{ "cells": [ { "cell_type": "markdown", "id": "nk6-0Tu0rWmp", "metadata": { "id": "nk6-0Tu0rWmp" }, "source": [ "# Searching for machine learning models using semantic search\n", "\n", "> Finding models on the Hugging Face hub using semantic search\n", "\n", "- toc: true \n", "- badges: false\n", "- comments: true\n", "- categories: [huggingface, huggingface-datasets, semantic-search]\n", "- search_exclude: false\n", "- badges: true\n", "- image: https://github.com/davanstrien/blog/blob/master/images/hub_model_search.webp?raw=true" ] }, { "cell_type": "markdown", "id": "6d3b7adf-e1d9-45bb-a663-debef927125d", "metadata": { "id": "6d3b7adf-e1d9-45bb-a663-debef927125d" }, "source": [ "The [Hugging Face model hub](https://huggingface.co/models) has (at the time of the last checking) 60,509 models publicly available. Some of these models are useful as base models for further fine-tuning; these include your classics like `bert-base-uncased`. \n", "\n", "The hub also has more obscure indie hits that might already do a good job on your desired downstream task or be a closer start. For example, if one wanted to classify the genre of 18th Century books, it might make sense to start with [a model for classifying 19th Century books](https://huggingface.co/BritishLibraryLabs/bl-books-genre). \n", "\n", "## Finding candidate models\n", "\n", "Ideally, we'd like a quick way to identify if a model might already do close to what we want. From there, we would likely want to review a bunch of other info about the model before deciding if it might be helpful for us or not. \n", "\n", "Unfortunately, finding suitable models on the hub isn't always that easy. Even knowing that models for genre classification exist on the hub, we don't find any results.\n", "\n", "![](../images/hub_model_search.webp)\n", "\n", "It's not documented exactly how the search on the hub works, but it seems to be based mainly on the model's name rather than the README or other information. In this blog post, I will continue some [previous experiments with embeddings](https://danielvanstrien.xyz/metadata/deployment/huggingface/ethics/huggingface-datasets/faiss/2022/01/13/image_search.html) to see if there might be different ways in which we could identify potential models. \n", "\n", "This will be a very rough experiment and is more about establishing whether this is an avenue worth exploring rather than a fully fleshed-out approach. " ] }, { "cell_type": "markdown", "id": "J6T4-TL7ZO4x", "metadata": { "id": "J6T4-TL7ZO4x" }, "source": [ "First install some libraries we'll use:" ] }, { "cell_type": "code", "execution_count": 1, "id": "4NYGk1z2859D", "metadata": { "id": "4NYGk1z2859D" }, "outputs": [], "source": [ "import torch" ] }, { "cell_type": "code", "execution_count": 2, "id": "aM_Rgsey87zT", "metadata": { "id": "aM_Rgsey87zT" }, "outputs": [], "source": [ "deps = [\"datasets\" ,\"sentence-transformers\", \"rich['jupyter']\", \"requests\"]\n", "if torch.cuda.is_available():\n", " deps.append(\"faiss-gpu\")\n", "else:\n", " deps.append(\"faise-cpu\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "9iRSQjuh9Zcv", "metadata": { "id": "9iRSQjuh9Zcv" }, "outputs": [], "source": [ "%%capture\n", "!pip install {\" \".join(deps)} --upgrade" ] }, { "cell_type": "code", "execution_count": 4, "id": "Q1aENjqJQd9-", "metadata": { "id": "Q1aENjqJQd9-" }, "outputs": [], "source": [ "!git config --global credential.helper store" ] }, { "cell_type": "markdown", "id": "UXIvkrdJZTUa", "metadata": { "id": "UXIvkrdJZTUa" }, "source": [ "These days I almost always have the rich extension loaded!" ] }, { "cell_type": "code", "execution_count": 5, "id": "b96767fb-42cc-43c0-ad41-e8b6acad55e2", "metadata": { "id": "b96767fb-42cc-43c0-ad41-e8b6acad55e2" }, "outputs": [], "source": [ "%load_ext rich" ] }, { "cell_type": "markdown", "id": "LNHwRQYVZcV9", "metadata": { "id": "LNHwRQYVZcV9" }, "source": [ "## Using the huggingface_hub API to download some model metadata \n", "\n", "Our goal is to see if we might be able to find suitable models more efficiently using some form of semantic search (i.e. using embeddings). To do this, we should grab some model data from the hub. The easiest way to do this is using the hub API. " ] }, { "cell_type": "code", "execution_count": 6, "id": "067510aa-422e-4df0-a96e-954b2e3e7b4c", "metadata": { "id": "067510aa-422e-4df0-a96e-954b2e3e7b4c" }, "outputs": [], "source": [ "from huggingface_hub import hf_api\n", "import re\n", "from rich import print" ] }, { "cell_type": "code", "execution_count": 7, "id": "b267170f-c18f-48d7-9b74-a4cda2b6f7ec", "metadata": { "id": "b267170f-c18f-48d7-9b74-a4cda2b6f7ec" }, "outputs": [], "source": [ "api = hf_api.HfApi()" ] }, { "cell_type": "code", "execution_count": 8, "id": "7951900f-2e41-4d6d-875d-21d6e0191325", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "7951900f-2e41-4d6d-875d-21d6e0191325", "outputId": "61ea0a7c-20fa-4759-f942-6d3d381a2958" }, "outputs": [ { "data": { "text/html": [ "
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\n" ], "text/plain": [ "\u001b[1m<\u001b[0m\u001b[1;95mhuggingface_hub.hf_api.HfApi\u001b[0m\u001b[39m object at \u001b[0m\u001b[1;36m0x7f63832ff810\u001b[0m\u001b[1m>\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "api" ] }, { "cell_type": "markdown", "id": "SIxLLDrJZ3eR", "metadata": { "id": "SIxLLDrJZ3eR" }, "source": [ "We can take a look at some example models" ] }, { "cell_type": "code", "execution_count": 9, "id": "f48afd68-f454-446b-895f-72fa4db1b0b0", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 644 }, "id": "f48afd68-f454-446b-895f-72fa4db1b0b0", "outputId": "f562419a-3aa8-400f-fd21-ee8704b78eca" }, "outputs": [ { "data": { "text/html": [ "
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       "    'tokenizer_config.json',\n",
       "    'vocab.txt'\n",
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       "        tags: ['pytorch', 'tf', 'rust', 'distilbert', 'text-classification', 'en', 'dataset:sst2', 'dataset:glue', 'transformers', 'license:apache-2.0', 'model-index']\n",
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\n" ], "text/plain": [ "\n", "ModelInfo: \u001b[1m{\u001b[0m\n", " modelId: distilbert-base-uncased-finetuned-sst-\u001b[1;36m2\u001b[0m-english\n", " sha: 00c3f1ef306e837efb641eaca05d24d161d9513c\n", " lastModified: \u001b[1;36m2022\u001b[0m-\u001b[1;36m07\u001b[0m-22T\u001b[1;92m08:00:55\u001b[0m.000Z\n", " tags: \u001b[1m[\u001b[0m\u001b[32m'pytorch'\u001b[0m, \u001b[32m'tf'\u001b[0m, \u001b[32m'rust'\u001b[0m, \u001b[32m'distilbert'\u001b[0m, \u001b[32m'text-classification'\u001b[0m, \u001b[32m'en'\u001b[0m, \u001b[32m'dataset:sst2'\u001b[0m, \u001b[32m'dataset:glue'\u001b[0m, \u001b[32m'transformers'\u001b[0m, \u001b[32m'license:apache-2.0'\u001b[0m, \u001b[32m'model-index'\u001b[0m\u001b[1m]\u001b[0m\n", " pipeline_tag: text-classification\n", " siblings: \u001b[1m[\u001b[0m\u001b[1;35mRepoFile\u001b[0m\u001b[1m(\u001b[0m\u001b[33mrfilename\u001b[0m=\u001b[32m'.gitattributes'\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mRepoFile\u001b[0m\u001b[1m(\u001b[0m\u001b[33mrfilename\u001b[0m=\u001b[32m'README.md'\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mRepoFile\u001b[0m\u001b[1m(\u001b[0m\u001b[33mrfilename\u001b[0m=\u001b[32m'config.json'\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mRepoFile\u001b[0m\u001b[1m(\u001b[0m\u001b[33mrfilename\u001b[0m=\u001b[32m'map.jpeg'\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mRepoFile\u001b[0m\u001b[1m(\u001b[0m\u001b[33mrfilename\u001b[0m=\u001b[32m'pytorch_model.bin'\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mRepoFile\u001b[0m\u001b[1m(\u001b[0m\u001b[33mrfilename\u001b[0m=\u001b[32m'rust_model.ot'\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mRepoFile\u001b[0m\u001b[1m(\u001b[0m\u001b[33mrfilename\u001b[0m=\u001b[32m'tf_model.h5'\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mRepoFile\u001b[0m\u001b[1m(\u001b[0m\u001b[33mrfilename\u001b[0m=\u001b[32m'tokenizer_config.json'\u001b[0m\u001b[1m)\u001b[0m, \u001b[1;35mRepoFile\u001b[0m\u001b[1m(\u001b[0m\u001b[33mrfilename\u001b[0m=\u001b[32m'vocab.txt'\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n", " private: \u001b[3;91mFalse\u001b[0m\n", " author: \u001b[3;35mNone\u001b[0m\n", " config: \u001b[3;35mNone\u001b[0m\n", " id: distilbert-base-uncased-finetuned-sst-\u001b[1;36m2\u001b[0m-english\n", " downloads: \u001b[1;36m5185721\u001b[0m\n", " likes: \u001b[1;36m76\u001b[0m\n", " library_name: transformers\n", "\u001b[1m}\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "all_models[0]" ] }, { "cell_type": "markdown", "id": "SJGBGJdUcY29", "metadata": { "id": "SJGBGJdUcY29" }, "source": [ "Let's see how many models that gives us. " ] }, { "cell_type": "code", "execution_count": 17, "id": "cc2ebb52-22a0-4b5c-827f-a30fb1888127", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "cc2ebb52-22a0-4b5c-827f-a30fb1888127", "outputId": "cb4843c7-c2a5-4ce4-88e2-8a3ca61d0b62" }, "outputs": [ { "data": { "text/html": [ "
6860\n",
       "
\n" ], "text/plain": [ "\u001b[1;36m6860\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "len(all_models)" ] }, { "cell_type": "markdown", "id": "styP6k8mcdf5", "metadata": { "id": "styP6k8mcdf5" }, "source": [ "Later on, in this blog, we'll want to work with the `config.json` files (we'll get back to why later!), so we'll quickly check that all our models have this. " ] }, { "cell_type": "code", "execution_count": 18, "id": "874f1d37-e12d-4e96-8ba6-c3e34375c808", "metadata": { "id": "874f1d37-e12d-4e96-8ba6-c3e34375c808" }, "outputs": [], "source": [ "def has_config(model):\n", " has_config = False\n", " files = model.siblings\n", " for file in files:\n", " if \"config.json\" in file.rfilename:\n", " has_config = True\n", " return has_config\n", " else:\n", " continue" ] }, { "cell_type": "code", "execution_count": 19, "id": "004ba1c9-6399-4f30-8874-3293d473ae82", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "004ba1c9-6399-4f30-8874-3293d473ae82", "outputId": "6170ebb0-3e34-4fd7-c65d-168f44cdc0a1" }, "outputs": [ { "data": { "text/html": [ "
True\n",
       "
\n" ], "text/plain": [ "\u001b[3;92mTrue\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "has_config(all_models[0])" ] }, { "cell_type": "code", "execution_count": 20, "id": "3a5685b2-c398-4b68-931b-8555dff260f4", "metadata": { "id": "3a5685b2-c398-4b68-931b-8555dff260f4" }, "outputs": [], "source": [ "has_config = [model for model in all_models if has_config(model)]" ] }, { "cell_type": "markdown", "id": "5M08yVc2c7_h", "metadata": { "id": "5M08yVc2c7_h" }, "source": [ "Let's check how many we have now" ] }, { "cell_type": "code", "execution_count": 21, "id": "2a10c2d8-a651-4ab9-9a63-2c16d1c0ca6c", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "2a10c2d8-a651-4ab9-9a63-2c16d1c0ca6c", "outputId": "a2781c38-844b-4e53-f384-955162485784" }, "outputs": [ { "data": { "text/html": [ "
6858\n",
       "
\n" ], "text/plain": [ "\u001b[1;36m6858\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "len(has_config)" ] }, { "cell_type": "markdown", "id": "QNmffwkcdCUg", "metadata": { "id": "QNmffwkcdCUg" }, "source": [ "We can also download a particular file from the hub " ] }, { "cell_type": "code", "execution_count": 22, "id": "407bf115-4fcc-404f-b087-07713bddba08", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "b0a08dc1a7ae40aaa3c5d10e5ac6b711", "73b673eb01584887a9cc11e627256faa", "99e8f5a24f954c4e950e7bdb7e0d1af6", "6240285db4444d648b6dadec3dbf76c4", "2aa31aa07c574edcb2b94503d955d74b", "12f542afbb2942fea0f4f9b1e6483983", "ed8f6bb5270d48489a7f8c2ae52f1ef4", "b7f39a86824b403da2a24973ff2e0f6d", "2f6781c7ba1845379a54323def5e0cd6", "56ced24d012d486fb8eac7dd70c49a6e", "a34afb994d24462ea6d707100fe6dba9" ] }, "id": "407bf115-4fcc-404f-b087-07713bddba08", "outputId": "b35734dc-07a4-4a84-da67-8545de2db38e" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b0a08dc1a7ae40aaa3c5d10e5ac6b711", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading: 0%| | 0.00/629 [00:00'/root/.cache/huggingface/hub/models--distilbert-base-uncased-finetuned-sst-2-english/snapshots/00c3f1ef306e837efb641eaca05d24d161d9513c/config.json'\n", "\n" ], "text/plain": [ "\u001b[32m'/root/.cache/huggingface/hub/models--distilbert-base-uncased-finetuned-sst-2-english/snapshots/00c3f1ef306e837efb641eaca05d24d161d9513c/config.json'\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "file" ] }, { "cell_type": "code", "execution_count": 24, "id": "82b0df42-0a43-444d-8c95-20416fbbe1e7", "metadata": { "id": "82b0df42-0a43-444d-8c95-20416fbbe1e7" }, "outputs": [], "source": [ "import json\n", "with open(file) as f:\n", " data = json.load(f)" ] }, { "cell_type": "code", "execution_count": 25, "id": "0e74566e-7454-4821-835f-48bcbf9994ee", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 397 }, "id": "0e74566e-7454-4821-835f-48bcbf9994ee", "outputId": "70e58eca-c14f-4233-ad3e-11ba57ec22db" }, "outputs": [ { "data": { "text/html": [ "
\n",
       "{\n",
       "    'activation': 'gelu',\n",
       "    'architectures': ['DistilBertForSequenceClassification'],\n",
       "    'attention_dropout': 0.1,\n",
       "    'dim': 768,\n",
       "    'dropout': 0.1,\n",
       "    'finetuning_task': 'sst-2',\n",
       "    'hidden_dim': 3072,\n",
       "    'id2label': {'0': 'NEGATIVE', '1': 'POSITIVE'},\n",
       "    'initializer_range': 0.02,\n",
       "    'label2id': {'NEGATIVE': 0, 'POSITIVE': 1},\n",
       "    'max_position_embeddings': 512,\n",
       "    'model_type': 'distilbert',\n",
       "    'n_heads': 12,\n",
       "    'n_layers': 6,\n",
       "    'output_past': True,\n",
       "    'pad_token_id': 0,\n",
       "    'qa_dropout': 0.1,\n",
       "    'seq_classif_dropout': 0.2,\n",
       "    'sinusoidal_pos_embds': False,\n",
       "    'tie_weights_': True,\n",
       "    'vocab_size': 30522\n",
       "}\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[1m{\u001b[0m\n", " \u001b[32m'activation'\u001b[0m: \u001b[32m'gelu'\u001b[0m,\n", " \u001b[32m'architectures'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'DistilBertForSequenceClassification'\u001b[0m\u001b[1m]\u001b[0m,\n", " \u001b[32m'attention_dropout'\u001b[0m: \u001b[1;36m0.1\u001b[0m,\n", " \u001b[32m'dim'\u001b[0m: \u001b[1;36m768\u001b[0m,\n", " \u001b[32m'dropout'\u001b[0m: \u001b[1;36m0.1\u001b[0m,\n", " \u001b[32m'finetuning_task'\u001b[0m: \u001b[32m'sst-2'\u001b[0m,\n", " \u001b[32m'hidden_dim'\u001b[0m: \u001b[1;36m3072\u001b[0m,\n", " \u001b[32m'id2label'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'0'\u001b[0m: \u001b[32m'NEGATIVE'\u001b[0m, \u001b[32m'1'\u001b[0m: \u001b[32m'POSITIVE'\u001b[0m\u001b[1m}\u001b[0m,\n", " \u001b[32m'initializer_range'\u001b[0m: \u001b[1;36m0.02\u001b[0m,\n", " \u001b[32m'label2id'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'NEGATIVE'\u001b[0m: \u001b[1;36m0\u001b[0m, \u001b[32m'POSITIVE'\u001b[0m: \u001b[1;36m1\u001b[0m\u001b[1m}\u001b[0m,\n", " \u001b[32m'max_position_embeddings'\u001b[0m: \u001b[1;36m512\u001b[0m,\n", " \u001b[32m'model_type'\u001b[0m: \u001b[32m'distilbert'\u001b[0m,\n", " \u001b[32m'n_heads'\u001b[0m: \u001b[1;36m12\u001b[0m,\n", " \u001b[32m'n_layers'\u001b[0m: \u001b[1;36m6\u001b[0m,\n", " \u001b[32m'output_past'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n", " \u001b[32m'pad_token_id'\u001b[0m: \u001b[1;36m0\u001b[0m,\n", " \u001b[32m'qa_dropout'\u001b[0m: \u001b[1;36m0.1\u001b[0m,\n", " \u001b[32m'seq_classif_dropout'\u001b[0m: \u001b[1;36m0.2\u001b[0m,\n", " \u001b[32m'sinusoidal_pos_embds'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n", " \u001b[32m'tie_weights_'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n", " \u001b[32m'vocab_size'\u001b[0m: \u001b[1;36m30522\u001b[0m\n", "\u001b[1m}\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data" ] }, { "cell_type": "markdown", "id": "1b-KIcNXd_Mh", "metadata": { "id": "1b-KIcNXd_Mh" }, "source": [ "We can also check if the model has a `README.md`" ] }, { "cell_type": "code", "execution_count": 26, "id": "14a01e6e-0bed-4c77-a448-aee48942d50b", "metadata": { "id": "14a01e6e-0bed-4c77-a448-aee48942d50b" }, "outputs": [], "source": [ "def has_file_in_repo(model,file_name):\n", " has_file = False\n", " files = model.siblings\n", " for file in files:\n", " if file_name in file.rfilename:\n", " has_file = True\n", " return has_file\n", " else:\n", " continue " ] }, { "cell_type": "code", "execution_count": 27, "id": "f3569532-b79d-43b3-b6de-dd7bccbebec6", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "f3569532-b79d-43b3-b6de-dd7bccbebec6", "outputId": "526f5015-acb4-4f1d-f9e3-890a780091dd" }, "outputs": [ { "data": { "text/html": [ "
True\n",
       "
\n" ], "text/plain": [ "\u001b[3;92mTrue\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "has_file_in_repo(has_config[0],'README.md')" ] }, { "cell_type": "code", "execution_count": 28, "id": "7c49793f-2c9b-44a2-8ca5-50aac39be0ce", "metadata": { "id": "7c49793f-2c9b-44a2-8ca5-50aac39be0ce" }, "outputs": [], "source": [ "has_readme = [model for model in has_config if has_file_in_repo(model,\"README.md\")]" ] }, { "cell_type": "markdown", "id": "bBuJmT5jeKou", "metadata": { "id": "bBuJmT5jeKou" }, "source": [ "We can see that there are more configs than READMEs" ] }, { "cell_type": "code", "execution_count": 29, "id": "431659b1-4913-4161-9de1-71d18fad467a", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "431659b1-4913-4161-9de1-71d18fad467a", "outputId": "ece8f7eb-585e-4449-ada1-0167aabfd0cf" }, "outputs": [ { "data": { "text/html": [ "
3482\n",
       "
\n" ], "text/plain": [ "\u001b[1;36m3482\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "len(has_readme)" ] }, { "cell_type": "code", "execution_count": 30, "id": "5a9d1090-b64e-4715-b5bc-16ed72024696", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "5a9d1090-b64e-4715-b5bc-16ed72024696", "outputId": "46efd8af-3b3a-45d3-dbaa-8f3c9c22447c" }, "outputs": [ { "data": { "text/html": [ "
6858\n",
       "
\n" ], "text/plain": [ "\u001b[1;36m6858\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "len(has_config)" ] }, { "cell_type": "markdown", "id": "YA22bNqueQP2", "metadata": { "id": "YA22bNqueQP2" }, "source": [ "We now write some functions to grab both the `README.md` and `config.json` files from the hub. " ] }, { "cell_type": "code", "execution_count": null, "id": "8f2c70a3-6c96-4c82-a93f-0c65bfcd68b5", "metadata": { "id": "8f2c70a3-6c96-4c82-a93f-0c65bfcd68b5" }, "outputs": [], "source": [ "from requests.exceptions import JSONDecodeError\n", "import concurrent.futures" ] }, { "cell_type": "code", "execution_count": null, "id": "ab2a6d98-e53e-49e3-b9dd-3f001e90ec09", "metadata": { "id": "ab2a6d98-e53e-49e3-b9dd-3f001e90ec09" }, "outputs": [], "source": [ "@lru_cache(maxsize=None)\n", "def get_model_labels(model):\n", " try:\n", " url = hf_hub_url(repo_id=model.modelId, filename=\"config.json\")\n", " return model.modelId, list(requests.get(url).json()['label2id'].keys())\n", " except (KeyError, JSONDecodeError, AttributeError):\n", " return model.modelId, None\n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "5a80fea4-b7ec-4a4b-98fe-d9987029bc49", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "5a80fea4-b7ec-4a4b-98fe-d9987029bc49", "outputId": "13f8a44e-a0f0-46db-919a-c496fe045580" }, "outputs": [ { "data": { "text/html": [ "
('distilbert-base-uncased-finetuned-sst-2-english', ['NEGATIVE', 'POSITIVE'])\n",
       "
\n" ], "text/plain": [ "\u001b[1m(\u001b[0m\u001b[32m'distilbert-base-uncased-finetuned-sst-2-english'\u001b[0m, \u001b[1m[\u001b[0m\u001b[32m'NEGATIVE'\u001b[0m, \u001b[32m'POSITIVE'\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "get_model_labels(has_config[0])" ] }, { "cell_type": "code", "execution_count": null, "id": "71ba4c5b-d302-4b7d-858b-0ecaecf65ad2", "metadata": { "id": "71ba4c5b-d302-4b7d-858b-0ecaecf65ad2" }, "outputs": [], "source": [ "def get_model_readme(model):\n", " url = hf_hub_url(repo_id=model.modelId, filename=\"README.md\")\n", " return requests.get(url).text\n" ] }, { "cell_type": "code", "execution_count": null, "id": "998611d1-911f-4b48-890e-2f952e412123", "metadata": { "id": "998611d1-911f-4b48-890e-2f952e412123" }, "outputs": [], "source": [ "def get_data(model):\n", " readme = get_model_readme(model)\n", " _, labels = get_model_labels(model)\n", " return model.modelId, labels, readme" ] }, { "cell_type": "markdown", "id": "Wp-wZQCqehcb", "metadata": { "id": "Wp-wZQCqehcb" }, "source": [ "Since this takes a little while we make a progress bar and do this using multiple threads" ] }, { "cell_type": "code", "execution_count": null, "id": "ki3UoGgBedzj", "metadata": { "id": "ki3UoGgBedzj" }, "outputs": [], "source": [ "from tqdm.auto import tqdm" ] }, { "cell_type": "code", "execution_count": null, "id": "3ff5f5c9-9c7a-4ace-b7bb-f85bca8b0051", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "228ce92c5f38428a95105d02358f781e", "557c1c17405e403da54b86d8b04b024e", "ad0337420f1f4671b875e87a15176452", "df0436126f5c4a08853c6be7ff939a3e", "3f3154328f74424c93e34dd89136ecf7", "05ae6cb62fb44d9e9f0d8104511d9c0e", "72b250bdd261446aa251e1b97c8b8162", "7e883b1d93b74e26b8aee6677df9c58d", "152207bbb68345d697cb66b40f02c8f3", "8485443f7a2e449bbf748278b971ec73", "55abd0d45c0b4b039520e4297dc46081" ] }, "id": "3ff5f5c9-9c7a-4ace-b7bb-f85bca8b0051", "outputId": "95fc39e4-d9a3-4d37-e795-7d568b11a2ab" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "228ce92c5f38428a95105d02358f781e", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/6850 [00:00\n", "
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modelIdlabelreadme
0distilbert-base-uncased-finetuned-sst-2-english[NEGATIVE, POSITIVE]---\\nlanguage: en\\nlicense: apache-2.0\\ndatase...
1cross-encoder/ms-marco-MiniLM-L-12-v2[LABEL_0]---\\nlicense: apache-2.0\\n---\\n# Cross-Encoder...
2cardiffnlp/twitter-xlm-roberta-base-sentiment[Negative, Neutral, Positive]---\\nlanguage: multilingual\\nwidget:\\n- text: ...
3facebook/bart-large-mnli[contradiction, entailment, neutral]---\\nlicense: mit\\nthumbnail: https://huggingf...
4ProsusAI/finbert[positive, negative, neutral]---\\nlanguage: \"en\"\\ntags:\\n- financial-sentim...
............
6845jinwooChoi/SKKU_AP_SA_KBT6[LABEL_0, LABEL_1, LABEL_2]Entry not found
6846jinwooChoi/SKKU_AP_SA_KBT7[LABEL_0, LABEL_1, LABEL_2]Entry not found
6847naem1023/electra-phrase-clause-classification-...NoneEntry not found
6848naem1023/electra-phrase-clause-classification-...None---\\nlicense: apache-2.0\\n---\\n
6849YYAH/Bert_Roman_Urdu[LABEL_0, LABEL_1, LABEL_2, LABEL_3]---\\nlicense: unknown\\n---\\n
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\n", " \n", " " ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "id": "wTMhrftrgsDh", "metadata": { "id": "wTMhrftrgsDh" }, "source": [ "You can see we now have a DataFrame containing the modelID, the model labels and the `README.md` for each model (where it exists). \n", "\n", "Since the `README.md` (the model card) is the obvious source of information about a model we'll start here. One question we may have is how long our the `README.md` is. Some models have very detailed model cards whilst others have very little information in the model card. We can get a bit of a sense of this by looking at the range of `README.md` lenghts:" ] }, { "cell_type": "code", "execution_count": null, "id": "cca840b3-8267-467b-afac-5ee65fe60826", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 166 }, "id": "cca840b3-8267-467b-afac-5ee65fe60826", "outputId": "649b8058-b23c-4a19-aadc-b86983dad67c" }, "outputs": [ { "data": { "text/html": [ "
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\n",
       "Dataset({\n",
       "    features: ['modelId', 'label', 'readme', 'readme_len'],\n",
       "    num_rows: 6850\n",
       "})\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[1;35mDataset\u001b[0m\u001b[1m(\u001b[0m\u001b[1m{\u001b[0m\n", " features: \u001b[1m[\u001b[0m\u001b[32m'modelId'\u001b[0m, \u001b[32m'label'\u001b[0m, \u001b[32m'readme'\u001b[0m, \u001b[32m'readme_len'\u001b[0m\u001b[1m]\u001b[0m,\n", " num_rows: \u001b[1;36m6850\u001b[0m\n", "\u001b[1m}\u001b[0m\u001b[1m)\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds = Dataset.from_pandas(df)\n", "ds" ] }, { "cell_type": "code", "execution_count": 9, "id": "LLbMIkpeoNlQ", "metadata": { "id": "LLbMIkpeoNlQ" }, "outputs": [], "source": [ "from huggingface_hub import notebook_login" ] }, { "cell_type": "code", "execution_count": 10, "id": "NNeymt5hoPGi", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 301, "referenced_widgets": [ "a2d1bdee854d47ddb32183ba141ab55d", "b2562460de0543a9be20e043c148a7c9", "3863ff3758874c60b2b0bc9f54f09146", "70d817adcee24f09a552c2331f3cdda1", "e1dcca41db4a40d897ccfb1eb7f2a010", "971e2965699d4b8e8e2fa0b05b3470fe", "8293a44938a24eeca7ce248747fe6591", "3018252f61f74baba9d2e466299b14c9", "985a4e94a6624db497fb549ff81f3c2e", "664ded2a090346638482d62daee73c67", "b428cdfadaf74e4b96485f5ce78edd69", "f5767bdfda10449da6b0676263878762", "d6219fdb579946aeaa9f5be0d6288f27", "6b9113db7e73426f81b33624fa9acb42" ] }, "id": "NNeymt5hoPGi", "outputId": "968e23b7-6fa3-4125-c942-f204d0c26600" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Login successful\n", "Your token has been saved to /root/.huggingface/token\n" ] } ], "source": [ "notebook_login()" ] }, { "cell_type": "code", "execution_count": null, "id": "CH0d6FiJoFJi", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 106, "referenced_widgets": [ "28cc772b519d4347985897d4903dc19a", "d47a67acfc694b758283f8fae594de27", "d4c6d4a913934f77849324ece6ef59a2", "3609d0dac2c94e1ea6fce14a24743b06", "e9bb74009b3f4cde9aea41628994c59d", "3f2007f0e2ab440c9977bbad877ed1b9", "f3a113d77ef34ec9b16a507ff5a22086", "875fb41ac3274893b1a02098bc67e831", "3ddbc8b058884ec5bd5c2c0d67ce1d99", "643b5a68217146b484a666dbc7ee9745", "bb49a2f1f29447c18c655bad111193fb" ] }, "id": "CH0d6FiJoFJi", "outputId": "df5b9707-6ada-4115-92df-9ed7c3b01512" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "28cc772b519d4347985897d4903dc19a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Pushing dataset shards to the dataset hub: 0%| | 0/1 [00:00100)" ] }, { "cell_type": "markdown", "id": "ddca70ce-2351-4e68-89a3-3d547fd24eb1", "metadata": { "id": "ddca70ce-2351-4e68-89a3-3d547fd24eb1" }, "source": [ "We now create embeddings for the `readme` column and store this in a new `embedding` column " ] }, { "cell_type": "code", "execution_count": 16, "id": "WH4bKP9Tb0UG", "metadata": { "id": "WH4bKP9Tb0UG" }, "outputs": [], "source": [ "def encode_readme(readme):\n", " return model.encode(readme,device='cuda')" ] }, { "cell_type": "code", "execution_count": 17, "id": "b47352c2-3fa7-4e9e-bcbe-df7ebbbfcf30", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "370b4cd2300449b48325c49182ada640", "a7c406fd736a4565ad7e0ba60be99a20", "6d06ce56b55944cba9aaf28bd3b3e508", "650c946a6dd04a1dad4a3ffdcbc7516c", "a114f7f350704e85b86c75117e5d9702", "8be00e4b45bf47ab825b99f70fe7cf30", "26626c55e36248829c67b10de605bea9", "3fdbf86e812c4142b49ff80dbcb59057", "a360fc81c930455f94d8d0dbebf1fff9", "33a54ceae027439fbb283cda95a39719", "b98bfe7ccbb9496fb8c172c1cf5a1f18" ] }, "id": "b47352c2-3fa7-4e9e-bcbe-df7ebbbfcf30", "outputId": "cfcf00ff-c9da-4569-9783-2639240f250f" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "370b4cd2300449b48325c49182ada640", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/206 [00:00\n", "Dataset({\n", " features: ['modelId', 'label', 'readme', 'readme_len', 'embedding'],\n", " num_rows: 3284\n", "})\n", "\n" ], "text/plain": [ "\n", "\u001b[1;35mDataset\u001b[0m\u001b[1m(\u001b[0m\u001b[1m{\u001b[0m\n", " features: \u001b[1m[\u001b[0m\u001b[32m'modelId'\u001b[0m, \u001b[32m'label'\u001b[0m, \u001b[32m'readme'\u001b[0m, \u001b[32m'readme_len'\u001b[0m, \u001b[32m'embedding'\u001b[0m\u001b[1m]\u001b[0m,\n", " num_rows: \u001b[1;36m3284\u001b[0m\n", "\u001b[1m}\u001b[0m\u001b[1m)\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds_with_embeddings" ] }, { "cell_type": "markdown", "id": "42442e08-0292-4fb0-ab70-4d7ab0195e9f", "metadata": { "id": "42442e08-0292-4fb0-ab70-4d7ab0195e9f" }, "source": [ "We can now use the `add_fais_index` to create an index which allows us to efficiently query these embeddings " ] }, { "cell_type": "code", "execution_count": 19, "id": "19424e6a-ef3d-4c8e-bf99-fbdedb4fdc65", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 115, "referenced_widgets": [ "ad63f7dfb31d4c3fb4c0ea73cab19917", "4a142d8f316a49aca701850ed01e3b79", "68c5bb5404724775885eac217430af35", "14004f118a1641528f9427d3c01d841e", "d473f6ab480542f4a1cc5cb622a54ad7", "be0ef18544614ed1b9da9f8931bbe6b8", "59ac58d5680147baa98cb9325e9f8087", "c5d45d8d6038433da4c538fc630da082", "c853942632cf4aaca95737b6c3f6cfba", "012e04123ad9478aace2cdbf396d24f2", "97a7930f16fc44eaa2ac91d3f24974fc" ] }, "id": "19424e6a-ef3d-4c8e-bf99-fbdedb4fdc65", "outputId": "2ee9c92d-f6b3-4b4d-d953-43497c538d03" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ad63f7dfb31d4c3fb4c0ea73cab19917", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/4 [00:00\n", "Dataset({\n", " features: ['modelId', 'label', 'readme', 'readme_len', 'embedding'],\n", " num_rows: 3284\n", "})\n", "\n" ], "text/plain": [ "\n", "\u001b[1;35mDataset\u001b[0m\u001b[1m(\u001b[0m\u001b[1m{\u001b[0m\n", " features: \u001b[1m[\u001b[0m\u001b[32m'modelId'\u001b[0m, \u001b[32m'label'\u001b[0m, \u001b[32m'readme'\u001b[0m, \u001b[32m'readme_len'\u001b[0m, \u001b[32m'embedding'\u001b[0m\u001b[1m]\u001b[0m,\n", " num_rows: \u001b[1;36m3284\u001b[0m\n", "\u001b[1m}\u001b[0m\u001b[1m)\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds_with_embeddings.add_faiss_index(column='embedding')" ] }, { "cell_type": "markdown", "id": "26d7bcfd-91b5-4f69-bc14-319db6fe8ee7", "metadata": { "id": "26d7bcfd-91b5-4f69-bc14-319db6fe8ee7" }, "source": [ "### Similar models\n", "\n", "To start, we'll take a readme for a model and see how well the model performs on finding similar models." ] }, { "cell_type": "code", "execution_count": 31, "id": "eefa5c39-54aa-4367-909e-16837b506c98", "metadata": { "id": "eefa5c39-54aa-4367-909e-16837b506c98" }, "outputs": [], "source": [ "query_readme = ds_with_embeddings[35]['readme']" ] }, { "cell_type": "code", "execution_count": 32, "id": "9c6e6f85-d27d-46ae-85fb-96e1cb137a23", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "9c6e6f85-d27d-46ae-85fb-96e1cb137a23", "outputId": "542302b8-dbb5-4046-ec0f-e713e60d8c29" }, "outputs": [ { "data": { "text/html": [ "
# Twitter-roBERTa-base for Irony Detection\n",
       "\n",
       "This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark.\n",
       "\n",
       "- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). \n",
       "- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).\n",
       "\n",
       "## Example of classification\n",
       "\n",
       "```python\n",
       "from transformers import AutoModelForSequenceClassification\n",
       "from transformers import TFAutoModelForSequenceClassification\n",
       "from transformers import AutoTokenizer\n",
       "import numpy as np\n",
       "from scipy.special import softmax\n",
       "import csv\n",
       "import urllib.request\n",
       "\n",
       "# Preprocess text (username and link placeholders)\n",
       "def preprocess(text):\n",
       "    new_text = [\n",
       "    ]\n",
       "    for t in text.split(\" \"):\n",
       "        t = '@user' if t.startswith('@') and len(t) > 1 else t\n",
       "        t = 'http' if t.startswith('http') else t\n",
       "        new_text.append(t)\n",
       "    return \" \".join(new_text)\n",
       "\n",
       "# Tasks:\n",
       "# emoji, emotion, hate, irony, offensive, sentiment\n",
       "# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary\n",
       "\n",
       "task='irony'\n",
       "MODEL = f\"cardiffnlp/twitter-roberta-base-{task}\"\n",
       "\n",
       "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n",
       "\n",
       "# download label mapping\n",
       "labels=[]\n",
       "mapping_link = f\"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt\"\n",
       "with urllib.request.urlopen(mapping_link) as f:\n",
       "    html = f.read().decode('utf-8').split(\"\\n\")\n",
       "    csvreader = csv.reader(html, delimiter='\\t')\n",
       "labels = [row[1] for row in csvreader if len(row) > 1]\n",
       "\n",
       "# PT\n",
       "model = AutoModelForSequenceClassification.from_pretrained(MODEL)\n",
       "model.save_pretrained(MODEL)\n",
       "\n",
       "text = \"Great, it broke the first day...\"\n",
       "text = preprocess(text)\n",
       "encoded_input = tokenizer(text, return_tensors='pt')\n",
       "output = model(**encoded_input)\n",
       "scores = output[0][0].detach().numpy()\n",
       "scores = softmax(scores)\n",
       "\n",
       "# # TF\n",
       "# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)\n",
       "# model.save_pretrained(MODEL)\n",
       "\n",
       "# text = \"Great, it broke the first day...\"\n",
       "# encoded_input = tokenizer(text, return_tensors='tf')\n",
       "# output = model(encoded_input)\n",
       "# scores = output[0][0].numpy()\n",
       "# scores = softmax(scores)\n",
       "\n",
       "ranking = np.argsort(scores)\n",
       "ranking = ranking[::-1]\n",
       "for i in range(scores.shape[0]):\n",
       "    l = labels[ranking]\n",
       "    s = scores[ranking]\n",
       "    print(f\"{i+1}) {l} {np.round(float(s), 4)}\")\n",
       "\n",
       "```\n",
       "\n",
       "Output: \n",
       "\n",
       "```\n",
       "1) irony 0.914\n",
       "2) non_irony 0.086\n",
       "```\n",
       "\n",
       "
\n" ], "text/plain": [ "# Twitter-roBERTa-base for Irony Detection\n", "\n", "This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark.\n", "\n", "- Paper: \u001b[1m[\u001b[0m_TweetEval_ benchmark \u001b[1m(\u001b[0mFindings of EMNLP \u001b[1;36m2020\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\u001b[1m(\u001b[0m\u001b[4;94mhttps://arxiv.org/pdf/2010.12421.pdf\u001b[0m\u001b[4;94m)\u001b[0m\u001b[4;94m.\u001b[0m \n", "- Git Repo: \u001b[1m[\u001b[0mTweeteval official repository\u001b[1m]\u001b[0m\u001b[1m(\u001b[0m\u001b[4;94mhttps://github.com/cardiffnlp/tweeteval\u001b[0m\u001b[4;94m)\u001b[0m\u001b[4;94m.\u001b[0m\n", "\n", "## Example of classification\n", "\n", "```python\n", "from transformers import AutoModelForSequenceClassification\n", "from transformers import TFAutoModelForSequenceClassification\n", "from transformers import AutoTokenizer\n", "import numpy as np\n", "from scipy.special import softmax\n", "import csv\n", "import urllib.request\n", "\n", "# Preprocess text \u001b[1m(\u001b[0musername and link placeholders\u001b[1m)\u001b[0m\n", "def \u001b[1;35mpreprocess\u001b[0m\u001b[1m(\u001b[0mtext\u001b[1m)\u001b[0m:\n", " new_text = \u001b[1m[\u001b[0m\n", " \u001b[1m]\u001b[0m\n", " for t in \u001b[1;35mtext.split\u001b[0m\u001b[1m(\u001b[0m\u001b[32m\" \"\u001b[0m\u001b[1m)\u001b[0m:\n", " t = \u001b[32m'@user'\u001b[0m if \u001b[1;35mt.startswith\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'@'\u001b[0m\u001b[1m)\u001b[0m and \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mt\u001b[1m)\u001b[0m > \u001b[1;36m1\u001b[0m else t\n", " t = \u001b[32m'http'\u001b[0m if \u001b[1;35mt.startswith\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'http'\u001b[0m\u001b[1m)\u001b[0m else t\n", " \u001b[1;35mnew_text.append\u001b[0m\u001b[1m(\u001b[0mt\u001b[1m)\u001b[0m\n", " return \u001b[32m\" \"\u001b[0m\u001b[1;35m.join\u001b[0m\u001b[1m(\u001b[0mnew_text\u001b[1m)\u001b[0m\n", "\n", "# Tasks:\n", "# emoji, emotion, hate, irony, offensive, sentiment\n", "# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary\n", "\n", "\u001b[33mtask\u001b[0m=\u001b[32m'irony'\u001b[0m\n", "MODEL = f\"cardiffnlp/twitter-roberta-base-\u001b[1m{\u001b[0mtask\u001b[1m}\u001b[0m\"\n", "\n", "tokenizer = \u001b[1;35mAutoTokenizer.from_pretrained\u001b[0m\u001b[1m(\u001b[0mMODEL\u001b[1m)\u001b[0m\n", "\n", "# download label mapping\n", "\u001b[33mlabels\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n", "mapping_link = f\"\u001b[4;94mhttps://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/\u001b[0m\u001b[1m{\u001b[0mtask\u001b[1m}\u001b[0m\u001b[35m/\u001b[0m\u001b[95mmapping.txt\u001b[0m\"\n", "with \u001b[1;35murllib.request.urlopen\u001b[0m\u001b[1m(\u001b[0mmapping_link\u001b[1m)\u001b[0m as f:\n", " html = \u001b[1;35mf.read\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1;35m.decode\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'utf-8'\u001b[0m\u001b[1m)\u001b[0m\u001b[1;35m.split\u001b[0m\u001b[1m(\u001b[0m\u001b[32m\"\\n\"\u001b[0m\u001b[1m)\u001b[0m\n", " csvreader = \u001b[1;35mcsv.reader\u001b[0m\u001b[1m(\u001b[0mhtml, \u001b[33mdelimiter\u001b[0m=\u001b[32m'\\t'\u001b[0m\u001b[1m)\u001b[0m\n", "labels = \u001b[1m[\u001b[0mrow\u001b[1m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1m]\u001b[0m for row in csvreader if \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mrow\u001b[1m)\u001b[0m > \u001b[1;36m1\u001b[0m\u001b[1m]\u001b[0m\n", "\n", "# PT\n", "model = \u001b[1;35mAutoModelForSequenceClassification.from_pretrained\u001b[0m\u001b[1m(\u001b[0mMODEL\u001b[1m)\u001b[0m\n", "\u001b[1;35mmodel.save_pretrained\u001b[0m\u001b[1m(\u001b[0mMODEL\u001b[1m)\u001b[0m\n", "\n", "text = \u001b[32m\"Great, it broke the first day...\"\u001b[0m\n", "text = \u001b[1;35mpreprocess\u001b[0m\u001b[1m(\u001b[0mtext\u001b[1m)\u001b[0m\n", "encoded_input = \u001b[1;35mtokenizer\u001b[0m\u001b[1m(\u001b[0mtext, \u001b[33mreturn_tensors\u001b[0m=\u001b[32m'pt'\u001b[0m\u001b[1m)\u001b[0m\n", "output = \u001b[1;35mmodel\u001b[0m\u001b[1m(\u001b[0m**encoded_input\u001b[1m)\u001b[0m\n", "scores = output\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1;35m.detach\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1;35m.numpy\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", "scores = \u001b[1;35msoftmax\u001b[0m\u001b[1m(\u001b[0mscores\u001b[1m)\u001b[0m\n", "\n", "# # TF\n", "# model = \u001b[1;35mTFAutoModelForSequenceClassification.from_pretrained\u001b[0m\u001b[1m(\u001b[0mMODEL\u001b[1m)\u001b[0m\n", "# \u001b[1;35mmodel.save_pretrained\u001b[0m\u001b[1m(\u001b[0mMODEL\u001b[1m)\u001b[0m\n", "\n", "# text = \u001b[32m\"Great, it broke the first day...\"\u001b[0m\n", "# encoded_input = \u001b[1;35mtokenizer\u001b[0m\u001b[1m(\u001b[0mtext, \u001b[33mreturn_tensors\u001b[0m=\u001b[32m'tf'\u001b[0m\u001b[1m)\u001b[0m\n", "# output = \u001b[1;35mmodel\u001b[0m\u001b[1m(\u001b[0mencoded_input\u001b[1m)\u001b[0m\n", "# scores = output\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1;35m.numpy\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", "# scores = \u001b[1;35msoftmax\u001b[0m\u001b[1m(\u001b[0mscores\u001b[1m)\u001b[0m\n", "\n", "ranking = \u001b[1;35mnp.argsort\u001b[0m\u001b[1m(\u001b[0mscores\u001b[1m)\u001b[0m\n", "ranking = ranking\u001b[1m[\u001b[0m::\u001b[1;36m-1\u001b[0m\u001b[1m]\u001b[0m\n", "for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mscores.shape\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m:\n", " l = labels\u001b[1m[\u001b[0mranking\u001b[1;3m]\u001b[0m\n", "\u001b[3m s = scores\u001b[0m\u001b[1;3m[\u001b[0m\u001b[3mranking\u001b[0m\u001b[1;3m]\u001b[0m\n", "\u001b[3m \u001b[0m\u001b[1;3;35mprint\u001b[0m\u001b[1;3m(\u001b[0m\u001b[3mf\"\u001b[0m\u001b[1;3m{\u001b[0m\u001b[3mi+\u001b[0m\u001b[1;3;36m1\u001b[0m\u001b[1;3m}\u001b[0m\u001b[1;3m)\u001b[0m\u001b[3m \u001b[0m\u001b[1;3m{\u001b[0m\u001b[3ml\u001b[0m\u001b[1;3m}\u001b[0m\u001b[3m \u001b[0m\u001b[1;3m{\u001b[0m\u001b[1;3;35mnp.round\u001b[0m\u001b[1;3m(\u001b[0m\u001b[1;3;35mfloat\u001b[0m\u001b[1;3m(\u001b[0m\u001b[3ms\u001b[0m\u001b[1;3m)\u001b[0m\u001b[3m, \u001b[0m\u001b[1;3;36m4\u001b[0m\u001b[1;3m)\u001b[0m\u001b[1;3m}\u001b[0m\u001b[3m\"\u001b[0m\u001b[1;3m)\u001b[0m\n", "\n", "\u001b[3m```\u001b[0m\n", "\n", "\u001b[3mOutput: \u001b[0m\n", "\n", "\u001b[3m```\u001b[0m\n", "\u001b[1;3;36m1\u001b[0m\u001b[1;3m)\u001b[0m\u001b[3m irony \u001b[0m\u001b[1;3;36m0.914\u001b[0m\n", "\u001b[1;3;36m2\u001b[0m\u001b[1;3m)\u001b[0m\u001b[3m non_irony \u001b[0m\u001b[1;3;36m0.086\u001b[0m\n", "\u001b[3m```\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(query_readme)" ] }, { "cell_type": "markdown", "id": "0698667a-09cd-461e-98c6-92c74757eedf", "metadata": { "id": "0698667a-09cd-461e-98c6-92c74757eedf" }, "source": [ "We pass this README into the model we used to create our embedding. This creates a query embedding for this README." ] }, { "cell_type": "code", "execution_count": 33, "id": "58c521ae-952e-4fa8-82bf-8d5d81627f5b", "metadata": { "id": "58c521ae-952e-4fa8-82bf-8d5d81627f5b" }, "outputs": [], "source": [ "q = model.encode(query_readme)" ] }, { "cell_type": "markdown", "id": "36db4903-0104-40e9-bba8-4b1683fe79a3", "metadata": { "id": "36db4903-0104-40e9-bba8-4b1683fe79a3" }, "source": [ "We can use `get_nearest_examples` to look for the most similar results to this query. " ] }, { "cell_type": "code", "execution_count": 34, "id": "5ba977d4-7163-4edf-8dfe-cd789303918a", "metadata": { "id": "5ba977d4-7163-4edf-8dfe-cd789303918a" }, "outputs": [], "source": [ "scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embedding', q, k=10)\n" ] }, { "cell_type": "markdown", "id": "fe54c158-639f-4d93-9dbd-11337c1453b1", "metadata": { "id": "fe54c158-639f-4d93-9dbd-11337c1453b1" }, "source": [ "Let's take a look at the first result " ] }, { "cell_type": "code", "execution_count": 38, "id": "R2ii4efY6KQv", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "R2ii4efY6KQv", "outputId": "da27ad73-add3-4a6c-c3af-0a3c59363dbf" }, "outputs": [ { "data": { "text/html": [ "
cardiffnlp/twitter-roberta-base-irony\n",
       "
\n" ], "text/plain": [ "cardiffnlp/twitter-roberta-base-irony\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " print(retrieved_examples['modelId'][0])" ] }, { "cell_type": "code", "execution_count": 39, "id": "44bad884-80c6-4ee6-af83-10e0ecf91082", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "44bad884-80c6-4ee6-af83-10e0ecf91082", "outputId": "b6c18969-17bd-4248-a3a9-bd19f9297b73" }, "outputs": [ { "data": { "text/html": [ "
# Twitter-roBERTa-base for Irony Detection\n",
       "\n",
       "This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark.\n",
       "\n",
       "- Paper: [_TweetEval_ benchmark (Findings of EMNLP 2020)](https://arxiv.org/pdf/2010.12421.pdf). \n",
       "- Git Repo: [Tweeteval official repository](https://github.com/cardiffnlp/tweeteval).\n",
       "\n",
       "## Example of classification\n",
       "\n",
       "```python\n",
       "from transformers import AutoModelForSequenceClassification\n",
       "from transformers import TFAutoModelForSequenceClassification\n",
       "from transformers import AutoTokenizer\n",
       "import numpy as np\n",
       "from scipy.special import softmax\n",
       "import csv\n",
       "import urllib.request\n",
       "\n",
       "# Preprocess text (username and link placeholders)\n",
       "def preprocess(text):\n",
       "    new_text = [\n",
       "    ]\n",
       "    for t in text.split(\" \"):\n",
       "        t = '@user' if t.startswith('@') and len(t) > 1 else t\n",
       "        t = 'http' if t.startswith('http') else t\n",
       "        new_text.append(t)\n",
       "    return \" \".join(new_text)\n",
       "\n",
       "# Tasks:\n",
       "# emoji, emotion, hate, irony, offensive, sentiment\n",
       "# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary\n",
       "\n",
       "task='irony'\n",
       "MODEL = f\"cardiffnlp/twitter-roberta-base-{task}\"\n",
       "\n",
       "tokenizer = AutoTokenizer.from_pretrained(MODEL)\n",
       "\n",
       "# download label mapping\n",
       "labels=[]\n",
       "mapping_link = f\"https://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/{task}/mapping.txt\"\n",
       "with urllib.request.urlopen(mapping_link) as f:\n",
       "    html = f.read().decode('utf-8').split(\"\\n\")\n",
       "    csvreader = csv.reader(html, delimiter='\\t')\n",
       "labels = [row[1] for row in csvreader if len(row) > 1]\n",
       "\n",
       "# PT\n",
       "model = AutoModelForSequenceClassification.from_pretrained(MODEL)\n",
       "model.save_pretrained(MODEL)\n",
       "\n",
       "text = \"Great, it broke the first day...\"\n",
       "text = preprocess(text)\n",
       "encoded_input = tokenizer(text, return_tensors='pt')\n",
       "output = model(**encoded_input)\n",
       "scores = output[0][0].detach().numpy()\n",
       "scores = softmax(scores)\n",
       "\n",
       "# # TF\n",
       "# model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)\n",
       "# model.save_pretrained(MODEL)\n",
       "\n",
       "# text = \"Great, it broke the first day...\"\n",
       "# encoded_input = tokenizer(text, return_tensors='tf')\n",
       "# output = model(encoded_input)\n",
       "# scores = output[0][0].numpy()\n",
       "# scores = softmax(scores)\n",
       "\n",
       "ranking = np.argsort(scores)\n",
       "ranking = ranking[::-1]\n",
       "for i in range(scores.shape[0]):\n",
       "    l = labels[ranking]\n",
       "    s = scores[ranking]\n",
       "    print(f\"{i+1}) {l} {np.round(float(s), 4)}\")\n",
       "\n",
       "```\n",
       "\n",
       "Output: \n",
       "\n",
       "```\n",
       "1) irony 0.914\n",
       "2) non_irony 0.086\n",
       "```\n",
       "\n",
       "
\n" ], "text/plain": [ "# Twitter-roBERTa-base for Irony Detection\n", "\n", "This is a roBERTa-base model trained on ~58M tweets and finetuned for irony detection with the TweetEval benchmark.\n", "\n", "- Paper: \u001b[1m[\u001b[0m_TweetEval_ benchmark \u001b[1m(\u001b[0mFindings of EMNLP \u001b[1;36m2020\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\u001b[1m(\u001b[0m\u001b[4;94mhttps://arxiv.org/pdf/2010.12421.pdf\u001b[0m\u001b[4;94m)\u001b[0m\u001b[4;94m.\u001b[0m \n", "- Git Repo: \u001b[1m[\u001b[0mTweeteval official repository\u001b[1m]\u001b[0m\u001b[1m(\u001b[0m\u001b[4;94mhttps://github.com/cardiffnlp/tweeteval\u001b[0m\u001b[4;94m)\u001b[0m\u001b[4;94m.\u001b[0m\n", "\n", "## Example of classification\n", "\n", "```python\n", "from transformers import AutoModelForSequenceClassification\n", "from transformers import TFAutoModelForSequenceClassification\n", "from transformers import AutoTokenizer\n", "import numpy as np\n", "from scipy.special import softmax\n", "import csv\n", "import urllib.request\n", "\n", "# Preprocess text \u001b[1m(\u001b[0musername and link placeholders\u001b[1m)\u001b[0m\n", "def \u001b[1;35mpreprocess\u001b[0m\u001b[1m(\u001b[0mtext\u001b[1m)\u001b[0m:\n", " new_text = \u001b[1m[\u001b[0m\n", " \u001b[1m]\u001b[0m\n", " for t in \u001b[1;35mtext.split\u001b[0m\u001b[1m(\u001b[0m\u001b[32m\" \"\u001b[0m\u001b[1m)\u001b[0m:\n", " t = \u001b[32m'@user'\u001b[0m if \u001b[1;35mt.startswith\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'@'\u001b[0m\u001b[1m)\u001b[0m and \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mt\u001b[1m)\u001b[0m > \u001b[1;36m1\u001b[0m else t\n", " t = \u001b[32m'http'\u001b[0m if \u001b[1;35mt.startswith\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'http'\u001b[0m\u001b[1m)\u001b[0m else t\n", " \u001b[1;35mnew_text.append\u001b[0m\u001b[1m(\u001b[0mt\u001b[1m)\u001b[0m\n", " return \u001b[32m\" \"\u001b[0m\u001b[1;35m.join\u001b[0m\u001b[1m(\u001b[0mnew_text\u001b[1m)\u001b[0m\n", "\n", "# Tasks:\n", "# emoji, emotion, hate, irony, offensive, sentiment\n", "# stance/abortion, stance/atheism, stance/climate, stance/feminist, stance/hillary\n", "\n", "\u001b[33mtask\u001b[0m=\u001b[32m'irony'\u001b[0m\n", "MODEL = f\"cardiffnlp/twitter-roberta-base-\u001b[1m{\u001b[0mtask\u001b[1m}\u001b[0m\"\n", "\n", "tokenizer = \u001b[1;35mAutoTokenizer.from_pretrained\u001b[0m\u001b[1m(\u001b[0mMODEL\u001b[1m)\u001b[0m\n", "\n", "# download label mapping\n", "\u001b[33mlabels\u001b[0m=\u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n", "mapping_link = f\"\u001b[4;94mhttps://raw.githubusercontent.com/cardiffnlp/tweeteval/main/datasets/\u001b[0m\u001b[1m{\u001b[0mtask\u001b[1m}\u001b[0m\u001b[35m/\u001b[0m\u001b[95mmapping.txt\u001b[0m\"\n", "with \u001b[1;35murllib.request.urlopen\u001b[0m\u001b[1m(\u001b[0mmapping_link\u001b[1m)\u001b[0m as f:\n", " html = \u001b[1;35mf.read\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1;35m.decode\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'utf-8'\u001b[0m\u001b[1m)\u001b[0m\u001b[1;35m.split\u001b[0m\u001b[1m(\u001b[0m\u001b[32m\"\\n\"\u001b[0m\u001b[1m)\u001b[0m\n", " csvreader = \u001b[1;35mcsv.reader\u001b[0m\u001b[1m(\u001b[0mhtml, \u001b[33mdelimiter\u001b[0m=\u001b[32m'\\t'\u001b[0m\u001b[1m)\u001b[0m\n", "labels = \u001b[1m[\u001b[0mrow\u001b[1m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1m]\u001b[0m for row in csvreader if \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mrow\u001b[1m)\u001b[0m > \u001b[1;36m1\u001b[0m\u001b[1m]\u001b[0m\n", "\n", "# PT\n", "model = \u001b[1;35mAutoModelForSequenceClassification.from_pretrained\u001b[0m\u001b[1m(\u001b[0mMODEL\u001b[1m)\u001b[0m\n", "\u001b[1;35mmodel.save_pretrained\u001b[0m\u001b[1m(\u001b[0mMODEL\u001b[1m)\u001b[0m\n", "\n", "text = \u001b[32m\"Great, it broke the first day...\"\u001b[0m\n", "text = \u001b[1;35mpreprocess\u001b[0m\u001b[1m(\u001b[0mtext\u001b[1m)\u001b[0m\n", "encoded_input = \u001b[1;35mtokenizer\u001b[0m\u001b[1m(\u001b[0mtext, \u001b[33mreturn_tensors\u001b[0m=\u001b[32m'pt'\u001b[0m\u001b[1m)\u001b[0m\n", "output = \u001b[1;35mmodel\u001b[0m\u001b[1m(\u001b[0m**encoded_input\u001b[1m)\u001b[0m\n", "scores = output\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1;35m.detach\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1;35m.numpy\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", "scores = \u001b[1;35msoftmax\u001b[0m\u001b[1m(\u001b[0mscores\u001b[1m)\u001b[0m\n", "\n", "# # TF\n", "# model = \u001b[1;35mTFAutoModelForSequenceClassification.from_pretrained\u001b[0m\u001b[1m(\u001b[0mMODEL\u001b[1m)\u001b[0m\n", "# \u001b[1;35mmodel.save_pretrained\u001b[0m\u001b[1m(\u001b[0mMODEL\u001b[1m)\u001b[0m\n", "\n", "# text = \u001b[32m\"Great, it broke the first day...\"\u001b[0m\n", "# encoded_input = \u001b[1;35mtokenizer\u001b[0m\u001b[1m(\u001b[0mtext, \u001b[33mreturn_tensors\u001b[0m=\u001b[32m'tf'\u001b[0m\u001b[1m)\u001b[0m\n", "# output = \u001b[1;35mmodel\u001b[0m\u001b[1m(\u001b[0mencoded_input\u001b[1m)\u001b[0m\n", "# scores = output\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1;35m.numpy\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n", "# scores = \u001b[1;35msoftmax\u001b[0m\u001b[1m(\u001b[0mscores\u001b[1m)\u001b[0m\n", "\n", "ranking = \u001b[1;35mnp.argsort\u001b[0m\u001b[1m(\u001b[0mscores\u001b[1m)\u001b[0m\n", "ranking = ranking\u001b[1m[\u001b[0m::\u001b[1;36m-1\u001b[0m\u001b[1m]\u001b[0m\n", "for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mscores.shape\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m:\n", " l = labels\u001b[1m[\u001b[0mranking\u001b[1;3m]\u001b[0m\n", "\u001b[3m s = scores\u001b[0m\u001b[1;3m[\u001b[0m\u001b[3mranking\u001b[0m\u001b[1;3m]\u001b[0m\n", "\u001b[3m \u001b[0m\u001b[1;3;35mprint\u001b[0m\u001b[1;3m(\u001b[0m\u001b[3mf\"\u001b[0m\u001b[1;3m{\u001b[0m\u001b[3mi+\u001b[0m\u001b[1;3;36m1\u001b[0m\u001b[1;3m}\u001b[0m\u001b[1;3m)\u001b[0m\u001b[3m \u001b[0m\u001b[1;3m{\u001b[0m\u001b[3ml\u001b[0m\u001b[1;3m}\u001b[0m\u001b[3m \u001b[0m\u001b[1;3m{\u001b[0m\u001b[1;3;35mnp.round\u001b[0m\u001b[1;3m(\u001b[0m\u001b[1;3;35mfloat\u001b[0m\u001b[1;3m(\u001b[0m\u001b[3ms\u001b[0m\u001b[1;3m)\u001b[0m\u001b[3m, \u001b[0m\u001b[1;3;36m4\u001b[0m\u001b[1;3m)\u001b[0m\u001b[1;3m}\u001b[0m\u001b[3m\"\u001b[0m\u001b[1;3m)\u001b[0m\n", "\n", "\u001b[3m```\u001b[0m\n", "\n", "\u001b[3mOutput: \u001b[0m\n", "\n", "\u001b[3m```\u001b[0m\n", "\u001b[1;3;36m1\u001b[0m\u001b[1;3m)\u001b[0m\u001b[3m irony \u001b[0m\u001b[1;3;36m0.914\u001b[0m\n", "\u001b[1;3;36m2\u001b[0m\u001b[1;3m)\u001b[0m\u001b[3m non_irony \u001b[0m\u001b[1;3;36m0.086\u001b[0m\n", "\u001b[3m```\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(retrieved_examples[\"readme\"][0])" ] }, { "cell_type": "markdown", "id": "dce88a45-a7b7-401e-8678-86b490e7088a", "metadata": { "id": "dce88a45-a7b7-401e-8678-86b490e7088a" }, "source": [ "and a lower similarity result" ] }, { "cell_type": "code", "execution_count": 42, "id": "f956bd40-1ead-4c7f-80a9-1e7f95c33a27", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 850 }, "id": "f956bd40-1ead-4c7f-80a9-1e7f95c33a27", "outputId": "29added4-9de2-49bf-c5ea-0785c5a0ff8a" }, "outputs": [ { "data": { "text/html": [ "
---\n",
       "language: \"en\"\n",
       "tags:\n",
       "- roberta\n",
       "- sentiment\n",
       "- twitter\n",
       "\n",
       "widget:\n",
       "- text: \"Oh no. This is bad..\"\n",
       "- text: \"To be or not to be.\"\n",
       "- text: \"Oh Happy Day\"\n",
       "\n",
       "---\n",
       "\n",
       "This RoBERTa-based model can classify the sentiment of English language text in 3 classes:\n",
       "\n",
       "- positive 😀\n",
       "- neutral 😐\n",
       "- negative 🙁\n",
       "\n",
       "The model was fine-tuned on 5,304 manually annotated social media posts. \n",
       "The hold-out accuracy is 86.1%. \n",
       "For details on the training approach see Web Appendix F in Hartmann et al. (2021). \n",
       "\n",
       "# Application\n",
       "```python\n",
       "from transformers import pipeline\n",
       "classifier = pipeline(\"text-classification\", model=\"j-hartmann/sentiment-roberta-large-english-3-classes\", \n",
       "return_all_scores=True)\n",
       "classifier(\"This is so nice!\")\n",
       "```\n",
       "\n",
       "```python\n",
       "Output:\n",
       "[[{'label': 'negative', 'score': 0.00016451838018838316},\n",
       "  {'label': 'neutral', 'score': 0.000174045650055632},\n",
       "  {'label': 'positive', 'score': 0.9996614456176758}]]\n",
       "```\n",
       "\n",
       "# Reference\n",
       "Please cite (https://journals.sagepub.com/doi/full/10.1177/00222437211037258) when you use our model. Feel free to \n",
       "reach out to (mailto:j.p.hartmann@rug.nl) with any questions or feedback you may have.\n",
       "```\n",
       "@article{hartmann2021,\n",
       "  title={The Power of Brand Selfies},\n",
       "  author={Hartmann, Jochen and Heitmann, Mark and Schamp, Christina and Netzer, Oded},\n",
       "  journal={Journal of Marketing Research}\n",
       "  year={2021}\n",
       "}\n",
       "```\n",
       "
\n" ], "text/plain": [ "---\n", "language: \u001b[32m\"en\"\u001b[0m\n", "tags:\n", "- roberta\n", "- sentiment\n", "- twitter\n", "\n", "widget:\n", "- text: \u001b[32m\"Oh no. This is bad..\"\u001b[0m\n", "- text: \u001b[32m\"To be or not to be.\"\u001b[0m\n", "- text: \u001b[32m\"Oh Happy Day\"\u001b[0m\n", "\n", "---\n", "\n", "This RoBERTa-based model can classify the sentiment of English language text in \u001b[1;36m3\u001b[0m classes:\n", "\n", "- positive 😀\n", "- neutral 😐\n", "- negative 🙁\n", "\n", "The model was fine-tuned on \u001b[1;36m5\u001b[0m,\u001b[1;36m304\u001b[0m manually annotated social media posts. \n", "The hold-out accuracy is \u001b[1;36m86.1\u001b[0m%. \n", "For details on the training approach see Web Appendix F in Hartmann et al. \u001b[1m(\u001b[0m\u001b[1;36m2021\u001b[0m\u001b[1m)\u001b[0m. \n", "\n", "# Application\n", "```python\n", "from transformers import pipeline\n", "classifier = \u001b[1;35mpipeline\u001b[0m\u001b[1m(\u001b[0m\u001b[32m\"text-classification\"\u001b[0m, \u001b[33mmodel\u001b[0m=\u001b[32m\"j\u001b[0m\u001b[32m-hartmann/sentiment-roberta-large-english-3-classes\"\u001b[0m, \n", "\u001b[33mreturn_all_scores\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m\n", "\u001b[1;35mclassifier\u001b[0m\u001b[1m(\u001b[0m\u001b[32m\"This is so nice!\"\u001b[0m\u001b[1m)\u001b[0m\n", "```\n", "\n", "```python\n", "Output:\n", "\u001b[1m[\u001b[0m\u001b[1m[\u001b[0m\u001b[1m{\u001b[0m\u001b[32m'label'\u001b[0m: \u001b[32m'negative'\u001b[0m, \u001b[32m'score'\u001b[0m: \u001b[1;36m0.00016451838018838316\u001b[0m\u001b[1m}\u001b[0m,\n", " \u001b[1m{\u001b[0m\u001b[32m'label'\u001b[0m: \u001b[32m'neutral'\u001b[0m, \u001b[32m'score'\u001b[0m: \u001b[1;36m0.000174045650055632\u001b[0m\u001b[1m}\u001b[0m,\n", " \u001b[1m{\u001b[0m\u001b[32m'label'\u001b[0m: \u001b[32m'positive'\u001b[0m, \u001b[32m'score'\u001b[0m: \u001b[1;36m0.9996614456176758\u001b[0m\u001b[1m}\u001b[0m\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m\n", "```\n", "\n", "# Reference\n", "Please cite \u001b[1m(\u001b[0m\u001b[4;94mhttps://journals.sagepub.com/doi/full/10.1177/00222437211037258\u001b[0m\u001b[4;94m)\u001b[0m when you use our model. Feel free to \n", "reach out to \u001b[1m(\u001b[0mmailto:j.p.hartmann@rug.nl\u001b[1m)\u001b[0m with any questions or feedback you may have.\n", "```\n", "@article\u001b[1m{\u001b[0mhartmann2021,\n", " \u001b[33mtitle\u001b[0m=\u001b[1m{\u001b[0mThe Power of Brand Selfies\u001b[1m}\u001b[0m,\n", " \u001b[33mauthor\u001b[0m=\u001b[1m{\u001b[0mHartmann, Jochen and Heitmann, Mark and Schamp, Christina and Netzer, Oded\u001b[1m}\u001b[0m,\n", " \u001b[33mjournal\u001b[0m=\u001b[1m{\u001b[0mJournal of Marketing Research\u001b[1m}\u001b[0m\n", " \u001b[33myear\u001b[0m=\u001b[1m{\u001b[0m\u001b[1;36m2021\u001b[0m\u001b[1m}\u001b[0m\n", "\u001b[1m}\u001b[0m\n", "```\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(retrieved_examples[\"readme\"][9])" ] }, { "cell_type": "markdown", "id": "08d809cf-84b5-4557-8e49-1a3c10e8d236", "metadata": { "id": "08d809cf-84b5-4557-8e49-1a3c10e8d236" }, "source": [ "The results seem pretty reasonable; the first result appears to be a duplicate. The lower result is for a slightly different task using social media data. " ] }, { "cell_type": "markdown", "id": "58a282f6-da24-4551-8081-1dd98b695b05", "metadata": { "id": "58a282f6-da24-4551-8081-1dd98b695b05" }, "source": [ "### Searching \n", "\n", "Being able to find similar model cards is a start but we actually wanted to be able to search directly. Let's see how our results do if we instead search for some terms we might use to try and find suitable models. " ] }, { "cell_type": "code", "execution_count": 43, "id": "5X1SP0zdqn0m", "metadata": { "id": "5X1SP0zdqn0m" }, "outputs": [], "source": [ "q = model.encode(\"fake news\")" ] }, { "cell_type": "code", "execution_count": 44, "id": "Gyu4kUT_rJN5", "metadata": { "id": "Gyu4kUT_rJN5" }, "outputs": [], "source": [ "scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embedding', q, k=10)\n" ] }, { "cell_type": "code", "execution_count": 45, "id": "ab6d70dc-9ece-4b14-aac5-9cb7e386dbae", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 67 }, "id": "ab6d70dc-9ece-4b14-aac5-9cb7e386dbae", "outputId": "72ba07c4-64ae-47c3-f7b3-4223feb9a78b" }, "outputs": [ { "data": { "text/html": [ "
This model is fined tuned for the Fake news classifier: Train a text classification model to detect fake news \n",
       "articles. Base on the Kaggle dataset(https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset).\n",
       "\n",
       "
\n" ], "text/plain": [ "This model is fined tuned for the Fake news classifier: Train a text classification model to detect fake news \n", "articles. Base on the Kaggle \u001b[1;35mdataset\u001b[0m\u001b[1m(\u001b[0m\u001b[4;94mhttps://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset\u001b[0m\u001b[4;94m)\u001b[0m\u001b[4;94m.\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(retrieved_examples[\"readme\"][0])" ] }, { "cell_type": "code", "execution_count": 46, "id": "4d202459-7ae7-4dae-84b5-71da7a634b13", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 116 }, "id": "4d202459-7ae7-4dae-84b5-71da7a634b13", "outputId": "085a337f-2700-4274-87f7-e93b476b9d41" }, "outputs": [ { "data": { "text/html": [ "
Fake news classifier\n",
       "This model trains a text classification model to detect fake news articles, \n",
       "\n",
       "it uses distilbert-base-uncased-finetuned-sst-2-english pretrained model to work on \n",
       "\n",
       "fake and real news dataset from kaggle (https://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset)\n",
       "
\n" ], "text/plain": [ "Fake news classifier\n", "This model trains a text classification model to detect fake news articles, \n", "\n", "it uses distilbert-base-uncased-finetuned-sst-\u001b[1;36m2\u001b[0m-english pretrained model to work on \n", "\n", "fake and real news dataset from kaggle \u001b[1m(\u001b[0m\u001b[4;94mhttps://www.kaggle.com/clmentbisaillon/fake-and-real-news-dataset\u001b[0m\u001b[4;94m)\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(retrieved_examples[\"readme\"][1])" ] }, { "cell_type": "code", "execution_count": 47, "id": "9a4284d7-33ba-4b06-938d-42b0a5a3c01f", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 248 }, "id": "9a4284d7-33ba-4b06-938d-42b0a5a3c01f", "outputId": "8942a90d-5e37-4cad-a51d-319a9bed69c8" }, "outputs": [ { "data": { "text/html": [ "
---\n",
       "license: mit\n",
       "---\n",
       "# Fake and real news classification task \n",
       "\n",
       "Model  : [DistilRoBERTa base model](https://huggingface.co/distilroberta-base)\n",
       "\n",
       "Dataset : [Fake and real news dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset)\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
\n" ], "text/plain": [ "---\n", "license: mit\n", "---\n", "# Fake and real news classification task \n", "\n", "Model : \u001b[1m[\u001b[0mDistilRoBERTa base model\u001b[1m]\u001b[0m\u001b[1m(\u001b[0m\u001b[4;94mhttps://huggingface.co/distilroberta-base\u001b[0m\u001b[4;94m)\u001b[0m\n", "\n", "Dataset : \u001b[1m[\u001b[0mFake and real news dataset\u001b[1m]\u001b[0m\u001b[1m(\u001b[0m\u001b[4;94mhttps://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset\u001b[0m\u001b[4;94m)\u001b[0m\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(retrieved_examples[\"readme\"][2])" ] }, { "cell_type": "markdown", "id": "9bfb2cfb-c946-4e91-9017-46a30dc7883f", "metadata": {}, "source": [ "Not a bad start. Let's try another one" ] }, { "cell_type": "code", "execution_count": 58, "id": "JtCctN1E6bP5", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "JtCctN1E6bP5", "outputId": "136640bf-230e-4db2-9bc9-ac625b75a6ad" }, "outputs": [ { "data": { "text/html": [ "
---\n",
       "language: en\n",
       "tags:\n",
       "- financial-sentiment-analysis\n",
       "- sentiment-analysis\n",
       "datasets:\n",
       "- financial_phrasebank\n",
       "widget:\n",
       "- text: Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing 7.7 %\n",
       "of net sales.\n",
       "- text: Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least EUR \n",
       "4,000.\n",
       "- text: Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in the \n",
       "corresponding period of 2008.\n",
       "---\n",
       "### FinancialBERT for Sentiment Analysis\n",
       "\n",
       "[*FinancialBERT*](https://huggingface.co/ahmedrachid/FinancialBERT) is a BERT model pre-trained on a large corpora \n",
       "of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that \n",
       "financial practitioners and researchers can benefit from this model without the necessity of the significant \n",
       "computational resources required to train the model. \n",
       "\n",
       "The model was fine-tuned for Sentiment Analysis task on _Financial PhraseBank_ dataset. Experiments show that this \n",
       "model outperforms the general BERT and other financial domain-specific models.\n",
       " \n",
       "More details on `FinancialBERT`'s pre-training process can be found at: \n",
       "https://www.researchgate.net/publication/358284785_FinancialBERT_-_A_Pretrained_Language_Model_for_Financial_Text_M\n",
       "ining\n",
       "\n",
       "### Training data\n",
       "FinancialBERT model was fine-tuned on [Financial \n",
       "PhraseBank](https://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10), a dataset consisting of \n",
       "4840 Financial News categorised by sentiment (negative, neutral, positive).\n",
       "\n",
       "### Fine-tuning hyper-parameters\n",
       "- learning_rate = 2e-5\n",
       "- batch_size = 32\n",
       "- max_seq_length = 512\n",
       "- num_train_epochs = 5\n",
       "\n",
       "### Evaluation metrics\n",
       "The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the \n",
       "test set.\n",
       "\n",
       "| sentiment  | precision        | recall           | f1-score  | support  |\n",
       "| ------------- |:-------------:|:-------------:|:-------------:| -----:|\n",
       "| negative | 0.96      | 0.97 | 0.97 | 58 |\n",
       "| neutral | 0.98      | 0.99 | 0.98 | 279 |\n",
       "| positive | 0.98     | 0.97 | 0.97 | 148 |\n",
       "| macro avg | 0.97     | 0.98 | 0.98 | 485 |\n",
       "| weighted avg | 0.98     | 0.98 | 0.98 | 485 |\n",
       "\n",
       " ### How to use \n",
       "The model can be used thanks to Transformers pipeline for sentiment analysis.\n",
       "```python\n",
       "from transformers import BertTokenizer, BertForSequenceClassification\n",
       "from transformers import pipeline\n",
       "\n",
       "model = BertForSequenceClassification.from_pretrained(\"ahmedrachid/FinancialBERT-Sentiment-Analysis\",num_labels=3)\n",
       "tokenizer = BertTokenizer.from_pretrained(\"ahmedrachid/FinancialBERT-Sentiment-Analysis\")\n",
       "\n",
       "nlp = pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer)\n",
       "\n",
       "sentences = [\"Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing\n",
       "7.7 % of net sales.\",  \n",
       "             \"Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least \n",
       "EUR 4,000.\", \n",
       "             \"Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in \n",
       "the corresponding period of 2008.\", \n",
       "             ]\n",
       "results = nlp(sentences)\n",
       "print(results)\n",
       "\n",
       "[{'label': 'positive', 'score': 0.9998133778572083},\n",
       " {'label': 'neutral', 'score': 0.9997822642326355},\n",
       " {'label': 'negative', 'score': 0.9877365231513977}]\n",
       "```\n",
       "\n",
       "> Created by [Ahmed Rachid Hazourli](https://www.linkedin.com/in/ahmed-rachid/)\n",
       "\n",
       "
\n" ], "text/plain": [ "---\n", "language: en\n", "tags:\n", "- financial-sentiment-analysis\n", "- sentiment-analysis\n", "datasets:\n", "- financial_phrasebank\n", "widget:\n", "- text: Operating profit rose to EUR \u001b[1;36m13.1\u001b[0m mn from EUR \u001b[1;36m8.7\u001b[0m mn in the corresponding period in \u001b[1;36m2007\u001b[0m representing \u001b[1;36m7.7\u001b[0m %\n", "of net sales.\n", "- text: Bids or offers include at least \u001b[1;36m1\u001b[0m,\u001b[1;36m000\u001b[0m shares and the value of the shares must correspond to at least EUR \n", "\u001b[1;36m4\u001b[0m,\u001b[1;36m000\u001b[0m.\n", "- text: Raute reported a loss per share of EUR \u001b[1;36m0.86\u001b[0m for the first half of \u001b[1;36m2009\u001b[0m , against EPS of EUR \u001b[1;36m0.74\u001b[0m in the \n", "corresponding period of \u001b[1;36m2008\u001b[0m.\n", "---\n", "### FinancialBERT for Sentiment Analysis\n", "\n", "\u001b[1m[\u001b[0m*FinancialBERT*\u001b[1m]\u001b[0m\u001b[1m(\u001b[0m\u001b[4;94mhttps://huggingface.co/ahmedrachid/FinancialBERT\u001b[0m\u001b[4;94m)\u001b[0m is a BERT model pre-trained on a large corpora \n", "of financial texts. The purpose is to enhance financial NLP research and practice in financial domain, hoping that \n", "financial practitioners and researchers can benefit from this model without the necessity of the significant \n", "computational resources required to train the model. \n", "\n", "The model was fine-tuned for Sentiment Analysis task on _Financial PhraseBank_ dataset. Experiments show that this \n", "model outperforms the general BERT and other financial domain-specific models.\n", " \n", "More details on `FinancialBERT`'s pre-training process can be found at: \n", "\u001b[4;94mhttps://www.researchgate.net/publication/358284785_FinancialBERT_-_A_Pretrained_Language_Model_for_Financial_Text_M\u001b[0m\n", "\u001b[4;94mining\u001b[0m\n", "\n", "### Training data\n", "FinancialBERT model was fine-tuned on \u001b[1m[\u001b[0mFinancial \n", "PhraseBank\u001b[1m]\u001b[0m\u001b[1m(\u001b[0m\u001b[4;94mhttps://www.researchgate.net/publication/251231364_FinancialPhraseBank-v10\u001b[0m\u001b[4;94m)\u001b[0m\u001b[4;94m,\u001b[0m a dataset consisting of \n", "\u001b[1;36m4840\u001b[0m Financial News categorised by sentiment \u001b[1m(\u001b[0mnegative, neutral, positive\u001b[1m)\u001b[0m.\n", "\n", "### Fine-tuning hyper-parameters\n", "- learning_rate = \u001b[1;36m2e-5\u001b[0m\n", "- batch_size = \u001b[1;36m32\u001b[0m\n", "- max_seq_length = \u001b[1;36m512\u001b[0m\n", "- num_train_epochs = \u001b[1;36m5\u001b[0m\n", "\n", "### Evaluation metrics\n", "The evaluation metrics used are: Precision, Recall and F1-score. The following is the classification report on the \n", "test set.\n", "\n", "| sentiment | precision | recall | f1-score | support |\n", "| ------------- |:-------------:|:-------------:|:-------------:| -----:|\n", "| negative | \u001b[1;36m0.96\u001b[0m | \u001b[1;36m0.97\u001b[0m | \u001b[1;36m0.97\u001b[0m | \u001b[1;36m58\u001b[0m |\n", "| neutral | \u001b[1;36m0.98\u001b[0m | \u001b[1;36m0.99\u001b[0m | \u001b[1;36m0.98\u001b[0m | \u001b[1;36m279\u001b[0m |\n", "| positive | \u001b[1;36m0.98\u001b[0m | \u001b[1;36m0.97\u001b[0m | \u001b[1;36m0.97\u001b[0m | \u001b[1;36m148\u001b[0m |\n", "| macro avg | \u001b[1;36m0.97\u001b[0m | \u001b[1;36m0.98\u001b[0m | \u001b[1;36m0.98\u001b[0m | \u001b[1;36m485\u001b[0m |\n", "| weighted avg | \u001b[1;36m0.98\u001b[0m | \u001b[1;36m0.98\u001b[0m | \u001b[1;36m0.98\u001b[0m | \u001b[1;36m485\u001b[0m |\n", "\n", " ### How to use \n", "The model can be used thanks to Transformers pipeline for sentiment analysis.\n", "```python\n", "from transformers import BertTokenizer, BertForSequenceClassification\n", "from transformers import pipeline\n", "\n", "model = \u001b[1;35mBertForSequenceClassification.from_pretrained\u001b[0m\u001b[1m(\u001b[0m\u001b[32m\"ahmedrachid/FinancialBERT-Sentiment-Analysis\"\u001b[0m,\u001b[33mnum_labels\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m\n", "tokenizer = \u001b[1;35mBertTokenizer.from_pretrained\u001b[0m\u001b[1m(\u001b[0m\u001b[32m\"ahmedrachid/FinancialBERT-Sentiment-Analysis\"\u001b[0m\u001b[1m)\u001b[0m\n", "\n", "nlp = \u001b[1;35mpipeline\u001b[0m\u001b[1m(\u001b[0m\u001b[32m\"sentiment-analysis\"\u001b[0m, \u001b[33mmodel\u001b[0m=\u001b[35mmodel\u001b[0m, \u001b[33mtokenizer\u001b[0m=\u001b[35mtokenizer\u001b[0m\u001b[1m)\u001b[0m\n", "\n", "sentences = \u001b[1m[\u001b[0m\u001b[32m\"Operating profit rose to EUR 13.1 mn from EUR 8.7 mn in the corresponding period in 2007 representing\u001b[0m\n", "\u001b[32m7.7 % of net sales.\"\u001b[0m, \n", " \u001b[32m\"Bids or offers include at least 1,000 shares and the value of the shares must correspond to at least \u001b[0m\n", "\u001b[32mEUR 4,000.\"\u001b[0m, \n", " \u001b[32m\"Raute reported a loss per share of EUR 0.86 for the first half of 2009 , against EPS of EUR 0.74 in \u001b[0m\n", "\u001b[32mthe corresponding period of 2008.\"\u001b[0m, \n", " \u001b[1m]\u001b[0m\n", "results = \u001b[1;35mnlp\u001b[0m\u001b[1m(\u001b[0msentences\u001b[1m)\u001b[0m\n", "\u001b[1;35mprint\u001b[0m\u001b[1m(\u001b[0mresults\u001b[1m)\u001b[0m\n", "\n", "\u001b[1m[\u001b[0m\u001b[1m{\u001b[0m\u001b[32m'label'\u001b[0m: \u001b[32m'positive'\u001b[0m, \u001b[32m'score'\u001b[0m: \u001b[1;36m0.9998133778572083\u001b[0m\u001b[1m}\u001b[0m,\n", " \u001b[1m{\u001b[0m\u001b[32m'label'\u001b[0m: \u001b[32m'neutral'\u001b[0m, \u001b[32m'score'\u001b[0m: \u001b[1;36m0.9997822642326355\u001b[0m\u001b[1m}\u001b[0m,\n", " \u001b[1m{\u001b[0m\u001b[32m'label'\u001b[0m: \u001b[32m'negative'\u001b[0m, \u001b[32m'score'\u001b[0m: \u001b[1;36m0.9877365231513977\u001b[0m\u001b[1m}\u001b[0m\u001b[1m]\u001b[0m\n", "```\n", "\n", "> Created by \u001b[1m[\u001b[0mAhmed Rachid Hazourli\u001b[1m]\u001b[0m\u001b[1m(\u001b[0m\u001b[4;94mhttps://www.linkedin.com/in/ahmed-rachid/\u001b[0m\u001b[4;94m)\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q = model.encode(\"financial sentiment\")\n", "scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('embedding', q, k=10)\n", "print(retrieved_examples[\"readme\"][0])" ] }, { "cell_type": "code", "execution_count": 59, "id": "vgxRIzp86hqj", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 545 }, "id": "vgxRIzp86hqj", "outputId": "2680360f-5af8-45d0-e96d-b3d2ab98fce6" }, "outputs": [ { "data": { "text/html": [ "
---\n",
       "language: \"en\"\n",
       "tags:\n",
       "- financial-sentiment-analysis\n",
       "- sentiment-analysis\n",
       "widget:\n",
       "- text: \"Stocks rallied and the British pound gained.\"\n",
       "---\n",
       "\n",
       "FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT\n",
       "language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial \n",
       "sentiment classification. [Financial \n",
       "PhraseBank](https://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientation\n",
       "s_in_Economic_Texts) by Malo et al. (2014) is used for fine-tuning. For more details, please see the paper \n",
       "[FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://arxiv.org/abs/1908.10063) and our \n",
       "related (https://medium.com/prosus-ai-tech-blog/finbert-financial-sentiment-analysis-with-bert-b277a3607101) on \n",
       "Medium.\n",
       "\n",
       "The model will give softmax outputs for three labels: positive, negative or neutral.\n",
       "\n",
       "---\n",
       "\n",
       "About Prosus\n",
       "\n",
       "Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and \n",
       "investing globally in markets with long-term growth potential, Prosus builds leading consumer internet companies \n",
       "that empower people and enrich communities. For more information, please visit www.prosus.com.\n",
       "\n",
       "Contact information\n",
       "\n",
       "Please contact Dogu Araci dogu.araciprosuscom and Zulkuf Genc zulkuf.gencprosuscom about any FinBERT related issues\n",
       "and questions.\n",
       "
\n" ], "text/plain": [ "---\n", "language: \u001b[32m\"en\"\u001b[0m\n", "tags:\n", "- financial-sentiment-analysis\n", "- sentiment-analysis\n", "widget:\n", "- text: \u001b[32m\"Stocks rallied and the British pound gained.\"\u001b[0m\n", "---\n", "\n", "FinBERT is a pre-trained NLP model to analyze sentiment of financial text. It is built by further training the BERT\n", "language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial \n", "sentiment classification. \u001b[1m[\u001b[0mFinancial \n", "PhraseBank\u001b[1m]\u001b[0m\u001b[1m(\u001b[0m\u001b[4;94mhttps://www.researchgate.net/publication/251231107_Good_Debt_or_Bad_Debt_Detecting_Semantic_Orientation\u001b[0m\n", "\u001b[4;94ms_in_Economic_Texts\u001b[0m\u001b[4;94m)\u001b[0m by Malo et al. \u001b[1m(\u001b[0m\u001b[1;36m2014\u001b[0m\u001b[1m)\u001b[0m is used for fine-tuning. For more details, please see the paper \n", "\u001b[1m[\u001b[0mFinBERT: Financial Sentiment Analysis with Pre-trained Language Models\u001b[1m]\u001b[0m\u001b[1m(\u001b[0m\u001b[4;94mhttps://arxiv.org/abs/1908.10063\u001b[0m\u001b[4;94m)\u001b[0m and our \n", "related \u001b[1m(\u001b[0m\u001b[4;94mhttps://medium.com/prosus-ai-tech-blog/finbert-financial-sentiment-analysis-with-bert-b277a3607101\u001b[0m\u001b[4;94m)\u001b[0m on \n", "Medium.\n", "\n", "The model will give softmax outputs for three labels: positive, negative or neutral.\n", "\n", "---\n", "\n", "About Prosus\n", "\n", "Prosus is a global consumer internet group and one of the largest technology investors in the world. Operating and \n", "investing globally in markets with long-term growth potential, Prosus builds leading consumer internet companies \n", "that empower people and enrich communities. For more information, please visit www.prosus.com.\n", "\n", "Contact information\n", "\n", "Please contact Dogu Araci dogu.araciprosuscom and Zulkuf Genc zulkuf.gencprosuscom about any FinBERT related issues\n", "and questions.\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(retrieved_examples[\"readme\"][1])" ] }, { "cell_type": "code", "execution_count": 61, "id": "ZsWQFzN26lyH", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "ZsWQFzN26lyH", "outputId": "184a7348-7329-4ada-f35f-2271a27dd9f9" }, "outputs": [ { "data": { "text/html": [ "
---\n",
       "license: apache-2.0\n",
       "tags:\n",
       "- Finance-sentiment-analysis\n",
       "- generated_from_trainer\n",
       "metrics:\n",
       "- f1\n",
       "- accuracy\n",
       "- precision\n",
       "- recall\n",
       "model-index:\n",
       "- name: bert-base-finance-sentiment-noisy-search\n",
       "  results: []\n",
       "widget:\n",
       " - text: \"Third quarter reported revenues were $10.9 billion, up 5 percent compared to prior year and up 8 percent \n",
       "on a currency-neutral basis\"\n",
       "   example_title: \"Positive\"\n",
       " - text: \"The London-listed website for businesses reported a pretax loss of $26.6 million compared with a loss of \n",
       "$12.9 million the previous year\"\n",
       "   example_title: \"Negative\"\n",
       " - text:  \"Microsoft updates Outlook, Teams, and PowerPoint to be hybrid work ready\"\n",
       "   example_title: \"Neutral\"\n",
       "---\n",
       "\n",
       "<!-- This model card has been generated automatically according to the information the Trainer had access to. You\n",
       "should probably proofread and complete it, then remove this comment. -->\n",
       "\n",
       "# bert-base-finance-sentiment-noisy-search\n",
       "\n",
       "This model is a fine-tuned version of (https://huggingface.co/bert-base-uncased) on Kaggle finance news sentiment \n",
       "analysis with data enhancement using noisy search. The process is explained below:\n",
       "\n",
       "1. First \"bert-base-uncased\" was fine-tuned on Kaggle's finance news sentiment analysis \n",
       "https://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news dataset achieving accuracy of about 88%\n",
       "2. We then used a logistic-regression classifier on the same data. Here we looked at coefficients that contributed \n",
       "the most to the \"Positive\" and \"Negative\" classes by inspecting only bi-grams. \n",
       "3. Using the top 25 bi-grams per class (i.e. \"Positive\" / \"Negative\") we invoked Bing news search with those \n",
       "bi-grams and retrieved up to 50 news items per bi-gram phrase.\n",
       "4. We called it \"noisy-search\" because it is assumed the positive bi-grams (e.g. \"profit rose\" , \"growth net\") give\n",
       "rise to positive examples whereas negative bi-grams (e.g. \"loss increase\", \"share loss\") result in negative \n",
       "examples but note that we didn't test for the validity of this assumption (hence: noisy-search)\n",
       "5. For each article we kept the title + excerpt and labeled it according to pre-assumptions on class associations.\n",
       "6. We then trained the same model on the noisy data and apply it to an held-out test set from the original data set\n",
       "split.\n",
       "7. Training with couple of thousands noisy \"positives\" and \"negatives\" examples yielded a test set accuracy of \n",
       "about 95%. \n",
       "8. It shows that by automatically collecting noisy examples using search we can boost accuracy performance from \n",
       "about 88% to more than 95%.\n",
       "\n",
       "Accuracy results for Logistic Regression (LR) and BERT (base-cased) are shown in the attached pdf:\n",
       "\n",
       "https://drive.google.com/file/d/1MI9gRdppactVZ_XvhCwvoaOV1aRfprrd/view?usp=sharing \n",
       "\n",
       "\n",
       "## Model description\n",
       "\n",
       "BERT model trained on noisy data from search results. See PDF for more details.\n",
       "\n",
       "## Intended uses & limitations\n",
       "\n",
       "Intended for use on finance news sentiment analysis with 3 options: \"Positive\", \"Neutral\" and \"Negative\"\n",
       "To get the best results feed the classifier with the title and either the 1st paragraph or a short news \n",
       "summarization e.g. of up to 64 tokens. \n",
       "\n",
       "### Training hyperparameters\n",
       "\n",
       "The following hyperparameters were used during training:\n",
       "- learning_rate: 5e-05\n",
       "- train_batch_size: 8\n",
       "- eval_batch_size: 8\n",
       "- seed: 42\n",
       "- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n",
       "- lr_scheduler_type: linear\n",
       "- num_epochs: 5\n",
       "\n",
       "\n",
       "### Framework versions\n",
       "\n",
       "- Transformers 4.16.2\n",
       "- Pytorch 1.10.0+cu111\n",
       "- Datasets 1.18.3\n",
       "- Tokenizers 0.11.0\n",
       "\n",
       "
\n" ], "text/plain": [ "---\n", "license: apache-\u001b[1;36m2.0\u001b[0m\n", "tags:\n", "- Finance-sentiment-analysis\n", "- generated_from_trainer\n", "metrics:\n", "- f1\n", "- accuracy\n", "- precision\n", "- recall\n", "model-index:\n", "- name: bert-base-finance-sentiment-noisy-search\n", " results: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n", "widget:\n", " - text: \u001b[32m\"Third quarter reported revenues were $10.9 billion, up 5 percent compared to prior year and up 8 percent \u001b[0m\n", "\u001b[32mon a currency-neutral basis\"\u001b[0m\n", " example_title: \u001b[32m\"Positive\"\u001b[0m\n", " - text: \u001b[32m\"The London-listed website for businesses reported a pretax loss of $26.6 million compared with a loss of \u001b[0m\n", "\u001b[32m$12.9 million the previous year\"\u001b[0m\n", " example_title: \u001b[32m\"Negative\"\u001b[0m\n", " - text: \u001b[32m\"Microsoft updates Outlook, Teams, and PowerPoint to be hybrid work ready\"\u001b[0m\n", " example_title: \u001b[32m\"Neutral\"\u001b[0m\n", "---\n", "\n", "\u001b[1m<\u001b[0m\u001b[39m!-- This model card has been generated automatically according to the information the Trainer had access to. You\u001b[0m\n", "\u001b[39mshould probably proofread and complete it, then remove this comment. --\u001b[0m\u001b[1m>\u001b[0m\n", "\n", "# bert-base-finance-sentiment-noisy-search\n", "\n", "This model is a fine-tuned version of \u001b[1m(\u001b[0m\u001b[4;94mhttps://huggingface.co/bert-base-uncased\u001b[0m\u001b[4;94m)\u001b[0m on Kaggle finance news sentiment \n", "analysis with data enhancement using noisy search. The process is explained below:\n", "\n", "\u001b[1;36m1\u001b[0m. First \u001b[32m\"bert-base-uncased\"\u001b[0m was fine-tuned on Kaggle's finance news sentiment analysis \n", "\u001b[4;94mhttps://www.kaggle.com/ankurzing/sentiment-analysis-for-financial-news\u001b[0m dataset achieving accuracy of about \u001b[1;36m88\u001b[0m%\n", "\u001b[1;36m2\u001b[0m. We then used a logistic-regression classifier on the same data. Here we looked at coefficients that contributed \n", "the most to the \u001b[32m\"Positive\"\u001b[0m and \u001b[32m\"Negative\"\u001b[0m classes by inspecting only bi-grams. \n", "\u001b[1;36m3\u001b[0m. Using the top \u001b[1;36m25\u001b[0m bi-grams per class \u001b[1m(\u001b[0mi.e. \u001b[32m\"Positive\"\u001b[0m \u001b[35m/\u001b[0m \u001b[32m\"Negative\"\u001b[0m\u001b[1m)\u001b[0m we invoked Bing news search with those \n", "bi-grams and retrieved up to \u001b[1;36m50\u001b[0m news items per bi-gram phrase.\n", "\u001b[1;36m4\u001b[0m. We called it \u001b[32m\"noisy-search\"\u001b[0m because it is assumed the positive bi-grams \u001b[1m(\u001b[0me.g. \u001b[32m\"profit rose\"\u001b[0m , \u001b[32m\"growth net\"\u001b[0m\u001b[1m)\u001b[0m give\n", "rise to positive examples whereas negative bi-grams \u001b[1m(\u001b[0me.g. \u001b[32m\"loss increase\"\u001b[0m, \u001b[32m\"share loss\"\u001b[0m\u001b[1m)\u001b[0m result in negative \n", "examples but note that we didn't test for the validity of this assumption \u001b[1m(\u001b[0mhence: noisy-search\u001b[1m)\u001b[0m\n", "\u001b[1;36m5\u001b[0m. For each article we kept the title + excerpt and labeled it according to pre-assumptions on class associations.\n", "\u001b[1;36m6\u001b[0m. We then trained the same model on the noisy data and apply it to an held-out test set from the original data set\n", "split.\n", "\u001b[1;36m7\u001b[0m. Training with couple of thousands noisy \u001b[32m\"positives\"\u001b[0m and \u001b[32m\"negatives\"\u001b[0m examples yielded a test set accuracy of \n", "about \u001b[1;36m95\u001b[0m%. \n", "\u001b[1;36m8\u001b[0m. It shows that by automatically collecting noisy examples using search we can boost accuracy performance from \n", "about \u001b[1;36m88\u001b[0m% to more than \u001b[1;36m95\u001b[0m%.\n", "\n", "Accuracy results for Logistic Regression \u001b[1m(\u001b[0mLR\u001b[1m)\u001b[0m and BERT \u001b[1m(\u001b[0mbase-cased\u001b[1m)\u001b[0m are shown in the attached pdf:\n", "\n", "\u001b[4;94mhttps://drive.google.com/file/d/1MI9gRdppactVZ_XvhCwvoaOV1aRfprrd/view?\u001b[0m\u001b[4;94musp\u001b[0m\u001b[4;94m=\u001b[0m\u001b[4;94msharing\u001b[0m \n", "\n", "\n", "## Model description\n", "\n", "BERT model trained on noisy data from search results. See PDF for more details.\n", "\n", "## Intended uses & limitations\n", "\n", "Intended for use on finance news sentiment analysis with \u001b[1;36m3\u001b[0m options: \u001b[32m\"Positive\"\u001b[0m, \u001b[32m\"Neutral\"\u001b[0m and \u001b[32m\"Negative\"\u001b[0m\n", "To get the best results feed the classifier with the title and either the 1st paragraph or a short news \n", "summarization e.g. of up to \u001b[1;36m64\u001b[0m tokens. \n", "\n", "### Training hyperparameters\n", "\n", "The following hyperparameters were used during training:\n", "- learning_rate: \u001b[1;36m5e-05\u001b[0m\n", "- train_batch_size: \u001b[1;36m8\u001b[0m\n", "- eval_batch_size: \u001b[1;36m8\u001b[0m\n", "- seed: \u001b[1;36m42\u001b[0m\n", "- optimizer: Adam with \u001b[33mbetas\u001b[0m=\u001b[1m(\u001b[0m\u001b[1;36m0.9\u001b[0m,\u001b[1;36m0.999\u001b[0m\u001b[1m)\u001b[0m and \u001b[33mepsilon\u001b[0m=\u001b[1;36m1e\u001b[0m\u001b[1;36m-08\u001b[0m\n", "- lr_scheduler_type: linear\n", "- num_epochs: \u001b[1;36m5\u001b[0m\n", "\n", "\n", "### Framework versions\n", "\n", "- Transformers \u001b[1;36m4.16\u001b[0m.\u001b[1;36m2\u001b[0m\n", "- Pytorch \u001b[1;36m1.10\u001b[0m.\u001b[1;36m0\u001b[0m+cu111\n", "- Datasets \u001b[1;36m1.18\u001b[0m.\u001b[1;36m3\u001b[0m\n", "- Tokenizers \u001b[1;36m0.11\u001b[0m.\u001b[1;36m0\u001b[0m\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(retrieved_examples[\"readme\"][9])" ] }, { "cell_type": "markdown", "id": "a028563e-7a55-4048-81a7-643c2c819bd6", "metadata": {}, "source": [ "These seem like a good starting point. However, we have a few issues relying on model cards alone. Firstly a lot of models don't include them and the quality of them can be mixed. It's maybe a question if we want to use a model that has no model card at all but it is possible that despite a good model card we don't capture everything we'd need for searching in the README. " ] }, { "cell_type": "markdown", "id": "304bc9c6-cbd0-4609-92ba-71ce0a1d17b5", "metadata": { "id": "304bc9c6-cbd0-4609-92ba-71ce0a1d17b5" }, "source": [ "## Can we search using model labels? \n", "\n", "We're only working with classification models in this case. For most Pytorch models on the hub, we have a config file. This config usually contains the model's labels. For example, 'positive', 'negative'. \n", "\n", "Maybe instead of relying only on the metadata, we can search 'inside' the model. The labels will often be a helpful reflection of what we're looking for. For example, we want to find a sentiment classification model that roughly puts text into positive or negative sentiment. Again, relying on exact label matches may not work well, but maybe embeddings get around this problem. Let's try it out! \n" ] }, { "cell_type": "markdown", "id": "3a96cd6d-f7ed-43f2-abeb-417905722a37", "metadata": { "id": "43e3539f-9ff3-42bb-bf1e-46713331c4b4" }, "source": [ "Let's look at an example label. " ] }, { "cell_type": "code", "execution_count": 62, "id": "Jpp0XLQCr8IJ", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "Jpp0XLQCr8IJ", "outputId": "eccdba8e-1adb-4eea-e8ff-51fe40107b0a" }, "outputs": [ { "data": { "text/html": [ "
['NEGATIVE', 'POSITIVE']\n",
       "
\n" ], "text/plain": [ "\u001b[1m[\u001b[0m\u001b[32m'NEGATIVE'\u001b[0m, \u001b[32m'POSITIVE'\u001b[0m\u001b[1m]\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds[0]['label']" ] }, { "cell_type": "markdown", "id": "fdccc678-ed22-4602-afb7-d26179f0fc9b", "metadata": {}, "source": [ "Since we're expecting labels to match this format lets filter out any that don't fit this structure. " ] }, { "cell_type": "code", "execution_count": 63, "id": "VN0CnVw4sPQ6", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "80d415bda86347cba39a1d0ed9b07f34", "4221421373b94c5eba3b1ca1d696d3b1", "9ab646e5c38c4f66be77fc9a20987e32", "4e721950f34445aea7e8ffa74ebcd13f", "9e32c3df1c054269b0df745bedb1fb85", "1712845de9b04a46a4462d51bdda32fe", "27943572198d468088d5cc47a2ddc94b", "1865c2f13e614a1ea530291341ba1c87", "19784c6a139a46ea85d43ee37ba1b03b", "b127dc590a344563a33f2a7586bb8186", "79894a62c12b429496506551771196dd" ] }, "id": "VN0CnVw4sPQ6", "outputId": "f3e3e663-9f0b-42f3-d73f-c6503e30e71b" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "80d415bda86347cba39a1d0ed9b07f34", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/7 [00:00\n", "Dataset({\n", " features: ['modelId', 'label', 'readme', 'readme_len', 'string_label'],\n", " num_rows: 4175\n", "})\n", "\n" ], "text/plain": [ "\n", "\u001b[1;35mDataset\u001b[0m\u001b[1m(\u001b[0m\u001b[1m{\u001b[0m\n", " features: \u001b[1m[\u001b[0m\u001b[32m'modelId'\u001b[0m, \u001b[32m'label'\u001b[0m, \u001b[32m'readme'\u001b[0m, \u001b[32m'readme_len'\u001b[0m, \u001b[32m'string_label'\u001b[0m\u001b[1m]\u001b[0m,\n", " num_rows: \u001b[1;36m4175\u001b[0m\n", "\u001b[1m}\u001b[0m\u001b[1m)\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds" ] }, { "cell_type": "code", "execution_count": 66, "id": "BXiXjHbxspIO", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "83eb1c47e6d14d268e01dfb6bece0c14", "19add75a4f69486aaf77e0f46a1d6d53", "a18e0d64917344e5beca64253ed3c1a2", "ef3a0a50d274468781b0441e691ddf7b", "51b38fbfb6cd431990fdff1627734878", "b691fa32db364ea5a34d71623960dd23", "ebc22ef492504b55b89ec1a86b5b4bd5", "181ed30c9e464c7c9ec1dc36f779c766", "c0f222fdaef14cbe82c15a168a3a51c6", "7eed9fff711f4c3a9c473cb751bcf8ab", "e7bdd0fcc07f423c9a69553c60b01b38" ] }, "id": "BXiXjHbxspIO", "outputId": "3cef0728-5614-42e6-f601-b0c32f381afb" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "83eb1c47e6d14d268e01dfb6bece0c14", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/261 [00:00\n", "Dataset({\n", " features: ['modelId', 'label', 'readme', 'readme_len', 'string_label', 'label_embedding'],\n", " num_rows: 4175\n", "})\n", "\n" ], "text/plain": [ "\n", "\u001b[1;35mDataset\u001b[0m\u001b[1m(\u001b[0m\u001b[1m{\u001b[0m\n", " features: \u001b[1m[\u001b[0m\u001b[32m'modelId'\u001b[0m, \u001b[32m'label'\u001b[0m, \u001b[32m'readme'\u001b[0m, \u001b[32m'readme_len'\u001b[0m, \u001b[32m'string_label'\u001b[0m, \u001b[32m'label_embedding'\u001b[0m\u001b[1m]\u001b[0m,\n", " num_rows: \u001b[1;36m4175\u001b[0m\n", "\u001b[1m}\u001b[0m\u001b[1m)\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds_with_embeddings" ] }, { "cell_type": "markdown", "id": "b9e4369f-58c8-476f-b4ef-c274b366bc42", "metadata": {}, "source": [ "### Searching with labels \n", "\n", "Now we have some embeddings for the labels, let's try searching. Let's start with an existing set of labels to see how well we can match those. " ] }, { "cell_type": "code", "execution_count": 68, "id": "Imdak0SwspDS", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "id": "Imdak0SwspDS", "outputId": "1cec116b-ddcc-4341-d7ec-2546c85be687" }, "outputs": [ { "data": { "text/html": [ "
'NEGATIVE,POSITIVE'\n",
       "
\n" ], "text/plain": [ "\u001b[32m'NEGATIVE,POSITIVE'\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_with_embeddings[0]['string_label']" ] }, { "cell_type": "code", "execution_count": 69, "id": "voCH1RhWspAp", "metadata": { "id": "voCH1RhWspAp" }, "outputs": [], "source": [ "q = model.encode(\"negative\")" ] }, { "cell_type": "code", "execution_count": 70, "id": "Fx1ZfWnTtHSB", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 115, "referenced_widgets": [ "71a66efdc9ca44019ebdd09eef58e444", "46281506e209402197db76d852f0b151", "0b44804c6cbe42fa9ed29a5c21fc1a8e", "488d70fb02aa420ba8c9cd3494c6d2ad", "e7519bbf5b844685b414c6b4401b2f87", "56c8332995414ebcb97dabb99ef0c800", "8fc4f8fe63674cf0889b066d21450348", "4fe9bcaf5e2f4f73a76ad045de06083d", "ef60b13d4d8149b39db05db3291d34a1", "9e0cf398672d42199c13d14ad653708a", "c6ed87962d32414fab524cd8762006b6" ] }, "id": "Fx1ZfWnTtHSB", "outputId": "fb3b53fe-3d6b-45f8-ea2b-a4478f787096" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "71a66efdc9ca44019ebdd09eef58e444", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/5 [00:00\n", "Dataset({\n", " features: ['modelId', 'label', 'readme', 'readme_len', 'string_label', 'label_embedding'],\n", " num_rows: 4175\n", "})\n", "\n" ], "text/plain": [ "\n", "\u001b[1;35mDataset\u001b[0m\u001b[1m(\u001b[0m\u001b[1m{\u001b[0m\n", " features: \u001b[1m[\u001b[0m\u001b[32m'modelId'\u001b[0m, \u001b[32m'label'\u001b[0m, \u001b[32m'readme'\u001b[0m, \u001b[32m'readme_len'\u001b[0m, \u001b[32m'string_label'\u001b[0m, \u001b[32m'label_embedding'\u001b[0m\u001b[1m]\u001b[0m,\n", " num_rows: \u001b[1;36m4175\u001b[0m\n", "\u001b[1m}\u001b[0m\u001b[1m)\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ds_with_embeddings.add_faiss_index(column='label_embedding')" ] }, { "cell_type": "code", "execution_count": 71, "id": "eonCnFZIso94", "metadata": { "id": "eonCnFZIso94" }, "outputs": [], "source": [ "scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('label_embedding', q, k=10)\n" ] }, { "cell_type": "code", "execution_count": 72, "id": "eD4DKZgmso6t", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 215 }, "id": "eD4DKZgmso6t", "outputId": "251c4135-b60a-48d4-eef7-dc6893b078e6" }, "outputs": [ { "data": { "text/html": [ "
\n",
       "[\n",
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       "    ['negative', 'positive'],\n",
       "    ['negative', 'positive'],\n",
       "    ['negative', 'positive'],\n",
       "    ['negative', 'positive'],\n",
       "    ['negative', 'positive'],\n",
       "    ['negative', 'positive'],\n",
       "    ['negative', 'positive'],\n",
       "    ['negative', 'positive'],\n",
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\n",
       "[\n",
       "    ['Dance', 'Heavy Metal', 'Hip Hop', 'Indie', 'Pop', 'Rock'],\n",
       "    ['Dance', 'Heavy Metal', 'Hip Hop', 'Indie', 'Pop', 'Rock'],\n",
       "    ['Dance', 'Heavy Metal', 'Hip Hop', 'Indie', 'Pop', 'Rock'],\n",
       "    [\n",
       "        'Alternative',\n",
       "        'Country',\n",
       "        'Eletronic Music',\n",
       "        'Gospel and Worship Songs',\n",
       "        'Hip-Hop',\n",
       "        'Jazz/Blues',\n",
       "        'Pop',\n",
       "        'R&B/Soul',\n",
       "        'Reggae',\n",
       "        'Rock'\n",
       "    ],\n",
       "    ['business', 'entertainment', 'sports'],\n",
       "    ['_silence_', '_unknown_', 'down', 'go', 'left', 'no', 'off', 'on', 'right', 'stop', 'up', 'yes'],\n",
       "    ['angry', 'happy', 'others', 'sad'],\n",
       "    ['Feeling', 'Thinking'],\n",
       "    [\n",
       "        'am_thuc',\n",
       "        'bong_da',\n",
       "        'cho_thue',\n",
       "        'doi_song',\n",
       "        'dong_vat',\n",
       "        'mua_ban',\n",
       "        'nhac',\n",
       "        'phim',\n",
       "        'phu_kien',\n",
       "        'sach',\n",
       "        'showbiz',\n",
       "        'the_thao',\n",
       "        'thoi_trang_nam',\n",
       "        'thoi_trang_nu',\n",
       "        'thuc_vat',\n",
       "        'tin_bds',\n",
       "        'tin_tuc',\n",
       "        'tri_thuc'\n",
       "    ],\n",
       "    ['intimacy']\n",
       "]\n",
       "
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\n",
       "[\n",
       "    ['Hateful', 'Not hateful'],\n",
       "    ['Hateful', 'Not hateful'],\n",
       "    ['hateful', 'non-hateful'],\n",
       "    ['hateful', 'non-hateful'],\n",
       "    ['hateful', 'non-hateful'],\n",
       "    ['HATE', 'NOT_HATE'],\n",
       "    ['NON_HATE', 'HATE'],\n",
       "    ['NON_HATE', 'HATE'],\n",
       "    ['NON_HATE', 'HATE'],\n",
       "    ['NON_HATE', 'HATE']\n",
       "]\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[1m[\u001b[0m\n", " \u001b[1m[\u001b[0m\u001b[32m'Hateful'\u001b[0m, \u001b[32m'Not hateful'\u001b[0m\u001b[1m]\u001b[0m,\n", " \u001b[1m[\u001b[0m\u001b[32m'Hateful'\u001b[0m, \u001b[32m'Not hateful'\u001b[0m\u001b[1m]\u001b[0m,\n", " \u001b[1m[\u001b[0m\u001b[32m'hateful'\u001b[0m, \u001b[32m'non-hateful'\u001b[0m\u001b[1m]\u001b[0m,\n", " \u001b[1m[\u001b[0m\u001b[32m'hateful'\u001b[0m, \u001b[32m'non-hateful'\u001b[0m\u001b[1m]\u001b[0m,\n", " \u001b[1m[\u001b[0m\u001b[32m'hateful'\u001b[0m, \u001b[32m'non-hateful'\u001b[0m\u001b[1m]\u001b[0m,\n", " \u001b[1m[\u001b[0m\u001b[32m'HATE'\u001b[0m, \u001b[32m'NOT_HATE'\u001b[0m\u001b[1m]\u001b[0m,\n", " \u001b[1m[\u001b[0m\u001b[32m'NON_HATE'\u001b[0m, \u001b[32m'HATE'\u001b[0m\u001b[1m]\u001b[0m,\n", " \u001b[1m[\u001b[0m\u001b[32m'NON_HATE'\u001b[0m, \u001b[32m'HATE'\u001b[0m\u001b[1m]\u001b[0m,\n", " \u001b[1m[\u001b[0m\u001b[32m'NON_HATE'\u001b[0m, \u001b[32m'HATE'\u001b[0m\u001b[1m]\u001b[0m,\n", " \u001b[1m[\u001b[0m\u001b[32m'NON_HATE'\u001b[0m, \u001b[32m'HATE'\u001b[0m\u001b[1m]\u001b[0m\n", "\u001b[1m]\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "retrieved_examples['label'][:10]" ] }, { "cell_type": "markdown", "id": "1f4e42e3-6387-4794-9a83-2b1b908b1361", "metadata": {}, "source": [ "Again here we have something quite close to what we'd get with string matching, but we have a bit more flexibility in how we spell/define our labels which might help surface more possible results. \n", "\n", "We'll try a bunch more things..." ] }, { "cell_type": "code", "execution_count": 79, "id": "wI_zeRh0A3MH", "metadata": { "id": "wI_zeRh0A3MH" }, "outputs": [], "source": [ "def query_labels(query:str):\n", " q = model.encode(query)\n", " scores, retrieved_examples = ds_with_embeddings.get_nearest_examples('label_embedding', q, k=10)\n", " print(f\"results for: {query}\")\n", " print(list(zip(retrieved_examples['label'][:10],retrieved_examples['modelId'][:10])))" ] }, { "cell_type": "code", "execution_count": 80, "id": "pe6MTppxBRbQ", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331 }, "id": "pe6MTppxBRbQ", "outputId": "73ede30f-0a47-475f-b0e9-ba2c199f026e" }, "outputs": [ { "data": { "text/html": [ "
results for: politics\n",
       "
\n" ], "text/plain": [ "results for: politics\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[\n",
       "    (['Democrat', 'Republican'], 'm-newhauser/distilbert-political-tweets'),\n",
       "    (['Geopolitical', 'Personal', 'Political', 'Religious'], 'dee4hf/autotrain-deephate2-1093539673'),\n",
       "    (['None', 'Environmental', 'Social', 'Governance'], 'yiyanghkust/finbert-esg'),\n",
       "    (['business', 'entertainment', 'sports'], 'bipin/malayalam-news-classifier'),\n",
       "    (\n",
       "        ['CRIME', 'ENTERTAINMENT', 'Finance', 'POLITICS', 'SPORTS', 'Terrorism'],\n",
       "        'Yarn007/autotrain-Napkin-872827783'\n",
       "    ),\n",
       "    (['business', 'entertainment', 'politics', 'sport', 'tech'], 'abhishek/autonlp-bbc-roberta-37249301'),\n",
       "    (\n",
       "        ['business', 'entertainment', 'politics', 'sport', 'tech'],\n",
       "        'abhishek/autonlp-bbc-news-classification-37229289'\n",
       "    ),\n",
       "    (['business', 'entertainment', 'politics', 'sport', 'tech'], 'Yarn/autotrain-Traimn-853827191'),\n",
       "    (['Neutral', 'Propaganda'], 'Real29/my-model-proppy'),\n",
       "    (['Neutral', 'Propaganda'], 'Real29/my-model-ptc')\n",
       "]\n",
       "
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results for: fiction, non_fiction\n",
       "
\n" ], "text/plain": [ "results for: fiction, non_fiction\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
[\n",
       "    (\n",
       "        ['action', 'drama', 'horror', 'sci_fi', 'superhero', 'thriller'],\n",
       "        'Tejas3/distillbert_110_uncased_movie_genre'\n",
       "    ),\n",
       "    (['action', 'drama', 'horror', 'sci_fi', 'superhero', 'thriller'], 'Tejas3/distillbert_110_uncased_v1'),\n",
       "    (\n",
       "        ['action', 'animation', 'comedy', 'drama', 'romance', 'thriller'],\n",
       "        'langfab/distilbert-base-uncased-finetuned-movie-genre'\n",
       "    ),\n",
       "    (['HATE', 'NON_HATE'], 'anthonny/dehatebert-mono-spanish-finetuned-sentiments_reviews_politicos'),\n",
       "    (['NON_HATE', 'HATE'], 'Hate-speech-CNERG/dehatebert-mono-english'),\n",
       "    (['NON_HATE', 'HATE'], 'Hate-speech-CNERG/dehatebert-mono-german'),\n",
       "    (['NON_HATE', 'HATE'], 'Hate-speech-CNERG/dehatebert-mono-italian'),\n",
       "    (['NON_HATE', 'HATE'], 'Hate-speech-CNERG/dehatebert-mono-spanish'),\n",
       "    (['NON_HATE', 'HATE'], 'Hate-speech-CNERG/dehatebert-mono-portugese'),\n",
       "    (['NON_HATE', 'HATE'], 'Hate-speech-CNERG/dehatebert-mono-polish')\n",
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results for: worry, disgust, anxiety, fear\n",
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" ] }, { "cell_type": "markdown", "id": "61d013c6-cb62-409d-af5a-958d05e11edc", "metadata": { "id": "61d013c6-cb62-409d-af5a-958d05e11edc" }, "source": [ "## Conclusion \n", "\n", "It seems like there is some merit in exploring some of these ideas further. There are a lot of improvements that could be made: \n", "- how the embeddings are created\n", "- removing some 'noise' from the README, for example, by first parsing the Markdown\n", "- improving how the embeddings are created for the labels \n", "- combining the embeddings in some way either upfront or when queryig\n", "- a bunch of other things... \n", "\n", "If I find some spare time, I plan to dig into these topics a bit further. 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