{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "WpNn7OdIb_ID" }, "source": [ "## Using BERTNN to predict affective meanings:\n", "\n", "This Jupyter-notebook file briefly reviews how the BERTNN model can estimate affective meanings. This is an ongoing project, and many parts may be changed in the final draft. This draft is for personal use. Please get in touch with the authors if you need to share it with your collaborators.\n", "\n", "You can click \"Runtime\" on the top menu and select \"Run all.\" Depending on the assigned cpu/gpu from Google colab, it takes a couple of minutes to run the following chucks. Then you can use one of the following functions to get estimated EPA values:\n", "\n", "- EPA_sents() : You get an estimated EPA value for all words of an event in MABMO grammar.\n", "\n", "To find affective meaning distribution for a given concept, we can use 'EPA_sents()' in different events and find the distribution for the word of interest. A simple approach is to use other words in affective dictionaries and make some random events and estimate the distribution using those events. Using this approach, we defined the following function to estimate EPA values for a specific concept.\n", "- get_output_new() : You get multiple estimated EPA values for a word as identity, behavior, or modifier in different events. The estimates are different depending on other concepts in MABMO grammar\n", "\n", "Each synthetic event is given in 'get_output_new()' shows different estimated EPA values for the word of interest. We can use multiple samples to get an estimated distribution.\n", "\n", "Please check the third chunks to find some examples." ] }, { "cell_type": "code", "source": [ "!pip install transformers==4.21.0\n", "\n", "!git clone https://huggingface.co/bertnn/product\n", "%cd product\n", "# !pip install -r requirements.txt\n", "%run predefined_bertnn.py\n", "\n", "!pip install git-lfs\n", "!git lfs install\n", "# !git clone https://huggingface.co/bertnn/whole\n", "# %cd whole\n", "# %run predefined.py\n" ], "metadata": { "id": "HETWZjmteWef", "outputId": "1f46028f-86a6-4e83-8f1f-76b26d47c296", "colab": { "base_uri": "https://localhost:8080/", "height": 981, "referenced_widgets": [ "bf0eed9151aa45e48322a0ef522397cf", "bcfbf895deac4626a01b4d39337deef2", "c237c6ef9a3747eaab27b9020281b154", "47bed602a7714a59a1d6f42b4b30cf45", "bedabb99729f4ac2b301a46d4b926e55", "59a2f60d694a4aefbff43f56d23b0dea", "3b305f25104c4e9fa3843f286adcf465", "606b863619d84db090285eac58606de3", "7025aba45ad7489a86e58dd1d63ec8bd", "d34feafba6aa449b9e6c2a44f970993a", "486566ad2afe46a79f34dd4c9d36ca7c", "257de431c3a34426aeb422fa768a36ec", "74fb6c2d30db4239986ae6b570fb3778", "d6316539f25f47189382e0fc86c973b7", "6cc89957b5f34551ae5a9fb8798c50d5", "47b80bcfd621441ebaaa5239744c22e0", "6de8ce6e19b0415ebbdbb6b1020d991f", "106bc9eaab894b23bfa0007168320ce1", "afc1c349623c41d6a2d9237c14d51975", "68c959028fcb4e3394418268c6a01b74", "a484e122462643ffa9b4f94d773c5c8a", "dca8bec64e894451a4240d8c77da0730", "f3a1d0491eb84e30b95d909e914ae67c", "6f9958bd17004fdfa6194aacebcf451b", "0abe36dfb02f4dd6a244bec30e17dad1", "a9db4a605abd405f9491585d4e6e15fb", "24266c2c47cd45d7a303e7a860b6412a", "aaa9122f29ad49e99636040c9e3961fe", "de7af9c552a343a7879b7f46a318aa45", "428ab4315d264181bcad9c5fa7862188", "4f88ab0159764e70a54a27681ad6ee85", "28a66e1809914509ba65b5917c77a0a6", "170d7519cf234a5f9f3bca003a0b1eb9", "f1f300b8fba84dd8a8d487a66512f9cf", "0ea82df6d27f4fb9a484e57a59d99461", "a85de4cea0c54ac4a1e67dddbcf9d6bf", "8950e85c74d54e32ae15a054403a0e51", "9130399af1744d5b9db4b360018c7367", "58aa81d2cd2e4ad0891b780962584199", "cdd5b606dc704aedb3ab961ba1e3edd0", "844b69bd850f4be5a66df0f124fb7b12", "a920e6438b4749bfaf2356cbd27e1931", "1bfae30b3fe4493fae58037a6afca0f9", "6d881620c57d4209a36848bc9e3687dc" ] } }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting transformers==4.21.0\n", " Downloading transformers-4.21.0-py3-none-any.whl (4.7 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m9.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.21.0) (3.13.1)\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.21.0) (0.20.3)\n", "Requirement already satisfied: 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\n" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "markdown", "source": [ "## Find affective meaning distribution for a concept.\n", "\n", "BERTNN uses the context to estimate affective meaning of a concept. Depending on the words used in an event, we may get different estimates. We defined 'get_output_new' or 'get_output_agg' to estimate different estimates for a concept. This funtion has the parameters in this function are as follow,\n", "- w: This is the word/concept of interest\n", "- wt: It specifies whether the concept is identity, modifier, or behavior.\n", "- batch_sz : Specifies the number of samples we want to use for the estimation\n", "- I_b,B_b and ,M_b: They specify the set used to make synthetic events in estimating the distribution.\n", "\n", "### You can replace the concept in the following chunk and see its corresponding estimates.\n", "\n", "To see all the columns and rows, please use the magic blue pen to convert the dataframe to an interactive table." ], "metadata": { "id": "KVOUjYSWdV3m" } }, { "cell_type": "code", "source": [ "df_sample=get_output_agg('intrudes','behavior',batch_sz=300 )\n", "df_sample.round(decimals=2)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "Jlp3dvMA4_Ao", "outputId": "d9929e52-9f08-444d-df6a-5587b9ec32af" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " EE EP EA ModA Actor Behavior \\\n", "0 -1.19 0.95 0.51 appalled shopper intrudes \n", "1 -1.27 0.87 0.56 employed invalid intrudes \n", "2 -1.26 0.92 0.54 open minded deadbeat intrudes \n", "3 -1.17 1.07 0.51 upset wallflower intrudes \n", "4 -0.85 1.07 0.57 attractive bum intrudes \n", ".. ... ... ... ... ... ... \n", "295 -1.46 1.00 0.53 disloyal dummy intrudes \n", "296 -1.31 0.97 0.47 snowed baker intrudes \n", "297 -1.19 0.99 0.53 manic switchboard operator intrudes \n", "298 -1.08 0.98 0.54 demure dope intrudes \n", "299 -1.43 0.90 0.64 tormented taxpayer intrudes \n", "\n", " ModO Object \n", "0 kind astrologer \n", "1 snowed wife \n", "2 serious state trooper \n", "3 industrious boy scout \n", "4 unstable secretary \n", ".. ... ... \n", "295 gregarious musician \n", "296 successful tailor \n", "297 aggravated janitor \n", "298 open republican \n", "299 disrespectful alcoholic \n", "\n", "[300 rows x 8 columns]" ], "text/html": [ "\n", "
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EEEPEAModAActorBehaviorModOObject
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8-1.220.940.53shallowvipintrudesupsetswitchboard operator
9-1.311.050.55efficientforeignerintrudesfrustratedretailer
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EEEPEAModAActorBehaviorModOObject
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