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null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "0ac95cc72ee44035a8dc04e906513695": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "code", "source": [], "metadata": { "id": "LN2V2eas8K9k" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "otN85XDGhfm3", "outputId": "820f99ab-bd79-4ff4-d6db-b5b862788a21" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "markdown", "source": [ "# LOADING AND INSTALLING LIBRARIES" ], "metadata": { "id": "xuzqFnfnjJJp" } }, { "cell_type": "code", "source": [ "!pip install huggingface_hub" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-kO80j1sLeOC", "outputId": "c762942b-cf5c-414b-8d9c-730dbd5e18f7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting huggingface_hub\n", " Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n", "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/268.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m153.6/268.8 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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"from transformers import AutoTokenizer\n", "from transformers import AutoConfig\n", "from transformers import AutoModelForSequenceClassification\n", "from transformers import TFAutoModelForSequenceClassification\n", "from transformers import IntervalStrategy\n", "from transformers import TrainingArguments\n", "from transformers import EarlyStoppingCallback\n", "from transformers import pipeline\n", "from transformers import TrainingArguments\n", "from transformers import Trainer\n", "from torch import nn" ], "metadata": { "id": "yA8MIQT6jTGn" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "notebook_login()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "877b3c20e62c4d899c1530521b4155be", "6308ff490e5a4634b046ae9db5186326", "45fa003681d642fba6305195a0b6c858", "f0ce330e1a3f4493b1288034f262ca39", "ea1a636092d944d6abaca13f3c2aea8a", "ffb9d30118c14af78f8a73c4680a48bb", "f30154f3db2b4084b9af401cd027f68b", "a42d078349da4954b1ae2620dd76dda0", "11be4b38dcdc46af9c9c18c34fa10548", "3cd70136f3414a79a27842e3477aec83", "0bd6b710c5364028987eb0716c79dcb6", "022a4d1c0e1643cd821a8c27031aad28", "ba138a095f9d4ca897afea2b6b7a5091", "37b8d094ba424f7e822730d9524b5c0e", "f9b16138587445f1b8ad4f40a8791fa1", "fa5c26d14c9c4cc1b3e3c1bdb914ed60", "8e211bb7af104a24892721e39f0c08f1", "a8647295995b40c49303520266d51040", "c49ad91312f54ff8aa29dff8f64047c0", "09f566728d884b1e973aafd866dfe958", "2a420ba232624f03beca036afff66a3e", "8d2df5528a45474eba41962fafc158cf", "e7e1180da7954acbad3c926c02e8855b", "5d5c23a4d6414e959208f57b651b5cf4", "e644233616fc454cb6718097c9748918", "5a70c088d51a4243bc08d3efce4791ea", "7be93fa736ae4012986e787f521b19ed", "40b08f4cc9394613b024f1e6ae4d5fd1", "f448ee00267141f5bf219c858b20fd36", "c02b32c46c174fa894428de29bfe78b1", "90c37dbd24c94f098824dab82e37734d", "019748b3ce9f4fc9938bf79f43f973fc" ] }, "id": "MBAKz5-sjgw5", "outputId": "799a2b85-ea99-4378-b73d-e35df5749c3b" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
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safe_textlabelagreement
9989stop worrying theyll lose vaccination missing ...1.01.000000
9990dawg said gone rich one day eazybash mmr theca...0.01.000000
9991one half hour isa pdx mmr0.01.000000
9992doesnt want vaccinate kid would call fucking r...1.00.666667
9993ill say glad mom gave mmr vaccination little r...1.01.000000
9994living time sperm used waste jenny mccarthy be...1.01.000000
9995spite measles outbreak judge mi threatens put ...1.00.666667
9996interesting trend child immunization oklahoma ...0.01.000000
9997cdc say measles highest level decade returned u0.01.000000
9998pneumonia vaccine woman w risk pulmonary disea...1.00.666667
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safe_textlabelagreement
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"execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Generate random UUID string for tweet_id\n", "'''UUIDs are often used in software applications for various purposes such as generating unique IDs for entities,\n", "tracking unique user sessions, or creating unique file names'''\n", "rand_tweet_id = str(uuid.uuid4())\n", "\n", "# Select row by index and assign values to columns\n", "row_index = 4799\n", "df.loc[row_index, 'tweet_id'] = rand_tweet_id\n", "df.loc[row_index, 'label'] = 1\n", "df.loc[row_index, 'agreement'] = 0.666667\n", "\n", "# Use .iloc[] and .iat[] to select and update safe_text column\n", "df.iloc[row_index, df.columns.get_loc('safe_text')] = df.iloc[row_index, 1]" ], "metadata": { "id": "dauhh14Fi3K8" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df[df.duplicated()].sum()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LPfhfoOyi8Wn", "outputId": "41db2aea-61b9-470a-bfb5-e0efe6805a61" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "safe_text gop stop blaming immigrant first ebola measles...\n", "label 83.0\n", "agreement 319.0\n", "tweet_id 0\n", "dtype: object" ] }, "metadata": {}, "execution_count": 29 } ] }, { "cell_type": "code", "source": [ "df['label'].value_counts()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kzjJDW9Ji9Cd", "outputId": "3f193d1b-e114-4ad5-e2a2-f2422800b9c7" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " 0.0 4907\n", " 1.0 4054\n", "-1.0 1038\n", "Name: label, dtype: int64" ] }, "metadata": {}, "execution_count": 30 } ] }, { "cell_type": "code", "source": [ "# Find the maximum count among all classes\n", "max_class_count = df['label'].value_counts().max()\n", "\n", "# Group the dataframe by 'label'\n", "grouped = df.groupby('label')\n", "\n", "# Sample each group to match the max_class_count\n", "balanced_df = grouped.apply(lambda x: x.sample(max_class_count, replace=True)).reset_index(drop=True)\n", "\n", "# The 'balanced_df' now contains an equal number of instances for each class\n", "print(balanced_df['label'].value_counts())\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5NU6_vpjjAYv", "outputId": "2cf3cacc-f1a8-4421-d208-dead9b91e2a4" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "-1.0 4907\n", " 0.0 4907\n", " 1.0 4907\n", "Name: label, dtype: int64\n" ] } ] }, { "cell_type": "code", "source": [ "# Split the train data => {train, eval}\n", "train, eval = train_test_split(balanced_df, test_size=0.2, random_state=40)" ], "metadata": { "id": "UzhuKl2djORg" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "train.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qhtgVoCljTSj", "outputId": "60e8c520-aa4f-4915-c6fd-624ba44e78e4" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Int64Index: 11776 entries, 7154 to 11590\n", "Data columns (total 4 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 safe_text 11776 non-null object \n", " 1 label 11776 non-null float64\n", " 2 agreement 11776 non-null float64\n", " 3 tweet_id 1 non-null object \n", "dtypes: float64(2), object(2)\n", "memory usage: 460.0+ KB\n" ] } ] }, { "cell_type": "code", "source": [ "eval.info()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Bcjc98CujTxL", "outputId": "7fff70e3-bf46-4058-ddc2-8a8468254a63" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Int64Index: 2945 entries, 12921 to 10715\n", "Data columns (total 4 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 safe_text 2945 non-null object \n", " 1 label 2945 non-null float64\n", " 2 agreement 2945 non-null float64\n", " 3 tweet_id 0 non-null object \n", "dtypes: float64(2), object(2)\n", "memory usage: 115.0+ KB\n" ] } ] }, { "cell_type": "code", "source": [ "# Save splitted subsets\n", "#train.to_csv(\"/data/train_subset.csv\", index=False)\n", "#eval.to_csv(\"/data/eval_subset.csv\", index=False)\n", "\n", "train.to_csv(\"/content/drive/MyDrive/LP5/Career_Accelerator_P5-NLP/train_subset.csv\", index=False)\n", "eval.to_csv(\"/content/drive/MyDrive/LP5/Career_Accelerator_P5-NLP/eval_subset.csv\", index=False)" ], "metadata": { "id": "0G8HwbvkR5lm" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "dataset = load_dataset('csv',\n", " data_files={'train': '/content/drive/MyDrive/LP5/Career_Accelerator_P5-NLP/train_subset.csv',\n", " 'eval': '/content/drive/MyDrive/LP5/Career_Accelerator_P5-NLP/eval_subset.csv'}, encoding = \"ISO-8859-1\")" ], "metadata": { "colab": { 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 199\u001b[0m \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 200\u001b[0;31m Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m 201\u001b[0m \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m 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order and get the name and state dict of the best model\n", "best_model = results_df.loc[results_df['f1_macro'].idxmin()]\n", "\n", "print(best_model)\n" ], "metadata": { "id": "cbR7ef0aluER" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "trainer_BERT.push_to_hub(\"MissChloe/PQ_Sentiment-Analysis_Bert_Model\")" ], "metadata": { "id": "yoDyJxhgmZAw" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "tokenizer_BERT.push_to_hub(\"MissChloe/PQ_Sentiment-Analysis_Bert_Model\")" ], "metadata": { "id": "xK80GEDhme5Y" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "model_BERT.push_to_hub(\"MissChloe/PQ_Sentiment-Analysis_Bert_Model\")" ], "metadata": { "id": "93wG67rkmmBN" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Load the tokenizer\n", "tokenizer = tokenizer_BERT.from_pretrained(\"MissChloe/PQ_Sentiment-Analysis_Bert_Model\")\n", "\n", "# Load the fine-tuned model\n", "model = pipeline(\"text-classification\", model=\"MissChloe/PQ_Sentiment-Analysis_Bert_Model\", tokenizer=tokenizer)" ], "metadata": { "id": "aSPSAYBImvp0" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "label_map = {0: \"negative\", 1: \"neutral\", 2: \"positive\"}\n", "\n", "# Make predictions on some example text\n", "result = model(\"I love these covid vaccines.\")\n", "\n", "# Map the numerical label to the corresponding class name\n", "result[0][\"label\"] = label_map[int(result[0][\"label\"].split(\"_\")[1])]\n", "\n", "# Print the predicted label and score\n", "print(result)" ], "metadata": { "id": "yhIG_vv0m84W" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "!pip freeze > /content/drive/MyDrive/NLP/NLP2/M1/requirements.txt" ], "metadata": { "id": "_S-Qy4PKnEIz" }, "execution_count": null, "outputs": [] } ] }