diff --git a/Panisson_Text_Representation_Learning/4.1.Transformer-Train.ipynb b/Panisson_Text_Representation_Learning/4.1.Transformer-Train.ipynb new file mode 100644 index 0000000..f4d28af --- /dev/null +++ b/Panisson_Text_Representation_Learning/4.1.Transformer-Train.ipynb @@ -0,0 +1,6049 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "---\n", + "# SUMMER SCHOOL 2022:\n", + "## Text mining and Natural Language Processing for Computational Social Sciences\n", + "\n", + "### SocialComQuant Project - Online Teaching Module: Text Representation Learning\n", + "\n", + "### André Panisson\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Text Classification with Transformers\n", + "\n", + "In this notebook, we're going to experiment with Transformers.\n", + "\n", + "The transformers architecture published in the research paper titled \"Attention Is All You Need\" is one of the most influential papers in deep learning and natural language processing. Transformer models have taken the world of Natural Language Processing by storm, changing the field by leaps and bounds. New, bigger, and better models seem to crop up almost every month, setting new benchmarks in performance across a wide variety of tasks.\n", + "\n", + "This notebook is intended as a straightforward guide to utilizing these awesome models for text classification tasks. As such, we won’t be talking about the theory behind the networks, or how they work under the hood.\n", + "\n", + "One important conceptual difference with Word2Vec models is that Transformers, even though they also represent the words as vectors, these vectors are contextual. It means that **the vector representation for a specific word changes depending on its context**." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell will run only if you are executing this notebook on Google Colab:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "p1nY7tCKf22f" + }, + "outputs": [], + "source": [ + "import os\n", + "RunningInCOLAB = 'COLAB_GPU' in os.environ\n", + "\n", + "if RunningInCOLAB:\n", + " !pip install datasets transformers sentencepiece" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "import transformers\n", + "transformers.logging.set_verbosity_error()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Load the Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Reusing dataset imdb (/home/panisson/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1)\n", + "Loading cached split indices for dataset at /home/panisson/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1/cache-cc7bf445f84ad2a1.arrow and /home/panisson/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1/cache-6987fd269768b1cf.arrow\n" + ] + } + ], + "source": [ + "from datasets import load_dataset\n", + "\n", + "imdb = load_dataset(\"imdb\", split=\"train\")\n", + "imdb = imdb.train_test_split(train_size=5000, test_size=5000, seed=42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tokenization" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import DistilBertTokenizerFast\n", + "\n", + "tokenizer = DistilBertTokenizerFast.from_pretrained(\"distilbert-base-uncased\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look inside the tokenizer. First, let's look into the vocabulary." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30522" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vocabulary = tokenizer.vocab\n", + "len(vocabulary)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6429" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vocabulary['amazing']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we take a sample document, we can see that each piece of the string is transformed in the corresponding identifier in the dictionary:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'

How this film ever got a 6 star average is beyond me. The script is so banal, and frankly an insult to whomevers life it is based upon. The cinematography comes straight from the slick world of advertising, and the talented Ridley Scott should be ashamed. Demi Moore however, shows none a surprise by participating in this film, if one looks at her tracklist. All in all, a \"high concept\" style film that even Don Simpson would be ashamed of.'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample = imdb['train'][\"text\"][10]\n", + "sample" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "tokenized_sample = tokenizer(sample, truncation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[101, 1026, 7987, 1013, 1028, 1026, 7987, 1013, 1028, 2129, 2023, 2143, 2412, 2288, 1037, 1020, 2732, 2779, 2003, 3458, 2033, 1012, 1996, 5896, 2003, 2061, 7221, 2389, 1010, 1998, 19597, 2019, 15301, 2000, 3183, 22507, 2015, 2166, 2009, 2003, 2241, 2588, 1012, 1996, 16434, 3310, 3442, 2013, 1996, 13554, 2088, 1997, 6475, 1010, 1998, 1996, 10904, 20608, 3660, 2323, 2022, 14984, 1012, 27668, 5405, 2174, 1010, 3065, 3904, 1037, 4474, 2011, 8019, 1999, 2023, 2143, 1010, 2065, 2028, 3504, 2012, 2014, 2650, 9863, 1012, 2035, 1999, 2035, 1010, 1037, 1000, 2152, 4145, 1000, 2806, 2143, 2008, 2130, 2123, 9304, 2052, 2022, 14984, 1997, 1012, 102]\n" + ] + } + ], + "source": [ + "print(tokenized_sample['input_ids'])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "id_to_word = {value: key for (key, value) in vocabulary.items()}" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['[CLS]', '<', 'br', '/', '>', '<', 'br', '/', '>', 'how', 'this', 'film', 'ever', 'got', 'a', '6', 'star', 'average', 'is', 'beyond', 'me', '.', 'the', 'script', 'is', 'so', 'ban', '##al', ',', 'and', 'frankly', 'an', 'insult', 'to', 'whom', '##ever', '##s', 'life', 'it', 'is', 'based', 'upon', '.', 'the', 'cinematography', 'comes', 'straight', 'from', 'the', 'slick', 'world', 'of', 'advertising', ',', 'and', 'the', 'talented', 'ridley', 'scott', 'should', 'be', 'ashamed', '.', 'demi', 'moore', 'however', ',', 'shows', 'none', 'a', 'surprise', 'by', 'participating', 'in', 'this', 'film', ',', 'if', 'one', 'looks', 'at', 'her', 'track', '##list', '.', 'all', 'in', 'all', ',', 'a', '\"', 'high', 'concept', '\"', 'style', 'film', 'that', 'even', 'don', 'simpson', 'would', 'be', 'ashamed', 'of', '.', '[SEP]']\n" + ] + } + ], + "source": [ + "print([id_to_word[i] for i in tokenized_sample['input_ids']])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's apply the tokenization to the whole dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 151, + "referenced_widgets": [ + "41a3f7548cc74be08bdb9ceee4846aa9", + "8455a75da21e4fe79bbdab6aefb8b317", + "e71de36690d74519a3faeb8147d4c81b", + "fad61b2546ae40899c2986e5aa078d29", + "cf584bfcb0e241d29610c23f8d4cc5b5", + "a414bae9d1164faf9b7c2e3ee262e89c", + "0ff005f6c90f46a5a357b165fd9587ce", + "40b28386f9ae4d39abd5dac4018df142", + "cc299ca5918d49eb84b59a0956f9d092", + "29f38c25cffb40ed8b9b22287f03d13d", + "4ac0a0cbc3ed417da1f58d185f0f60cf", + "c8c59233e5454950b29249ba3ce35e44", + "f8391f54a4f14fb9b7883047d9c804fb", + "079297fda7e440d499f0f12e59945ea2", + "04237b2791ef4916bd4d726dcc557749", + "865d7dda646d4d7a9c5e91fac22cdc52", + "6a481a55a8184f969d37b9e1833fc314", + "537f6627bb144e148882ff9a591e5b9e", + "d26687a7e9e0441ba15315c850435966", + "be777c99509e405590a9fb2a95e2f76f", + "77fb4ddff8434dc59056bf9e3c6bd135", + "85b14a972d3144b692ba1ada276fe332", + "053ee2dbaf7549ef9321c073b08fa46f", + "d1c74f905e0c4f9f8c8c334e27207474", + "4808c43575e54fa2b491bae4e08a051b", + "d590899ad25345c3a05099365b2ecbbf", + "4237e589503945e2a46a9bd8f9996865", + "385b119f37d84328947eb827f51ccd90", + "ee401edc25804f81af69d749758f2fc6", + "a3497096008049b293e44c9f15f7fcaa", + "2f3d6ce682ab40d1abe985194de88238", + "e88a8335fb51472a9f462a53f8880bbe", + "e0b94653424a4c7880635988a56f0b09" + ] + }, + "id": "vMoVMwpYhAcn", + "outputId": "c43bccc2-ab2b-4ea6-a4d0-054838e41427" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading cached processed dataset at /home/panisson/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1/cache-60bf6fe36a3a0a8a.arrow\n", + "Loading cached processed dataset at /home/panisson/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1/cache-1f0a2eb601cfb462.arrow\n" + ] + } + ], + "source": [ + "def preprocess_function(examples):\n", + " return tokenizer(examples[\"text\"], truncation=True)\n", + "\n", + "tokenized_imdb = imdb.map(preprocess_function, batched=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model we are going to train is limited to inputs with maximum 512 tokens. Also, all inputs must be arrays with exactly 512 entries.\n", + "\n", + "Therefore, if a document has less than 512 tokens, the remaining positions are padded with zeros. If a documenti has more than 512 tokens, the input is truncated to exactly 512 tokens." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "AkhbAz96hCwx" + }, + "outputs": [], + "source": [ + "from transformers import DataCollatorWithPadding\n", + "\n", + "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([10, 512])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_collator(tokenizer(imdb['train'][\"text\"][:10], truncation=True))['input_ids'].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre-trained model\n", + "\n", + "First, load a pre-trained model.\n", + "\n", + "DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion.\n", + "\n", + "This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import AutoModel" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "model = AutoModel.from_pretrained(\"distilbert-base-uncased\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "tokenized_sample = tokenizer(sample, truncation=True, return_tensors='pt')\n", + "\n", + "output = model(**tokenized_sample)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With this model, every token of the input document is represented as a vector of 768 numbers. The vector representation for each token depends on the token's context." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([1, 106, 768])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output.last_hidden_state.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will adapt this model for a classification task:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from transformers import AutoModelForSequenceClassification\n", + "\n", + "model = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Train (fine-tune) the model\n", + "\n", + "Finally, we set the parameters for training, and train it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 158, + "referenced_widgets": [ + "9c680354e97346b18962b91c697b6123", + "31320ca1da414247ba3e4d0e69e0716b", + "0161243878cd4d28b1557b17c027455e", + "17fda5e0d08944778c1b128eda767b4b", + "289c927f157e4366a8ecd99351d4c1c8", + "1f021180a95247f7b036120a74e85fc8", + "c61782e4b51b416f927379d400437506", + "1f3e2b7911f84423804242b0615faadf", + "6ccbd15442824edd893b13a66464e845", + "c258249b4b53411e82335034ebadacea", + "030e00865b3a47609aec8698ef3b3642" + ] + }, + "id": "jLKahOZQhES-", + "outputId": "a9c1d64c-d733-40f1-d074-51a22c77e406" + }, + "outputs": [], + "source": [ + "from transformers import TrainingArguments, Trainer\n", + "\n", + "training_args = TrainingArguments(\n", + " output_dir=\"./results\",\n", + " learning_rate=2e-5,\n", + " per_device_train_batch_size=8,\n", + " per_device_eval_batch_size=8,\n", + " num_train_epochs=1,\n", + " weight_decay=0.01,\n", + ")\n", + "\n", + "trainer = Trainer(\n", + " model=model,\n", + " args=training_args,\n", + " train_dataset=tokenized_imdb[\"train\"],\n", + " eval_dataset=tokenized_imdb[\"test\"],\n", + " tokenizer=tokenizer,\n", + " data_collator=data_collator,\n", + ")\n", + "\n", + "# FIX - wrap collator in a dictionary\n", + "old_collator = trainer.data_collator\n", + "trainer.data_collator = lambda data: dict(old_collator(data))\n", + "# End FIX\n", + "\n", + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After training, we should save it to disk, so we can load and use it in classification tasks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model.save_pretrained('distilbert-trained')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": 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+ "## Text mining and Natural Language Processing for Computational Social Sciences\n", + "\n", + "### SocialComQuant Project - Online Teaching Module: Text Representation Learning\n", + "\n", + "### André Panisson\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Text Classification with Transformers (part 2)\n", + "\n", + "In this notebook, we're going to test a model that was trained previously." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell will run only if you are executing this notebook on Google Colab:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "p1nY7tCKf22f" + }, + "outputs": [], + "source": [ + "import os\n", + "RunningInCOLAB = 'COLAB_GPU' in os.environ\n", + "\n", + "if RunningInCOLAB:\n", + " !pip install datasets transformers sentencepiece" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "import transformers\n", + "transformers.logging.set_verbosity_error()\n", + "import datasets\n", + "datasets.logging.set_verbosity_error()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "env: CUDA_VISIBLE_DEVICES=1\n" + ] + } + ], + "source": [ + "%env CUDA_VISIBLE_DEVICES 1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Load the Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 201, + "referenced_widgets": [ + "16e7518ec8724202bdf56afafec03031", + "79fb777c4a2749ca845e7fdec2f0481f", + "059dfb0ea145495eb0a7f8a4fb2b9545", + 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split=\"train\")\n", + "imdb = imdb.train_test_split(train_size=5000, test_size=5000, seed=42)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import DistilBertForSequenceClassification, DistilBertTokenizerFast, pipeline\n", + "\n", + "tokenizer = DistilBertTokenizerFast.from_pretrained(\"distilbert-base-uncased\")\n", + "model = DistilBertForSequenceClassification.from_pretrained('distilbert-trained/', num_labels=2)\n", + "pipe = pipeline(model=model, tokenizer=tokenizer, task=\"text-classification\", device=0,\n", + " max_length=512, truncation=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "text = \"\"\"\n", + "This film is about a struggling actor trying to find satisfaction in life, especially love which he has not\n", + "had a taste of for 5 years.\n", + "It basically is a film featuring a man with very poor social skills, \n", + "and he says wrong things all the time. The plot is hollow and contrived.\n", + "The main character, James, is lonely, but this theme of loneliness is not adequately explored.\n", + "It is more like an empty statement which other subplots stem from.\n", + "Sadness and disappointment after being dumped are superficial. There is a serious lack of emotions in the film.\n", + "It is not funny as a comedy either. There are some funny one liners but that is it.\n", + "It lacks the happy and uplifting atmosphere to infect people with happy mood.\n", + "I don't find \"I Want Someone to Eat Cheese With\" funny.\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'label': 'LABEL_0', 'score': 0.9838571548461914}]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pipe(text)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "pred = pipe(imdb['test']['text'])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "pred_labels = [(0 if p['label']=='LABEL_0' else 1) for p in pred]\n", + "true_labels = imdb['test']['label']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + 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Processing for Computational Social Sciences\n", + "\n", + "### SocialComQuant Project - Online Teaching Module: Text Representation Learning\n", + "\n", + "### André Panisson\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "## Preparation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following cell will run only if you are executing this notebook on Google Colab:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "p1nY7tCKf22f" + }, + "outputs": [], + "source": [ + "import os\n", + "RunningInCOLAB = 'COLAB_GPU' in os.environ\n", + "\n", + "if RunningInCOLAB:\n", + " !pip install datasets transformers sentencepiece" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "import transformers\n", + "transformers.logging.set_verbosity_error()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the model for Translation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "WecH-aGzhL1T" + }, + "outputs": [], + "source": [ + "from transformers import MarianMTModel, MarianTokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85, + "referenced_widgets": [ + "8f584cb1932445cd8e053974d33ebf37", + "07ce4026c831418aaa45131c2755890c", + "ae53c6e961b64113b6c51692e35610fe", + "c274f88eb5424c31be65b3ed0e28d9c8", + "db7c0a0a875449a9a5e87c0e4d91e94b", + "7fbbe104c03c4d92a9cd6ca0e40d3a7e", + "9d1d301841ef4936ae1b278f2bb4adac", + "87888217374f44e79b1e7afbd9fc7cf8", + "ca5c5d30375145548c27ff9431c9e5c0", + "3e10f422e8754d4db3e69bd513816d65", + "cb8a88857b2c4fe69e0b612925bfcc9e" + ] + }, + "id": "zqVpYPE2lOBy", + "outputId": "1425e974-8a60-44b2-b698-b1553cd4895e" + }, + "outputs": [ + 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