diff --git a/SiameseBERT_SemanticSearch.ipynb b/SiameseBERT_SemanticSearch.ipynb index e3fa478..fedb035 100644 --- a/SiameseBERT_SemanticSearch.ipynb +++ b/SiameseBERT_SemanticSearch.ipynb @@ -1,485 +1,549 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "liIk8f980xT5" + }, + "source": [ + "# Sentence Embeddings using Siamese BERT-Networks\n", + "---\n", + "This Google Colab Notebook illustrates using the Sentence Transformer python library to quickly create BERT embeddings for sentences and perform fast semantic searches.\n", + "\n", + "The Sentence Transformer library is available on [pypi](https://pypi.org/project/sentence-transformers/) and [github](https://github.com/UKPLab/sentence-transformers). The library implements code from the ACL 2019 paper entitled \"[Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410.pdf)\" by Nils Reimers and Iryna Gurevych.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "bzljhyTQEZds" + }, + "source": [ + "## Install Sentence Transformer Library" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { "colab": { - "name": "SiameseBERT-SemanticSearch.ipynb", - "provenance": [], - "collapsed_sections": [], - "toc_visible": true + "base_uri": "https://localhost:8080/", + "height": 768 }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" + "colab_type": "code", + "id": "AmxRYxNDvn6y", + "outputId": "81694439-a0c9-45f1-88e6-ea77aa94f39a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting sentence-transformers\n", + "\u001b[?25l Downloading 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size=884629 sha256=63585f2eb5e65adb473e2e282bda1bdc423a86bfde2a175308c56709911f6678\n", + " Stored in directory: /root/.cache/pip/wheels/6d/ec/1a/21b8912e35e02741306f35f66c785f3afe94de754a0eaf1422\n", + "Successfully built sentence-transformers sacremoses\n", + "Installing collected packages: sacremoses, sentencepiece, transformers, sentence-transformers\n", + "Successfully installed sacremoses-0.0.38 sentence-transformers-0.2.5 sentencepiece-0.1.85 transformers-2.3.0\n" + ] } + ], + "source": [ + "# Install the library using pip\n", + "!pip install sentence-transformers\n", + "!pip install scipy" + ] }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "liIk8f980xT5", - "colab_type": "text" - }, - "source": [ - "# Sentence Embeddings using Siamese BERT-Networks\n", - "---\n", - "This Google Colab Notebook illustrates using the Sentence Transformer python library to quickly create BERT embeddings for sentences and perform fast semantic searches.\n", - "\n", - "The Sentence Transformer library is available on [pypi](https://pypi.org/project/sentence-transformers/) and [github](https://github.com/UKPLab/sentence-transformers). The library implements code from the ACL 2019 paper entitled \"[Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://www.aclweb.org/anthology/D19-1410.pdf)\" by Nils Reimers and Iryna Gurevych.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "bzljhyTQEZds", - "colab_type": "text" - }, - "source": [ - "## Install Sentence Transformer Library" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "AmxRYxNDvn6y", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 768 - }, - "outputId": "81694439-a0c9-45f1-88e6-ea77aa94f39a" - }, - "source": [ - "# Install the library using pip\n", - "!pip install sentence-transformers" - ], - "execution_count": 1, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Collecting sentence-transformers\n", - "\u001b[?25l Downloading https://files.pythonhosted.org/packages/c9/91/c85ddef872d5bb39949386930c1f834ac382e145fcd30155b09d6fb65c5a/sentence-transformers-0.2.5.tar.gz (49kB)\n", - 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" Stored in directory: /root/.cache/pip/wheels/b4/ce/39/5bbda8ac34eb52df8c6531382ca077773fbfcbfb6386e5d66c\n", - " Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for sacremoses: filename=sacremoses-0.0.38-cp36-none-any.whl size=884629 sha256=63585f2eb5e65adb473e2e282bda1bdc423a86bfde2a175308c56709911f6678\n", - " Stored in directory: /root/.cache/pip/wheels/6d/ec/1a/21b8912e35e02741306f35f66c785f3afe94de754a0eaf1422\n", - "Successfully built sentence-transformers sacremoses\n", - "Installing collected packages: sacremoses, sentencepiece, transformers, sentence-transformers\n", - "Successfully installed sacremoses-0.0.38 sentence-transformers-0.2.5 sentencepiece-0.1.85 transformers-2.3.0\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "eIAKz6KVEndZ", - "colab_type": "text" - }, - "source": [ - "## Load the BERT Model" - ] + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eIAKz6KVEndZ" + }, + "source": [ + "## Load the BERT Model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 }, + "colab_type": "code", + "id": "5IO_j2Ofv5pq", + "outputId": "717b9097-4c19-49b0-97cb-b8c31c84d3ec" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "5IO_j2Ofv5pq", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 80 - }, - "outputId": "717b9097-4c19-49b0-97cb-b8c31c84d3ec" - }, - "source": [ - "from sentence_transformers import SentenceTransformer\n", - "\n", - "# Load the BERT model. Various models trained on Natural Language Inference (NLI) https://github.com/UKPLab/sentence-transformers/blob/master/docs/pretrained-models/nli-models.md and \n", - "# Semantic Textual Similarity are available https://github.com/UKPLab/sentence-transformers/blob/master/docs/pretrained-models/sts-models.md\n", - "\n", - "model = SentenceTransformer('bert-base-nli-mean-tokens')" + "data": { + "text/html": [ + "

\n", + "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.
\n", + "We recommend you upgrade now \n", + "or ensure your notebook will continue to use TensorFlow 1.x via the %tensorflow_version 1.x magic:\n", + "more info.

\n" ], - "execution_count": 2, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/html": [ - "

\n", - "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.
\n", - "We recommend you upgrade now \n", - "or ensure your notebook will continue to use TensorFlow 1.x via the %tensorflow_version 1.x magic:\n", - "more info.

\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - } - }, - { - "output_type": "stream", - "text": [ - "100%|██████████| 405M/405M [00:18<00:00, 21.8MB/s]\n" - ], - "name": "stderr" - } + "text/plain": [ + "" ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "I7_Ib3ITEwgO", - "colab_type": "text" - }, - "source": [ - "## Setup a Corpus" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "rEa50nefv7HY", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "be7fcea8-15ac-4ab6-ebdf-55e507a7d496" - }, - "source": [ - "# A corpus is a list with documents split by sentences.\n", - "\n", - "sentences = ['Absence of sanity', \n", - " 'Lack of saneness',\n", - " 'A man is eating food.',\n", - " 'A man is eating a piece of bread.',\n", - " 'The girl is carrying a baby.',\n", - " 'A man is riding a horse.',\n", - " 'A woman is playing violin.',\n", - " 'Two men pushed carts through the woods.',\n", - " 'A man is riding a white horse on an enclosed ground.',\n", - " 'A monkey is playing drums.',\n", - " 'A cheetah is running behind its prey.']\n", - "\n", - "# Each sentence is encoded as a 1-D vector with 78 columns\n", - "sentence_embeddings = model.encode(sentences)\n", - "\n", - "print('Sample BERT embedding vector - length', len(sentence_embeddings[0]))\n", - "\n", - "print('Sample BERT embedding vector - note includes negative values', sentence_embeddings[0])" - ], - "execution_count": 24, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Sample BERT embedding vector - length 768\n", - "Sample BERT embedding vector - note includes negative values [ 2.95403183e-01 2.91810960e-01 2.16480136e+00 2.20419839e-01\n", - " -1.30865965e-02 1.01950336e+00 1.51298153e+00 2.34131858e-01\n", - " 2.73058057e-01 1.35122865e-01 -1.11313331e+00 -1.25884697e-01\n", - " 1.45378590e-01 9.77708638e-01 1.39352250e+00 4.57705110e-01\n", - 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Various models trained on Natural Language Inference (NLI) https://github.com/UKPLab/sentence-transformers/blob/master/docs/pretrained-models/nli-models.md and \n", + "# Semantic Textual Similarity are available https://github.com/UKPLab/sentence-transformers/blob/master/docs/pretrained-models/sts-models.md\n", + "\n", + "model = SentenceTransformer('bert-base-nli-mean-tokens')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "I7_Ib3ITEwgO" + }, + "source": [ + "## Setup a Corpus" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, + "colab_type": "code", + "id": "rEa50nefv7HY", + "outputId": "be7fcea8-15ac-4ab6-ebdf-55e507a7d496" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "QLZaLMDuGhEh", - "colab_type": "text" - }, - "source": [ - "## Perform Semantic Search" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample BERT embedding vector - length 768\n", + "Sample BERT embedding vector - note includes negative values [ 2.95403183e-01 2.91810960e-01 2.16480136e+00 2.20419839e-01\n", + " -1.30865965e-02 1.01950336e+00 1.51298153e+00 2.34131858e-01\n", + " 2.73058057e-01 1.35122865e-01 -1.11313331e+00 -1.25884697e-01\n", + " 1.45378590e-01 9.77708638e-01 1.39352250e+00 4.57705110e-01\n", + " -5.82131505e-01 -7.24940956e-01 -3.61734480e-01 -2.27514893e-01\n", + " 1.66630689e-02 2.04861969e-01 6.55133009e-01 -1.29376340e+00\n", + " -7.26099372e-01 -1.91135541e-01 -3.07211697e-01 -1.30278623e+00\n", + " -1.42963886e+00 5.67453261e-03 3.54811519e-01 4.83712614e-01\n", + " 6.65388048e-01 5.33848584e-01 6.40496492e-01 5.90408444e-01\n", + " 7.83850178e-02 -1.07759213e+00 -1.24676526e-01 -3.98406386e-01\n", + " 7.36313939e-01 5.28293252e-01 5.63290775e-01 4.14546162e-01\n", + " 4.49179649e-01 -9.58786383e-02 1.45424592e+00 -2.69145072e-01\n", + " -2.44059876e-01 -1.10387051e+00 -2.00923532e-01 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-8.49243045e-01 5.52447960e-02 8.02986920e-01 4.63941514e-01\n", + " 6.54758275e-01 -5.37097454e-04 -2.04843923e-01 9.76368427e-01\n", + " -3.53015751e-01 9.11225975e-01 7.22659290e-01 -3.19766104e-01\n", + " 2.27475837e-01 -2.76821464e-01 4.26936716e-01 3.22493613e-01\n", + " -4.22099531e-01 2.96951920e-01 6.81850255e-01 1.47002387e+00\n", + " -2.75375154e-02 -7.22703218e-01 3.05495150e-02 -5.42968288e-02\n", + " -5.06122649e-01 4.57347259e-02 8.26341063e-02 4.99086320e-01\n", + " 9.00193095e-01 -8.83195698e-01 -9.96071637e-01 -2.98155367e-01\n", + " -4.14106518e-01 -5.26974797e-01 -5.91103375e-01 -2.92363405e-01]\n" + ] + } + ], + "source": [ + "# A corpus is a list with documents split by sentences.\n", + "\n", + "sentences = ['Absence of sanity', \n", + " 'Lack of saneness',\n", + " 'A man is eating food.',\n", + " 'A man is eating a piece of bread.',\n", + " 'The girl is carrying a baby.',\n", + " 'A man is riding a horse.',\n", + " 'A woman is playing violin.',\n", + " 'Two men pushed carts through the woods.',\n", + " 'A man is riding a white horse on an enclosed ground.',\n", + " 'A monkey is playing drums.',\n", + " 'A cheetah is running behind its prey.']\n", + "\n", + "# Each sentence is encoded as a 1-D vector with 78 columns\n", + "sentence_embeddings = model.encode(sentences)\n", + "\n", + "print('Sample BERT embedding vector - length', len(sentence_embeddings[0]))\n", + "\n", + "print('Sample BERT embedding vector - note includes negative values', sentence_embeddings[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "QLZaLMDuGhEh" + }, + "source": [ + "## Perform Semantic Search" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 }, + "colab_type": "code", + "id": "D6wkgQmywt0k", + "outputId": "2e038dec-abbe-4628-94a7-dc00d38d8a03" + }, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "D6wkgQmywt0k", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 221 - }, - "outputId": "2e038dec-abbe-4628-94a7-dc00d38d8a03" - }, - "source": [ - "#@title Sematic Search Form\n", - "\n", - "# code adapted from https://github.com/UKPLab/sentence-transformers/blob/master/examples/application_semantic_search.py\n", - "\n", - "query = 'Nobody has sane thoughts' #@param {type: 'string'}\n", - "\n", - "queries = [query]\n", - "query_embeddings = model.encode(queries)\n", - "\n", - "# Find the closest 3 sentences of the corpus for each query sentence based on cosine similarity\n", - "number_top_matches = 3 #@param {type: \"number\"}\n", - "\n", - "print(\"Semantic Search Results\")\n", - "\n", - "for query, query_embedding in zip(queries, query_embeddings):\n", - " distances = scipy.spatial.distance.cdist([query_embedding], sentence_embeddings, \"cosine\")[0]\n", - "\n", - " results = zip(range(len(distances)), distances)\n", - " results = sorted(results, key=lambda x: x[1])\n", - "\n", - " print(\"\\n\\n======================\\n\\n\")\n", - " print(\"Query:\", query)\n", - " print(\"\\nTop 5 most similar sentences in corpus:\")\n", - "\n", - " for idx, distance in results[0:number_top_matches]:\n", - " print(sentences[idx].strip(), \"(Cosine Score: %.4f)\" % (1-distance))" - ], - "execution_count": 27, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Semantic Search Results\n", - "\n", - "\n", - "======================\n", - "\n", - "\n", - "Query: Nobody has sane thoughts\n", - "\n", - "Top 5 most similar sentences in corpus:\n", - "Lack of saneness (Cosine Score: 0.8958)\n", - "Absence of sanity (Cosine Score: 0.8744)\n", - "A man is riding a horse. (Cosine Score: 0.1705)\n" - ], - "name": "stdout" - } - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Semantic Search Results\n", + "\n", + "\n", + "======================\n", + "\n", + "\n", + "Query: Nobody has sane thoughts\n", + "\n", + "Top 5 most similar sentences in corpus:\n", + "Lack of saneness (Cosine Score: 0.8958)\n", + "Absence of sanity (Cosine Score: 0.8744)\n", + "A man is riding a horse. (Cosine Score: 0.1705)\n" + ] } - ] -} \ No newline at end of file + ], + "source": [ + "#@title Sematic Search Form\n", + "\n", + "# code adapted from https://github.com/UKPLab/sentence-transformers/blob/master/examples/application_semantic_search.py\n", + "\n", + "query = 'Nobody has sane thoughts' #@param {type: 'string'}\n", + "\n", + "queries = [query]\n", + "query_embeddings = model.encode(queries)\n", + "\n", + "# Find the closest 3 sentences of the corpus for each query sentence based on cosine similarity\n", + "number_top_matches = 3 #@param {type: \"number\"}\n", + "\n", + "print(\"Semantic Search Results\")\n", + "\n", + "for query, query_embedding in zip(queries, query_embeddings):\n", + " distances = scipy.spatial.distance.cdist([query_embedding], sentence_embeddings, \"cosine\")[0]\n", + "\n", + " results = zip(range(len(distances)), distances)\n", + " results = sorted(results, key=lambda x: x[1])\n", + "\n", + " print(\"\\n\\n======================\\n\\n\")\n", + " print(\"Query:\", query)\n", + " print(\"\\nTop 5 most similar sentences in corpus:\")\n", + "\n", + " for idx, distance in results[0:number_top_matches]:\n", + " print(sentences[idx].strip(), \"(Cosine Score: %.4f)\" % (1-distance))" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "SiameseBERT-SemanticSearch.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}