "id": "jVxSi2jS3M3b"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Use MLTransform in write mode\n",
+        "\n",
+        "In `write` mode, `MLTransform` saves the transforms and their 
attributes to an artifact location. These transforms are used when you run 
`MLTransform` in `read` mode.\n",

Review Comment:
   This section might benefit from a brief introduction of MLTransform (e.g. 
"MLTransform is a transform that can be used for a variety of machine learning 
pre- and post-processing operations and data preparation, including generating 
embeddings.")



##########
examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb:
##########
@@ -0,0 +1,456 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "UmEFwsNs1OES"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate Text Embeddings by using Hugging Face Hub models\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\";
 />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\";
 />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ],
+      "metadata": {
+        "id": "ZUSiAR62SgO8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "## Text Embeddings\n",
+        "\n",
+        "Text embeddings are a way of representing text as numerical vectors. 
This allows computers to understand and process text data, which is essential 
for many natural language processing (NLP) tasks.\n",
+        "\n",
+        "### Uses of text embeddings\n",
+        "By converting text into numerical vectors, text embeddings make it 
possible for computers to process and analyze text data. This enables a wide 
range of NLP tasks, including:\n",
+        "\n",
+        "* Semantic search: Finding documents or passages that are relevant to 
a query, even if the query doesn't use the exact same words as the 
documents.\n",
+        "* Text classification: Categorzing text data into different classes, 
such as spam or not spam, or positive sentiment or negative sentiment.\n",
+        "* Machine translation: Translating text from one language to another 
while preserving the meaning.\n",
+        "* Text summarization: Creating shorter summaries of longer pieces of 
text.\n",
+        "\n",
+        "In this notebook, we will use Apache Beam's `MLTransform` to 
embeddings on the text data.\n",
+        "\n",
+        "Hugging Face's 
[`SentenceTransformers`](https://huggingface.co/sentence-transformers) 
framework uses Python to generate sentence, text, and image embeddings.\n",
+        "\n",
+        "To generate text embeddings that use Hugging Face models and 
`MLTransform`, use `SentenceTransformerEmbeddings` to specify the model 
configuration.\n",
+        "\n",
+        "To use `SentenceTransformerEmbeddings`, first install the `the 
sentence-transformers` package."
+      ],
+      "metadata": {
+        "id": "yvVIEhF01ZWq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Install dependencies\n",
+        " Install Apache Beam and the dependencies needed to work with Hugging 
Face embeddings."

Review Comment:
   Nit: remove leading space



##########
examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb:
##########
@@ -0,0 +1,456 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "UmEFwsNs1OES"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate Text Embeddings by using Hugging Face Hub models\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\";
 />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\";
 />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ],
+      "metadata": {
+        "id": "ZUSiAR62SgO8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "## Text Embeddings\n",
+        "\n",
+        "Text embeddings are a way of representing text as numerical vectors. 
This allows computers to understand and process text data, which is essential 
for many natural language processing (NLP) tasks.\n",
+        "\n",
+        "### Uses of text embeddings\n",
+        "By converting text into numerical vectors, text embeddings make it 
possible for computers to process and analyze text data. This enables a wide 
range of NLP tasks, including:\n",
+        "\n",
+        "* Semantic search: Finding documents or passages that are relevant to 
a query, even if the query doesn't use the exact same words as the 
documents.\n",
+        "* Text classification: Categorzing text data into different classes, 
such as spam or not spam, or positive sentiment or negative sentiment.\n",
+        "* Machine translation: Translating text from one language to another 
while preserving the meaning.\n",
+        "* Text summarization: Creating shorter summaries of longer pieces of 
text.\n",
+        "\n",
+        "In this notebook, we will use Apache Beam's `MLTransform` to 
embeddings on the text data.\n",
+        "\n",
+        "Hugging Face's 
[`SentenceTransformers`](https://huggingface.co/sentence-transformers) 
framework uses Python to generate sentence, text, and image embeddings.\n",
+        "\n",
+        "To generate text embeddings that use Hugging Face models and 
`MLTransform`, use `SentenceTransformerEmbeddings` to specify the model 
configuration.\n",
+        "\n",
+        "To use `SentenceTransformerEmbeddings`, first install the `the 
sentence-transformers` package."
+      ],
+      "metadata": {
+        "id": "yvVIEhF01ZWq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Install dependencies\n",
+        " Install Apache Beam and the dependencies needed to work with Hugging 
Face embeddings."
+      ],
+      "metadata": {
+        "id": "jqYXaBJ821Zs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "! git clone https://github.com/apache/beam.git\n";,
+        "! cd beam/sdks/python\n",
+        "! pip install beam/sdks/python\n",
+        "! pip install sentence-transformers"
+      ],
+      "metadata": {
+        "id": "shzCUrZI1XhF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tempfile\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.ml.transforms.base import MLTransform\n",
+        "from apache_beam.ml.transforms.embeddings.huggingface import 
SentenceTransformerEmbeddings"
+      ],
+      "metadata": {
+        "id": "jVxSi2jS3M3b"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Use MLTransform in write mode\n",
+        "\n",
+        "In `write` mode, `MLTransform` saves the transforms and their 
attributes to an artifact location. These transforms are used when you run 
`MLTransform` in `read` mode.\n",

Review Comment:
   and maybe after the `read` mode sentence we could add a small snippet about 
why read/write mode is useful (maybe just copy the first bullet in 
https://beam.apache.org/documentation/ml/preprocess-data/#use-mltransform)



##########
examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb:
##########
@@ -0,0 +1,456 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "UmEFwsNs1OES"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate Text Embeddings by using Hugging Face Hub models\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\";
 />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\";
 />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ],
+      "metadata": {
+        "id": "ZUSiAR62SgO8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "## Text Embeddings\n",
+        "\n",
+        "Text embeddings are a way of representing text as numerical vectors. 
This allows computers to understand and process text data, which is essential 
for many natural language processing (NLP) tasks.\n",
+        "\n",
+        "### Uses of text embeddings\n",
+        "By converting text into numerical vectors, text embeddings make it 
possible for computers to process and analyze text data. This enables a wide 
range of NLP tasks, including:\n",
+        "\n",
+        "* Semantic search: Finding documents or passages that are relevant to 
a query, even if the query doesn't use the exact same words as the 
documents.\n",
+        "* Text classification: Categorzing text data into different classes, 
such as spam or not spam, or positive sentiment or negative sentiment.\n",
+        "* Machine translation: Translating text from one language to another 
while preserving the meaning.\n",
+        "* Text summarization: Creating shorter summaries of longer pieces of 
text.\n",
+        "\n",
+        "In this notebook, we will use Apache Beam's `MLTransform` to 
embeddings on the text data.\n",
+        "\n",
+        "Hugging Face's 
[`SentenceTransformers`](https://huggingface.co/sentence-transformers) 
framework uses Python to generate sentence, text, and image embeddings.\n",
+        "\n",
+        "To generate text embeddings that use Hugging Face models and 
`MLTransform`, use `SentenceTransformerEmbeddings` to specify the model 
configuration.\n",
+        "\n",
+        "To use `SentenceTransformerEmbeddings`, first install the `the 
sentence-transformers` package."
+      ],
+      "metadata": {
+        "id": "yvVIEhF01ZWq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Install dependencies\n",
+        " Install Apache Beam and the dependencies needed to work with Hugging 
Face embeddings."
+      ],
+      "metadata": {
+        "id": "jqYXaBJ821Zs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "! git clone https://github.com/apache/beam.git\n";,
+        "! cd beam/sdks/python\n",
+        "! pip install beam/sdks/python\n",
+        "! pip install sentence-transformers"
+      ],
+      "metadata": {
+        "id": "shzCUrZI1XhF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tempfile\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.ml.transforms.base import MLTransform\n",
+        "from apache_beam.ml.transforms.embeddings.huggingface import 
SentenceTransformerEmbeddings"
+      ],
+      "metadata": {
+        "id": "jVxSi2jS3M3b"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Use MLTransform in write mode\n",
+        "\n",
+        "In `write` mode, `MLTransform` saves the transforms and their 
attributes to an artifact location. These transforms are used when you run 
`MLTransform` in `read` mode.\n",
+        "\n",
+        "For more information about using `MLTransform`, see [Preprocess data 
with MLTransform](https://beam.apache.org/documentation/ml/preprocess-data/) in 
the Apache Beam documentation."
+      ],
+      "metadata": {
+        "id": "kXDM8C7d3nPV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To generate text embeddings with `MLTransform`, the following 
pipeline uses the model `sentence-transformers/all-MiniLM-L6-v2` and the text 
inputs from the Hugging Face blog [Getting Started With 
Embeddings](https://huggingface.co/blog/getting-started-with-embeddings)."
+      ],
+      "metadata": {
+        "id": "Dbkmu3HP6Kql"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "content = [\n",
+        "    {'x': 'How do I get a replacement Medicare card?'},\n",
+        "    {'x': 'What is the monthly premium for Medicare Part B?'},\n",
+        "    {'x': 'How do I terminate my Medicare Part B (medical 
insurance)?'},\n",
+        "    {'x': 'How do I sign up for Medicare?'},\n",
+        "    {'x': 'Can I sign up for Medicare Part B if I am working and have 
health insurance through an employer?'},\n",
+        "    {'x': 'How do I sign up for Medicare Part B if I already have 
Part A?'},\n",
+        "    {'x': 'What are Medicare late enrollment penalties?'},\n",
+        "    {'x': 'What is Medicare and who can get it?'},\n",
+        "    {'x': 'How can I get help with my Medicare Part A and Part B 
premiums?'},\n",
+        "    {'x': 'What are the different parts of Medicare?'},\n",
+        "    {'x': 'Will my Medicare premiums be higher because of my higher 
income?'},\n",
+        "    {'x': 'What is TRICARE ?'},\n",
+        "    {'x': \"Should I sign up for Medicare Part B if I have Veterans' 
Benefits?\"}\n",
+        "]\n",
+        "\n",
+        "\n",
+        "# helper function that returns a dict containing only first\n",
+        "#10 elements of generated embeddings.\n",
+        "def truncate_embeddings(d):\n",
+        "  for key in d.keys():\n",
+        "    d[key] = d[key][:10]\n",
+        "  return d"
+      ],
+      "metadata": {
+        "id": "LCTUs8F73iDg"
+      },
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "artifact_location_minilm = tempfile.mkdtemp(prefix='huggingface_')\n",
+        "text_embedding_model_name = 
'sentence-transformers/all-MiniLM-L6-v2'\n",
+        "embedding_transform = SentenceTransformerEmbeddings(\n",
+        "        model_name=text_embedding_model_name, columns=['x'])\n",
+        "\n",
+        "with beam.Pipeline() as pipeline:\n",
+        "  data_pcoll = (\n",
+        "          pipeline\n",
+        "          | \"CreateData\" >> beam.Create(content))\n",
+        "  transformed_pcoll = (\n",
+        "      data_pcoll\n",
+        "      | \"MLTransform\" >> 
MLTransform(write_artifact_location=artifact_location_minilm).with_transform(embedding_transform))\n",
+        "\n",
+        "  transformed_pcoll | beam.Map(truncate_embeddings) | 'LogOutput' >> 
beam.Map(print)\n",
+        "\n",
+        "  transformed_pcoll | \"PrintEmbeddingShape\" >> beam.Map(lambda x: 
print(f\"Embedding shape: {len(x['x'])}\"))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/";
+        },
+        "id": "SF6izkN134sf",
+        "outputId": "740f450a-dc9c-4c9d-f4fb-8ef27cca3d74"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'x': [-0.023889463394880295, 0.05525851249694824, 
-0.011654896661639214, -0.03341428190469742, -0.012260555289685726, 
-0.024872763082385063, -0.01266342680901289, 0.025345895439386368, 
0.01850851997733116, -0.08350814878940582]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.01268761046230793, 0.04687413573265076, 
-0.010502150282263756, -0.020383981987833977, -0.01336114201694727, 
0.04232167452573776, 0.016627851873636246, -0.004099288955330849, 
-0.0026070312596857548, -0.010187783278524876]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [0.0004943296662531793, 0.11941202729940414, 
0.005229473114013672, -0.09273427724838257, 0.007772865705192089, 
-0.005324989557266235, 0.03450643643736839, -0.05198145657777786, 
-0.006264965515583754, -0.006110507529228926]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.029711326584219933, 0.02329839952290058, 
-0.05704096704721451, -0.01218305341899395, -0.013710316270589828, 
0.02979600988328457, 0.0637386366724968, 0.0011010386515408754, 
-0.04512352868914604, -0.040747467428445816]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.02562842145562172, 0.070388562977314, 
-0.017379559576511383, -0.0565667562186718, 0.02857644483447075, 
0.052822552621364594, 0.06706249713897705, -0.05261750519275665, 
-0.054702047258615494, -0.11623040586709976]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.022656124085187912, 0.021159743890166283, 
0.0051048519089818, -0.04649421200156212, 0.009073587134480476, 
0.04149482399225235, 0.0542682446539402, -0.02418488636612892, 
-0.013482789508998394, -0.07596635073423386]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.0029113641940057278, 0.060791268944740295, 
-0.009175681509077549, -0.006133317016065121, 0.04049248993396759, 
0.036593958735466, 0.002054463606327772, -0.03134453296661377, 
0.03180575743317604, -0.02349487692117691]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.08052562177181244, 0.05988812819123268, 
-0.048846807330846786, -0.040176115930080414, -0.06334187835454941, 
0.04184781387448311, 0.11904510855674744, 0.010651882737874985, 
-0.030094878748059273, -0.004561211448162794]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.0343877375125885, 0.07250142097473145, 
0.01443990133702755, -0.03669498860836029, 0.014018685556948185, 
0.06307007372379303, 0.03468254581093788, -0.014530746266245842, 
-0.05986189469695091, -0.04538322612643242]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.005963834468275309, 0.025043703615665436, 
-0.003182061715051532, -0.025242920964956284, -0.0398230254650116, 
-0.012771873734891415, 0.0447133406996727, 0.014535333029925823, 
-0.03821341320872307, -0.04114910215139389]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.039007965475320816, -0.010609461925923824, 
-0.007382705342024565, -0.050189778208732605, -0.0025175788905471563, 
-0.0416409894824028, 0.02696940489113331, -0.014800631441175938, 
-0.014126974157989025, -0.061636749655008316]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.09598278254270554, -0.06301165372133255, 
-0.11690578609704971, -0.05907457321882248, -0.05132286250591278, 
-0.0034391973167657852, 0.018687350675463676, 0.006543711293488741, 
-0.04905705526471138, -0.031649429351091385]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.011600406840443611, 0.05651004612445831, 
0.016623979434370995, -0.09469003975391388, -0.009865491650998592, 
0.07234735041856766, 0.04412448778748512, -0.0411749929189682, 
-0.04212445020675659, -0.10263106226921082]}\n",
+            "Embedding shape: 10\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Pass additional arguments that are supported by 
`sentence-transformer` models, such as `convert_to_numpy=False`. These 
arguments are passed as a `dict` to the `SentenceTransformerEmbeddings` 
transform by using the `inference_args` parameter.\n",
+        "\n",
+        "By passing `convert_to_numpy=False`, the output will contain 
`torch.Tensor`s."
+      ],
+      "metadata": {
+        "id": "1MFom0PW_vRv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "artifact_location_minilm_with_inference_args = 
tempfile.mkdtemp(prefix='huggingface_')\n",
+        "\n",
+        "embedding_transform = SentenceTransformerEmbeddings(\n",
+        "        model_name=text_embedding_model_name, columns=['x'],\n",
+        "        inference_args={'convert_to_numpy': False}\n",
+        "        )\n",
+        "\n",
+        "with beam.Pipeline() as pipeline:\n",
+        "  data_pcoll = (\n",
+        "          pipeline\n",
+        "          | \"CreateData\" >> beam.Create(content))\n",
+        "  transformed_pcoll = (\n",
+        "      data_pcoll\n",
+        "      | \"MLTransform\" >> 
MLTransform(write_artifact_location=artifact_location_minilm_with_inference_args).with_transform(embedding_transform))\n",
+        "\n",
+        "  # The outputs are in the Pytorch tensor type.\n",
+        "  transformed_pcoll | 'LogOutput' >> beam.Map(lambda x: 
print(type(x['x'])))\n",
+        "\n",
+        "  transformed_pcoll | \"PrintEmbeddingShape\" >> beam.Map(lambda x: 
print(f\"Embedding shape: {len(x['x'])}\"))\n"
+      ],
+      "metadata": {
+        "id": "xyezKuzY_uLD",
+        "colab": {
+          "base_uri": "https://localhost:8080/";
+        },
+        "outputId": "d09a07d5-55dc-4544-ea75-39b8105a3e5b"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Embedding shape: 384\n",
+            "<class 'torch.Tensor'>\n",
+            "Embedding shape: 384\n",
+            "<class 'torch.Tensor'>\n",
+            "Embedding shape: 384\n",
+            "<class 'torch.Tensor'>\n",
+            "Embedding shape: 384\n",
+            "<class 'torch.Tensor'>\n",
+            "Embedding shape: 384\n",
+            "<class 'torch.Tensor'>\n",
+            "Embedding shape: 384\n",
+            "<class 'torch.Tensor'>\n",
+            "Embedding shape: 384\n",
+            "<class 'torch.Tensor'>\n",
+            "Embedding shape: 384\n",
+            "<class 'torch.Tensor'>\n",
+            "Embedding shape: 384\n",
+            "<class 'torch.Tensor'>\n",
+            "Embedding shape: 384\n",
+            "<class 'torch.Tensor'>\n",
+            "Embedding shape: 384\n",
+            "<class 'torch.Tensor'>\n",
+            "Embedding shape: 384\n",
+            "<class 'torch.Tensor'>\n",
+            "Embedding shape: 384\n",
+            "<class 'torch.Tensor'>\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Use the model `sentence-transformers/sentence-t5-large` to generate 
text embeddings. The model uses only the encoder from a `T5-large model`. The 
weights are stored in FP16. For more information about the model, see 
[Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text 
Models](https://arxiv.org/abs/2108.08877)."

Review Comment:
   ```suggestion
           "Next, we will use the model 
`sentence-transformers/sentence-t5-large` to generate text embeddings. The 
model uses only the encoder from a `T5-large model`. The weights are stored in 
FP16. For more information about the model, see [Sentence-T5: Scalable Sentence 
Encoders from Pre-trained Text-to-Text 
Models](https://arxiv.org/abs/2108.08877)."
   ```
   
   Is this example showing something meaningfully different from the one above 
it? If not, I'd cut it. If yes, then I'd emphasize the difference in this 
paragraph.



##########
examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb:
##########
@@ -0,0 +1,456 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "UmEFwsNs1OES"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate Text Embeddings by using Hugging Face Hub models\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\";
 />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\";
 />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ],
+      "metadata": {
+        "id": "ZUSiAR62SgO8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "## Text Embeddings\n",
+        "\n",
+        "Text embeddings are a way of representing text as numerical vectors. 
This allows computers to understand and process text data, which is essential 
for many natural language processing (NLP) tasks.\n",
+        "\n",
+        "### Uses of text embeddings\n",
+        "By converting text into numerical vectors, text embeddings make it 
possible for computers to process and analyze text data. This enables a wide 
range of NLP tasks, including:\n",
+        "\n",
+        "* Semantic search: Finding documents or passages that are relevant to 
a query, even if the query doesn't use the exact same words as the 
documents.\n",
+        "* Text classification: Categorzing text data into different classes, 
such as spam or not spam, or positive sentiment or negative sentiment.\n",
+        "* Machine translation: Translating text from one language to another 
while preserving the meaning.\n",
+        "* Text summarization: Creating shorter summaries of longer pieces of 
text.\n",
+        "\n",
+        "In this notebook, we will use Apache Beam's `MLTransform` to 
embeddings on the text data.\n",
+        "\n",
+        "Hugging Face's 
[`SentenceTransformers`](https://huggingface.co/sentence-transformers) 
framework uses Python to generate sentence, text, and image embeddings.\n",
+        "\n",
+        "To generate text embeddings that use Hugging Face models and 
`MLTransform`, use `SentenceTransformerEmbeddings` to specify the model 
configuration.\n",
+        "\n",
+        "To use `SentenceTransformerEmbeddings`, first install the `the 
sentence-transformers` package."
+      ],
+      "metadata": {
+        "id": "yvVIEhF01ZWq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Install dependencies\n",
+        " Install Apache Beam and the dependencies needed to work with Hugging 
Face embeddings."
+      ],
+      "metadata": {
+        "id": "jqYXaBJ821Zs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "! git clone https://github.com/apache/beam.git\n";,
+        "! cd beam/sdks/python\n",
+        "! pip install beam/sdks/python\n",
+        "! pip install sentence-transformers"
+      ],
+      "metadata": {
+        "id": "shzCUrZI1XhF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tempfile\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.ml.transforms.base import MLTransform\n",
+        "from apache_beam.ml.transforms.embeddings.huggingface import 
SentenceTransformerEmbeddings"
+      ],
+      "metadata": {
+        "id": "jVxSi2jS3M3b"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Use MLTransform in write mode\n",
+        "\n",
+        "In `write` mode, `MLTransform` saves the transforms and their 
attributes to an artifact location. These transforms are used when you run 
`MLTransform` in `read` mode.\n",
+        "\n",
+        "For more information about using `MLTransform`, see [Preprocess data 
with MLTransform](https://beam.apache.org/documentation/ml/preprocess-data/) in 
the Apache Beam documentation."
+      ],
+      "metadata": {
+        "id": "kXDM8C7d3nPV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To generate text embeddings with `MLTransform`, the following 
pipeline uses the model `sentence-transformers/all-MiniLM-L6-v2` and the text 
inputs from the Hugging Face blog [Getting Started With 
Embeddings](https://huggingface.co/blog/getting-started-with-embeddings)."
+      ],
+      "metadata": {
+        "id": "Dbkmu3HP6Kql"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "content = [\n",
+        "    {'x': 'How do I get a replacement Medicare card?'},\n",
+        "    {'x': 'What is the monthly premium for Medicare Part B?'},\n",
+        "    {'x': 'How do I terminate my Medicare Part B (medical 
insurance)?'},\n",
+        "    {'x': 'How do I sign up for Medicare?'},\n",
+        "    {'x': 'Can I sign up for Medicare Part B if I am working and have 
health insurance through an employer?'},\n",
+        "    {'x': 'How do I sign up for Medicare Part B if I already have 
Part A?'},\n",
+        "    {'x': 'What are Medicare late enrollment penalties?'},\n",
+        "    {'x': 'What is Medicare and who can get it?'},\n",
+        "    {'x': 'How can I get help with my Medicare Part A and Part B 
premiums?'},\n",
+        "    {'x': 'What are the different parts of Medicare?'},\n",
+        "    {'x': 'Will my Medicare premiums be higher because of my higher 
income?'},\n",
+        "    {'x': 'What is TRICARE ?'},\n",
+        "    {'x': \"Should I sign up for Medicare Part B if I have Veterans' 
Benefits?\"}\n",
+        "]\n",
+        "\n",
+        "\n",
+        "# helper function that returns a dict containing only first\n",
+        "#10 elements of generated embeddings.\n",
+        "def truncate_embeddings(d):\n",
+        "  for key in d.keys():\n",
+        "    d[key] = d[key][:10]\n",
+        "  return d"
+      ],
+      "metadata": {
+        "id": "LCTUs8F73iDg"
+      },
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "artifact_location_minilm = tempfile.mkdtemp(prefix='huggingface_')\n",
+        "text_embedding_model_name = 
'sentence-transformers/all-MiniLM-L6-v2'\n",
+        "embedding_transform = SentenceTransformerEmbeddings(\n",
+        "        model_name=text_embedding_model_name, columns=['x'])\n",
+        "\n",
+        "with beam.Pipeline() as pipeline:\n",
+        "  data_pcoll = (\n",
+        "          pipeline\n",
+        "          | \"CreateData\" >> beam.Create(content))\n",
+        "  transformed_pcoll = (\n",
+        "      data_pcoll\n",
+        "      | \"MLTransform\" >> 
MLTransform(write_artifact_location=artifact_location_minilm).with_transform(embedding_transform))\n",
+        "\n",
+        "  transformed_pcoll | beam.Map(truncate_embeddings) | 'LogOutput' >> 
beam.Map(print)\n",
+        "\n",
+        "  transformed_pcoll | \"PrintEmbeddingShape\" >> beam.Map(lambda x: 
print(f\"Embedding shape: {len(x['x'])}\"))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/";
+        },
+        "id": "SF6izkN134sf",
+        "outputId": "740f450a-dc9c-4c9d-f4fb-8ef27cca3d74"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'x': [-0.023889463394880295, 0.05525851249694824, 
-0.011654896661639214, -0.03341428190469742, -0.012260555289685726, 
-0.024872763082385063, -0.01266342680901289, 0.025345895439386368, 
0.01850851997733116, -0.08350814878940582]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.01268761046230793, 0.04687413573265076, 
-0.010502150282263756, -0.020383981987833977, -0.01336114201694727, 
0.04232167452573776, 0.016627851873636246, -0.004099288955330849, 
-0.0026070312596857548, -0.010187783278524876]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [0.0004943296662531793, 0.11941202729940414, 
0.005229473114013672, -0.09273427724838257, 0.007772865705192089, 
-0.005324989557266235, 0.03450643643736839, -0.05198145657777786, 
-0.006264965515583754, -0.006110507529228926]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.029711326584219933, 0.02329839952290058, 
-0.05704096704721451, -0.01218305341899395, -0.013710316270589828, 
0.02979600988328457, 0.0637386366724968, 0.0011010386515408754, 
-0.04512352868914604, -0.040747467428445816]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.02562842145562172, 0.070388562977314, 
-0.017379559576511383, -0.0565667562186718, 0.02857644483447075, 
0.052822552621364594, 0.06706249713897705, -0.05261750519275665, 
-0.054702047258615494, -0.11623040586709976]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.022656124085187912, 0.021159743890166283, 
0.0051048519089818, -0.04649421200156212, 0.009073587134480476, 
0.04149482399225235, 0.0542682446539402, -0.02418488636612892, 
-0.013482789508998394, -0.07596635073423386]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.0029113641940057278, 0.060791268944740295, 
-0.009175681509077549, -0.006133317016065121, 0.04049248993396759, 
0.036593958735466, 0.002054463606327772, -0.03134453296661377, 
0.03180575743317604, -0.02349487692117691]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.08052562177181244, 0.05988812819123268, 
-0.048846807330846786, -0.040176115930080414, -0.06334187835454941, 
0.04184781387448311, 0.11904510855674744, 0.010651882737874985, 
-0.030094878748059273, -0.004561211448162794]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.0343877375125885, 0.07250142097473145, 
0.01443990133702755, -0.03669498860836029, 0.014018685556948185, 
0.06307007372379303, 0.03468254581093788, -0.014530746266245842, 
-0.05986189469695091, -0.04538322612643242]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.005963834468275309, 0.025043703615665436, 
-0.003182061715051532, -0.025242920964956284, -0.0398230254650116, 
-0.012771873734891415, 0.0447133406996727, 0.014535333029925823, 
-0.03821341320872307, -0.04114910215139389]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.039007965475320816, -0.010609461925923824, 
-0.007382705342024565, -0.050189778208732605, -0.0025175788905471563, 
-0.0416409894824028, 0.02696940489113331, -0.014800631441175938, 
-0.014126974157989025, -0.061636749655008316]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.09598278254270554, -0.06301165372133255, 
-0.11690578609704971, -0.05907457321882248, -0.05132286250591278, 
-0.0034391973167657852, 0.018687350675463676, 0.006543711293488741, 
-0.04905705526471138, -0.031649429351091385]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.011600406840443611, 0.05651004612445831, 
0.016623979434370995, -0.09469003975391388, -0.009865491650998592, 
0.07234735041856766, 0.04412448778748512, -0.0411749929189682, 
-0.04212445020675659, -0.10263106226921082]}\n",
+            "Embedding shape: 10\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Pass additional arguments that are supported by 
`sentence-transformer` models, such as `convert_to_numpy=False`. These 
arguments are passed as a `dict` to the `SentenceTransformerEmbeddings` 
transform by using the `inference_args` parameter.\n",
+        "\n",
+        "By passing `convert_to_numpy=False`, the output will contain 
`torch.Tensor`s."
+      ],
+      "metadata": {
+        "id": "1MFom0PW_vRv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "artifact_location_minilm_with_inference_args = 
tempfile.mkdtemp(prefix='huggingface_')\n",
+        "\n",
+        "embedding_transform = SentenceTransformerEmbeddings(\n",
+        "        model_name=text_embedding_model_name, columns=['x'],\n",
+        "        inference_args={'convert_to_numpy': False}\n",
+        "        )\n",
+        "\n",
+        "with beam.Pipeline() as pipeline:\n",
+        "  data_pcoll = (\n",
+        "          pipeline\n",
+        "          | \"CreateData\" >> beam.Create(content))\n",
+        "  transformed_pcoll = (\n",
+        "      data_pcoll\n",
+        "      | \"MLTransform\" >> 
MLTransform(write_artifact_location=artifact_location_minilm_with_inference_args).with_transform(embedding_transform))\n",

Review Comment:
   Same comment applies elsewhere



##########
examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb:
##########
@@ -0,0 +1,456 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "UmEFwsNs1OES"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate Text Embeddings by using Hugging Face Hub models\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\";
 />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\";
 />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ],
+      "metadata": {
+        "id": "ZUSiAR62SgO8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "## Text Embeddings\n",
+        "\n",
+        "Text embeddings are a way of representing text as numerical vectors. 
This allows computers to understand and process text data, which is essential 
for many natural language processing (NLP) tasks.\n",
+        "\n",
+        "### Uses of text embeddings\n",
+        "By converting text into numerical vectors, text embeddings make it 
possible for computers to process and analyze text data. This enables a wide 
range of NLP tasks, including:\n",
+        "\n",
+        "* Semantic search: Finding documents or passages that are relevant to 
a query, even if the query doesn't use the exact same words as the 
documents.\n",
+        "* Text classification: Categorzing text data into different classes, 
such as spam or not spam, or positive sentiment or negative sentiment.\n",
+        "* Machine translation: Translating text from one language to another 
while preserving the meaning.\n",
+        "* Text summarization: Creating shorter summaries of longer pieces of 
text.\n",
+        "\n",
+        "In this notebook, we will use Apache Beam's `MLTransform` to 
embeddings on the text data.\n",
+        "\n",
+        "Hugging Face's 
[`SentenceTransformers`](https://huggingface.co/sentence-transformers) 
framework uses Python to generate sentence, text, and image embeddings.\n",
+        "\n",
+        "To generate text embeddings that use Hugging Face models and 
`MLTransform`, use `SentenceTransformerEmbeddings` to specify the model 
configuration.\n",
+        "\n",
+        "To use `SentenceTransformerEmbeddings`, first install the `the 
sentence-transformers` package."
+      ],
+      "metadata": {
+        "id": "yvVIEhF01ZWq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Install dependencies\n",
+        " Install Apache Beam and the dependencies needed to work with Hugging 
Face embeddings."
+      ],
+      "metadata": {
+        "id": "jqYXaBJ821Zs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "! git clone https://github.com/apache/beam.git\n";,
+        "! cd beam/sdks/python\n",
+        "! pip install beam/sdks/python\n",
+        "! pip install sentence-transformers"
+      ],
+      "metadata": {
+        "id": "shzCUrZI1XhF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tempfile\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.ml.transforms.base import MLTransform\n",
+        "from apache_beam.ml.transforms.embeddings.huggingface import 
SentenceTransformerEmbeddings"
+      ],
+      "metadata": {
+        "id": "jVxSi2jS3M3b"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Use MLTransform in write mode\n",
+        "\n",
+        "In `write` mode, `MLTransform` saves the transforms and their 
attributes to an artifact location. These transforms are used when you run 
`MLTransform` in `read` mode.\n",
+        "\n",
+        "For more information about using `MLTransform`, see [Preprocess data 
with MLTransform](https://beam.apache.org/documentation/ml/preprocess-data/) in 
the Apache Beam documentation."
+      ],
+      "metadata": {
+        "id": "kXDM8C7d3nPV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To generate text embeddings with `MLTransform`, the following 
pipeline uses the model `sentence-transformers/all-MiniLM-L6-v2` and the text 
inputs from the Hugging Face blog [Getting Started With 
Embeddings](https://huggingface.co/blog/getting-started-with-embeddings)."
+      ],
+      "metadata": {
+        "id": "Dbkmu3HP6Kql"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "content = [\n",
+        "    {'x': 'How do I get a replacement Medicare card?'},\n",
+        "    {'x': 'What is the monthly premium for Medicare Part B?'},\n",
+        "    {'x': 'How do I terminate my Medicare Part B (medical 
insurance)?'},\n",
+        "    {'x': 'How do I sign up for Medicare?'},\n",
+        "    {'x': 'Can I sign up for Medicare Part B if I am working and have 
health insurance through an employer?'},\n",
+        "    {'x': 'How do I sign up for Medicare Part B if I already have 
Part A?'},\n",
+        "    {'x': 'What are Medicare late enrollment penalties?'},\n",
+        "    {'x': 'What is Medicare and who can get it?'},\n",
+        "    {'x': 'How can I get help with my Medicare Part A and Part B 
premiums?'},\n",
+        "    {'x': 'What are the different parts of Medicare?'},\n",
+        "    {'x': 'Will my Medicare premiums be higher because of my higher 
income?'},\n",
+        "    {'x': 'What is TRICARE ?'},\n",
+        "    {'x': \"Should I sign up for Medicare Part B if I have Veterans' 
Benefits?\"}\n",
+        "]\n",
+        "\n",
+        "\n",
+        "# helper function that returns a dict containing only first\n",
+        "#10 elements of generated embeddings.\n",
+        "def truncate_embeddings(d):\n",
+        "  for key in d.keys():\n",
+        "    d[key] = d[key][:10]\n",
+        "  return d"
+      ],
+      "metadata": {
+        "id": "LCTUs8F73iDg"
+      },
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "artifact_location_minilm = tempfile.mkdtemp(prefix='huggingface_')\n",
+        "text_embedding_model_name = 
'sentence-transformers/all-MiniLM-L6-v2'\n",
+        "embedding_transform = SentenceTransformerEmbeddings(\n",
+        "        model_name=text_embedding_model_name, columns=['x'])\n",
+        "\n",
+        "with beam.Pipeline() as pipeline:\n",
+        "  data_pcoll = (\n",
+        "          pipeline\n",
+        "          | \"CreateData\" >> beam.Create(content))\n",
+        "  transformed_pcoll = (\n",
+        "      data_pcoll\n",
+        "      | \"MLTransform\" >> 
MLTransform(write_artifact_location=artifact_location_minilm).with_transform(embedding_transform))\n",
+        "\n",
+        "  transformed_pcoll | beam.Map(truncate_embeddings) | 'LogOutput' >> 
beam.Map(print)\n",
+        "\n",
+        "  transformed_pcoll | \"PrintEmbeddingShape\" >> beam.Map(lambda x: 
print(f\"Embedding shape: {len(x['x'])}\"))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/";
+        },
+        "id": "SF6izkN134sf",
+        "outputId": "740f450a-dc9c-4c9d-f4fb-8ef27cca3d74"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'x': [-0.023889463394880295, 0.05525851249694824, 
-0.011654896661639214, -0.03341428190469742, -0.012260555289685726, 
-0.024872763082385063, -0.01266342680901289, 0.025345895439386368, 
0.01850851997733116, -0.08350814878940582]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.01268761046230793, 0.04687413573265076, 
-0.010502150282263756, -0.020383981987833977, -0.01336114201694727, 
0.04232167452573776, 0.016627851873636246, -0.004099288955330849, 
-0.0026070312596857548, -0.010187783278524876]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [0.0004943296662531793, 0.11941202729940414, 
0.005229473114013672, -0.09273427724838257, 0.007772865705192089, 
-0.005324989557266235, 0.03450643643736839, -0.05198145657777786, 
-0.006264965515583754, -0.006110507529228926]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.029711326584219933, 0.02329839952290058, 
-0.05704096704721451, -0.01218305341899395, -0.013710316270589828, 
0.02979600988328457, 0.0637386366724968, 0.0011010386515408754, 
-0.04512352868914604, -0.040747467428445816]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.02562842145562172, 0.070388562977314, 
-0.017379559576511383, -0.0565667562186718, 0.02857644483447075, 
0.052822552621364594, 0.06706249713897705, -0.05261750519275665, 
-0.054702047258615494, -0.11623040586709976]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.022656124085187912, 0.021159743890166283, 
0.0051048519089818, -0.04649421200156212, 0.009073587134480476, 
0.04149482399225235, 0.0542682446539402, -0.02418488636612892, 
-0.013482789508998394, -0.07596635073423386]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.0029113641940057278, 0.060791268944740295, 
-0.009175681509077549, -0.006133317016065121, 0.04049248993396759, 
0.036593958735466, 0.002054463606327772, -0.03134453296661377, 
0.03180575743317604, -0.02349487692117691]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.08052562177181244, 0.05988812819123268, 
-0.048846807330846786, -0.040176115930080414, -0.06334187835454941, 
0.04184781387448311, 0.11904510855674744, 0.010651882737874985, 
-0.030094878748059273, -0.004561211448162794]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.0343877375125885, 0.07250142097473145, 
0.01443990133702755, -0.03669498860836029, 0.014018685556948185, 
0.06307007372379303, 0.03468254581093788, -0.014530746266245842, 
-0.05986189469695091, -0.04538322612643242]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.005963834468275309, 0.025043703615665436, 
-0.003182061715051532, -0.025242920964956284, -0.0398230254650116, 
-0.012771873734891415, 0.0447133406996727, 0.014535333029925823, 
-0.03821341320872307, -0.04114910215139389]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.039007965475320816, -0.010609461925923824, 
-0.007382705342024565, -0.050189778208732605, -0.0025175788905471563, 
-0.0416409894824028, 0.02696940489113331, -0.014800631441175938, 
-0.014126974157989025, -0.061636749655008316]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.09598278254270554, -0.06301165372133255, 
-0.11690578609704971, -0.05907457321882248, -0.05132286250591278, 
-0.0034391973167657852, 0.018687350675463676, 0.006543711293488741, 
-0.04905705526471138, -0.031649429351091385]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.011600406840443611, 0.05651004612445831, 
0.016623979434370995, -0.09469003975391388, -0.009865491650998592, 
0.07234735041856766, 0.04412448778748512, -0.0411749929189682, 
-0.04212445020675659, -0.10263106226921082]}\n",
+            "Embedding shape: 10\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Pass additional arguments that are supported by 
`sentence-transformer` models, such as `convert_to_numpy=False`. These 
arguments are passed as a `dict` to the `SentenceTransformerEmbeddings` 
transform by using the `inference_args` parameter.\n",

Review Comment:
   ```suggestion
           "You can also pass additional arguments that are supported by 
`sentence-transformer` models, such as `convert_to_numpy=False`. These 
arguments are passed as a `dict` to the `SentenceTransformerEmbeddings` 
transform by using the `inference_args` parameter.\n",
   ```



##########
examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb:
##########
@@ -0,0 +1,456 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "UmEFwsNs1OES"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Generate Text Embeddings by using Hugging Face Hub models\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://colab.sandbox.google.com/github/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/colab_32px.png\";
 />Run in Google Colab</a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" 
href=\"https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/data_preprocessing/huggingface_text_embeddings.ipynb\";><img
 
src=\"https://raw.githubusercontent.com/google/or-tools/main/tools/github_32px.png\";
 />View source on GitHub</a>\n",
+        "  </td>\n",
+        "</table>\n"
+      ],
+      "metadata": {
+        "id": "ZUSiAR62SgO8"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "\n",
+        "## Text Embeddings\n",
+        "\n",
+        "Text embeddings are a way of representing text as numerical vectors. 
This allows computers to understand and process text data, which is essential 
for many natural language processing (NLP) tasks.\n",
+        "\n",
+        "### Uses of text embeddings\n",
+        "By converting text into numerical vectors, text embeddings make it 
possible for computers to process and analyze text data. This enables a wide 
range of NLP tasks, including:\n",
+        "\n",
+        "* Semantic search: Finding documents or passages that are relevant to 
a query, even if the query doesn't use the exact same words as the 
documents.\n",
+        "* Text classification: Categorzing text data into different classes, 
such as spam or not spam, or positive sentiment or negative sentiment.\n",
+        "* Machine translation: Translating text from one language to another 
while preserving the meaning.\n",
+        "* Text summarization: Creating shorter summaries of longer pieces of 
text.\n",
+        "\n",
+        "In this notebook, we will use Apache Beam's `MLTransform` to 
embeddings on the text data.\n",
+        "\n",
+        "Hugging Face's 
[`SentenceTransformers`](https://huggingface.co/sentence-transformers) 
framework uses Python to generate sentence, text, and image embeddings.\n",
+        "\n",
+        "To generate text embeddings that use Hugging Face models and 
`MLTransform`, use `SentenceTransformerEmbeddings` to specify the model 
configuration.\n",
+        "\n",
+        "To use `SentenceTransformerEmbeddings`, first install the `the 
sentence-transformers` package."
+      ],
+      "metadata": {
+        "id": "yvVIEhF01ZWq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Install dependencies\n",
+        " Install Apache Beam and the dependencies needed to work with Hugging 
Face embeddings."
+      ],
+      "metadata": {
+        "id": "jqYXaBJ821Zs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "! git clone https://github.com/apache/beam.git\n";,
+        "! cd beam/sdks/python\n",
+        "! pip install beam/sdks/python\n",
+        "! pip install sentence-transformers"
+      ],
+      "metadata": {
+        "id": "shzCUrZI1XhF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tempfile\n",
+        "import apache_beam as beam\n",
+        "from apache_beam.ml.transforms.base import MLTransform\n",
+        "from apache_beam.ml.transforms.embeddings.huggingface import 
SentenceTransformerEmbeddings"
+      ],
+      "metadata": {
+        "id": "jVxSi2jS3M3b"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Use MLTransform in write mode\n",
+        "\n",
+        "In `write` mode, `MLTransform` saves the transforms and their 
attributes to an artifact location. These transforms are used when you run 
`MLTransform` in `read` mode.\n",
+        "\n",
+        "For more information about using `MLTransform`, see [Preprocess data 
with MLTransform](https://beam.apache.org/documentation/ml/preprocess-data/) in 
the Apache Beam documentation."
+      ],
+      "metadata": {
+        "id": "kXDM8C7d3nPV"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "To generate text embeddings with `MLTransform`, the following 
pipeline uses the model `sentence-transformers/all-MiniLM-L6-v2` and the text 
inputs from the Hugging Face blog [Getting Started With 
Embeddings](https://huggingface.co/blog/getting-started-with-embeddings)."
+      ],
+      "metadata": {
+        "id": "Dbkmu3HP6Kql"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "content = [\n",
+        "    {'x': 'How do I get a replacement Medicare card?'},\n",
+        "    {'x': 'What is the monthly premium for Medicare Part B?'},\n",
+        "    {'x': 'How do I terminate my Medicare Part B (medical 
insurance)?'},\n",
+        "    {'x': 'How do I sign up for Medicare?'},\n",
+        "    {'x': 'Can I sign up for Medicare Part B if I am working and have 
health insurance through an employer?'},\n",
+        "    {'x': 'How do I sign up for Medicare Part B if I already have 
Part A?'},\n",
+        "    {'x': 'What are Medicare late enrollment penalties?'},\n",
+        "    {'x': 'What is Medicare and who can get it?'},\n",
+        "    {'x': 'How can I get help with my Medicare Part A and Part B 
premiums?'},\n",
+        "    {'x': 'What are the different parts of Medicare?'},\n",
+        "    {'x': 'Will my Medicare premiums be higher because of my higher 
income?'},\n",
+        "    {'x': 'What is TRICARE ?'},\n",
+        "    {'x': \"Should I sign up for Medicare Part B if I have Veterans' 
Benefits?\"}\n",
+        "]\n",
+        "\n",
+        "\n",
+        "# helper function that returns a dict containing only first\n",
+        "#10 elements of generated embeddings.\n",
+        "def truncate_embeddings(d):\n",
+        "  for key in d.keys():\n",
+        "    d[key] = d[key][:10]\n",
+        "  return d"
+      ],
+      "metadata": {
+        "id": "LCTUs8F73iDg"
+      },
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "artifact_location_minilm = tempfile.mkdtemp(prefix='huggingface_')\n",
+        "text_embedding_model_name = 
'sentence-transformers/all-MiniLM-L6-v2'\n",
+        "embedding_transform = SentenceTransformerEmbeddings(\n",
+        "        model_name=text_embedding_model_name, columns=['x'])\n",
+        "\n",
+        "with beam.Pipeline() as pipeline:\n",
+        "  data_pcoll = (\n",
+        "          pipeline\n",
+        "          | \"CreateData\" >> beam.Create(content))\n",
+        "  transformed_pcoll = (\n",
+        "      data_pcoll\n",
+        "      | \"MLTransform\" >> 
MLTransform(write_artifact_location=artifact_location_minilm).with_transform(embedding_transform))\n",
+        "\n",
+        "  transformed_pcoll | beam.Map(truncate_embeddings) | 'LogOutput' >> 
beam.Map(print)\n",
+        "\n",
+        "  transformed_pcoll | \"PrintEmbeddingShape\" >> beam.Map(lambda x: 
print(f\"Embedding shape: {len(x['x'])}\"))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/";
+        },
+        "id": "SF6izkN134sf",
+        "outputId": "740f450a-dc9c-4c9d-f4fb-8ef27cca3d74"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'x': [-0.023889463394880295, 0.05525851249694824, 
-0.011654896661639214, -0.03341428190469742, -0.012260555289685726, 
-0.024872763082385063, -0.01266342680901289, 0.025345895439386368, 
0.01850851997733116, -0.08350814878940582]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.01268761046230793, 0.04687413573265076, 
-0.010502150282263756, -0.020383981987833977, -0.01336114201694727, 
0.04232167452573776, 0.016627851873636246, -0.004099288955330849, 
-0.0026070312596857548, -0.010187783278524876]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [0.0004943296662531793, 0.11941202729940414, 
0.005229473114013672, -0.09273427724838257, 0.007772865705192089, 
-0.005324989557266235, 0.03450643643736839, -0.05198145657777786, 
-0.006264965515583754, -0.006110507529228926]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.029711326584219933, 0.02329839952290058, 
-0.05704096704721451, -0.01218305341899395, -0.013710316270589828, 
0.02979600988328457, 0.0637386366724968, 0.0011010386515408754, 
-0.04512352868914604, -0.040747467428445816]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.02562842145562172, 0.070388562977314, 
-0.017379559576511383, -0.0565667562186718, 0.02857644483447075, 
0.052822552621364594, 0.06706249713897705, -0.05261750519275665, 
-0.054702047258615494, -0.11623040586709976]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.022656124085187912, 0.021159743890166283, 
0.0051048519089818, -0.04649421200156212, 0.009073587134480476, 
0.04149482399225235, 0.0542682446539402, -0.02418488636612892, 
-0.013482789508998394, -0.07596635073423386]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.0029113641940057278, 0.060791268944740295, 
-0.009175681509077549, -0.006133317016065121, 0.04049248993396759, 
0.036593958735466, 0.002054463606327772, -0.03134453296661377, 
0.03180575743317604, -0.02349487692117691]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.08052562177181244, 0.05988812819123268, 
-0.048846807330846786, -0.040176115930080414, -0.06334187835454941, 
0.04184781387448311, 0.11904510855674744, 0.010651882737874985, 
-0.030094878748059273, -0.004561211448162794]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.0343877375125885, 0.07250142097473145, 
0.01443990133702755, -0.03669498860836029, 0.014018685556948185, 
0.06307007372379303, 0.03468254581093788, -0.014530746266245842, 
-0.05986189469695091, -0.04538322612643242]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.005963834468275309, 0.025043703615665436, 
-0.003182061715051532, -0.025242920964956284, -0.0398230254650116, 
-0.012771873734891415, 0.0447133406996727, 0.014535333029925823, 
-0.03821341320872307, -0.04114910215139389]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.039007965475320816, -0.010609461925923824, 
-0.007382705342024565, -0.050189778208732605, -0.0025175788905471563, 
-0.0416409894824028, 0.02696940489113331, -0.014800631441175938, 
-0.014126974157989025, -0.061636749655008316]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.09598278254270554, -0.06301165372133255, 
-0.11690578609704971, -0.05907457321882248, -0.05132286250591278, 
-0.0034391973167657852, 0.018687350675463676, 0.006543711293488741, 
-0.04905705526471138, -0.031649429351091385]}\n",
+            "Embedding shape: 10\n",
+            "{'x': [-0.011600406840443611, 0.05651004612445831, 
0.016623979434370995, -0.09469003975391388, -0.009865491650998592, 
0.07234735041856766, 0.04412448778748512, -0.0411749929189682, 
-0.04212445020675659, -0.10263106226921082]}\n",
+            "Embedding shape: 10\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Pass additional arguments that are supported by 
`sentence-transformer` models, such as `convert_to_numpy=False`. These 
arguments are passed as a `dict` to the `SentenceTransformerEmbeddings` 
transform by using the `inference_args` parameter.\n",
+        "\n",
+        "By passing `convert_to_numpy=False`, the output will contain 
`torch.Tensor`s."
+      ],
+      "metadata": {
+        "id": "1MFom0PW_vRv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "artifact_location_minilm_with_inference_args = 
tempfile.mkdtemp(prefix='huggingface_')\n",
+        "\n",
+        "embedding_transform = SentenceTransformerEmbeddings(\n",
+        "        model_name=text_embedding_model_name, columns=['x'],\n",
+        "        inference_args={'convert_to_numpy': False}\n",
+        "        )\n",
+        "\n",
+        "with beam.Pipeline() as pipeline:\n",
+        "  data_pcoll = (\n",
+        "          pipeline\n",
+        "          | \"CreateData\" >> beam.Create(content))\n",
+        "  transformed_pcoll = (\n",
+        "      data_pcoll\n",
+        "      | \"MLTransform\" >> 
MLTransform(write_artifact_location=artifact_location_minilm_with_inference_args).with_transform(embedding_transform))\n",

Review Comment:
   Nit: the formatting is a little funky here, this level of tabbing should 
match the expression right above it



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