4-Evaluating LLMs/4_1_rouge_evaluations.ipynb

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        "<a href=\"https://colab.research.google.com/github/peremartra/Large-Language-Model-Notebooks-Course/blob/main/4-Evaluating%20LLMs/4_1_rouge_evaluations.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "<div>\n",
        "    <h1>Large Language Models Projects</a></h1>\n",
        "    <h3>Apply and Implement Strategies for Large Language Models</h3>\n",
        "    <h2>4.1-BLEU,  ROUGE and N-Grams. </h2>\n",
        "    <h3>Evaluating summaries with ROUGE </h3>\n",
        "</div>\n",
        "\n",
        "by [Pere Martra](https://www.linkedin.com/in/pere-martra/)\n",
        "_______\n",
        "Models: t5-base-cnn / t5-base\n",
        "\n",
        "Colab Environment: CPU\n",
        "\n",
        "Keys:\n",
        "* Summary Evaluation.\n",
        "* N-Grams.\n",
        "* Rouge.\n",
        "\n",
        "Related article: [ROUGE Metrics: Evaluating Summaries in Large Language Models](https://pub.towardsai.net/rouge-metrics-evaluating-summaries-in-large-language-models-d200ee7ca0e6)\n",
        "_______\n"
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        "# How to Evaluate Large Language Models for Summarization Using ROUGE.\n",
        "The way we evaluate large language models is quite different from evaluating machine learning models, where metrics like Accuracy, F1 Score, or Recall were commonly used.\n",
        "\n",
        "Metrics for generated language are distinct. Depending on the specific application, different metrics are chosen to assess the model's performance.\n",
        "\n",
        "In this notebook, we will explore the usage of the ROUGE metric to measure the quality of summaries generated by a language model.\n",
        "\n",
        "## What is ROUGE?\n",
        "ROUGE isn't just a single metric; it's a set of metrics that measure the overlap and similarity between the generated summary and a reference summary that serves as a benchmark.\n",
        "\n",
        "It returns fourth individual metrics. The metrics provided are:\n",
        "\n",
        "* ROUGE-1: Measures the overlap of unigrams, or single words.\n",
        "* ROUGE-2: Measures the overlap of bigrams, or pairs of words.\n",
        "* ROUGE-L: Measures the longest common subsequence, rewarding longer shared sequences between the generated and reference summaries.\n",
        "* ROUGE-LSUM: Calculated as the length of the LCS divided by the sum of the lengths of the generated summary and the reference summary.\n",
        "\n",
        "## What are we going to do?\n",
        "We are going to use two T5 models, one of them being the t5-Base model and the other a t5-base fine-tuned  specifically designed for creating summaries.\n",
        "\n",
        "First, we will use a dataset and generate summaries using both models. By comparing the two generated summaries, we can observe whether the fine-tuning has been effective in producing different results. In other words, here we will only determine that the two models exhibit significant differences in summary generation, but we won't know which one might perform better.\n",
        "\n",
        "To determine which model generates better summaries, we will utilize a well-known dataset called 'cnn_dailymail,' which is available in the 'datasets' library.\n",
        "\n",
        "This dataset contains reference summaries that can be used for comparison. We will assess the summaries generated by the two models against these reference summaries.\n",
        "\n",
        "The model that obtains a higher ROUGE score will be considered the one that produces better summaries.\n",
        "\n",
        "## The models.\n",
        "t5-Base Finnetunned: https://huggingface.co/flax-community/t5-base-cnn-dm\n",
        "\n",
        "t5-Base: https://huggingface.co/t5-base\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q evaluate==0.4.2\n",
        "!pip install -q transformers==4.42.4\n",
        "!pip install -q rouge_score==0.1.2\n",
        "!pip install kaggle\n",
        "#!pip install -q datasets==2.1.0"
      ],
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            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/84.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.14)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.7.4)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.32.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.4)\n",
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            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.7)\n"
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    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n",
        "import evaluate\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "from nltk.tokenize import sent_tokenize"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U414LeQpHFnh",
        "outputId": "8210b0df-dce4-4c91-c4e0-4eb77c84f728"
      },
      "id": "U414LeQpHFnh",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6466b7f5",
      "metadata": {
        "papermill": {
          "duration": 0.013868,
          "end_time": "2023-08-14T22:02:21.4225",
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          "status": "completed"
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        "tags": [],
        "id": "6466b7f5"
      },
      "source": [
        "# Load the Data"
      ]
    },
    {
      "cell_type": "code",
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      "id": "c1bfad0c",
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      "outputs": [],
      "source": [
        "#Import generic libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9463e9f0",
      "metadata": {
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          "status": "completed"
        },
        "tags": [],
        "id": "9463e9f0"
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      "source": [
        "The dataset is available on Kaggle and comprises a collection of technological news articles compiled by MIT. The article text is located in the 'Article Body' column.\n",
        "\n",
        "https://www.kaggle.com/datasets/deepanshudalal09/mit-ai-news-published-till-2023\n",
        "\n",
        "## Importing Dataset from Kaggle"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Yo only need acces to the articles.csv file from the Dataaset, you can download and load it directly, if you prefer to use the API Kaggle you can use the code Below. To use the Kaggle API you will need to hace your kaggle.json file with your keys in the directory /content/drive/MyDrive/kaggle"
      ],
      "metadata": {
        "id": "Zm2Bv97P5VFc"
      },
      "id": "Zm2Bv97P5VFc"
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ['KAGGLE_CONFIG_DIR'] = '/content/drive/MyDrive/kaggle'"
      ],
      "metadata": {
        "id": "-_3a0v2GghP1"
      },
      "id": "-_3a0v2GghP1",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d deepanshudalal09/mit-ai-news-published-till-2023"
      ],
      "metadata": {
        "id": "r75XOrBuhY6m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8aff021a-4c29-4825-c121-d52a30b0bea1"
      },
      "id": "r75XOrBuhY6m",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.6.17 / client 1.6.14)\n",
            "Dataset URL: https://www.kaggle.com/datasets/deepanshudalal09/mit-ai-news-published-till-2023\n",
            "License(s): unknown\n",
            "Downloading mit-ai-news-published-till-2023.zip to /content\n",
            "  0% 0.00/1.90M [00:00<?, ?B/s]\n",
            "100% 1.90M/1.90M [00:00<00:00, 128MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "file_path = '/content/mit-ai-news-published-till-2023.zip'\n",
        "with zipfile.ZipFile(file_path, 'r') as zip_ref:\n",
        "   zip_ref.extractall('/content/drive/MyDrive/kaggle')"
      ],
      "metadata": {
        "id": "zVhE7Sr_h8cf"
      },
      "id": "zVhE7Sr_h8cf",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading Dataset"
      ],
      "metadata": {
        "id": "wXm0WI235KRV"
      },
      "id": "wXm0WI235KRV"
    },
    {
      "cell_type": "code",
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        "tags": [],
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      "outputs": [],
      "source": [
        "news = pd.read_csv('/content/drive/MyDrive/kaggle/articles.csv')\n",
        "DOCUMENT=\"Article Body\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e13d6679",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T22:02:25.305575Z",
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        "tags": [],
        "id": "e13d6679"
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      "outputs": [],
      "source": [
        "#Because it is just a course we select a small portion of News.\n",
        "MAX_NEWS = 3\n",
        "subset_news = news.head(MAX_NEWS)"
      ]
    },
    {
      "cell_type": "code",
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To streamline this process, scientists often use machine learning to predict molecular properties and narrow down the molecules they need to synthesize and test in the lab.', 'Researchers from MIT and the MIT-Watson AI Lab have developed a new, unified framework that can simultaneously predict molecular properties and generate new molecules much more efficiently than these popular deep-learning approaches.', 'To teach a machine-learning model to predict a molecule\\u2019s biological or mechanical properties, researchers must show it millions of labeled molecular structures \\u2014 a process known as training. Due to the expense of discovering molecules and the challenges of hand-labeling millions of structures, large training datasets are often hard to come by, which limits the effectiveness of machine-learning approaches.', 'By contrast, the system created by the MIT researchers can effectively predict molecular properties using only a small amount of data. Their system has an underlying understanding of the rules that dictate how building blocks combine to produce valid molecules. These rules capture the similarities between molecular structures, which helps the system generate new molecules and predict their properties in a data-efficient manner.', 'This method outperformed other machine-learning approaches on both small and large datasets, and was able to accurately predict molecular properties and generate viable molecules when given a dataset with fewer than 100 samples.', '\\u201cOur goal with this project is to use some data-driven methods to speed up the discovery of new molecules, so you can train a model to do the prediction without all of these cost-heavy experiments,\\u201d says lead author Minghao Guo, a computer science and electrical engineering (EECS) graduate student.', 'Guo\\u2019s co-authors include MIT-IBM Watson AI Lab research staff members Veronika Thost, Payel Das, and Jie Chen; recent MIT graduates Samuel Song \\u201923 and Adithya Balachandran \\u201923; and senior author Wojciech Matusik, a professor of electrical engineering and computer science and a member of the MIT-IBM Watson AI Lab, who leads the Computational Design and Fabrication Group within the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the International Conference for Machine Learning.', 'Learning the language of molecules', 'To achieve the best results with machine-learning models, scientists need training datasets with millions of molecules that have similar properties to those they hope to discover. In reality, these domain-specific datasets are usually very small. So, researchers use models that have been pretrained on large datasets of general molecules, which they apply to a much smaller, targeted dataset. However, because these models haven\\u2019t acquired much domain-specific knowledge, they tend to perform poorly.', 'The MIT team took a different approach. They created a machine-learning system that automatically learns the \\u201clanguage\\u201d of molecules \\u2014 what is known as a molecular grammar \\u2014 using only a small, domain-specific dataset. It uses this grammar to construct viable molecules and predict their properties.', 'In language theory, one generates words, sentences, or paragraphs based on a set of grammar rules. You can think of a molecular grammar the same way. It is a set of production rules that dictate how to generate molecules or polymers by combining atoms and substructures.', 'Just like a language grammar, which can generate a plethora of sentences using the same rules, one molecular grammar can represent a vast number of molecules. Molecules with similar structures use the same grammar production rules, and the system learns to understand these similarities.', 'Since structurally similar molecules often have similar properties, the system uses its underlying knowledge of molecular similarity to predict properties of new molecules more efficiently.', '\\u201cOnce we have this grammar as a representation for all the different molecules, we can use it to boost the process of property prediction,\\u201d Guo says.', 'The system learns the production rules for a molecular grammar using reinforcement learning \\u2014 a trial-and-error process where the model is rewarded for behavior that gets it closer to achieving a goal.', 'But because there could be billions of ways to combine atoms and substructures, the process to learn grammar production rules would be too computationally expensive for anything but the tiniest dataset.', 'The researchers decoupled the molecular grammar into two parts. The first part, called a metagrammar, is a general, widely applicable grammar they design manually and give the system at the outset. Then it only needs to learn a much smaller, molecule-specific grammar from the domain dataset. This hierarchical approach speeds up the learning process.', 'Big results, small datasets', 'In experiments, the researchers\\u2019 new system simultaneously generated viable molecules and polymers, and predicted their properties more accurately than several popular machine-learning approaches, even when the domain-specific datasets had only a few hundred samples. Some other methods also required a costly pretraining step that the new system avoids.', 'The technique was especially effective at predicting physical properties of polymers, such as the glass transition temperature, which is the temperature required for a material to transition from solid to liquid. Obtaining this information manually is often extremely costly because the experiments require extremely high temperatures and pressures.', 'To push their approach further, the researchers cut one training set down by more than half \\u2014 to just 94 samples. Their model still achieved results that were on par with methods trained using the entire dataset.', '\\u201cThis grammar-based representation is very powerful. And because the grammar itself is a very general representation, it can be deployed to different kinds of graph-form data. We are trying to identify other applications beyond chemistry or material science,\\u201d Guo says.', 'In the future, they also want to extend their current molecular grammar to include the 3D geometry of molecules and polymers, which is key to understanding the interactions between polymer chains. They are also developing an interface that would show a user the learned grammar production rules and solicit feedback to correct rules that may be wrong, boosting the accuracy of the system.', 'This work is funded, in part, by the MIT-IBM Watson AI Lab and its member company, Evonik.']\",\n          \"['Is it possible to build machine-learning models without machine-learning expertise?', 'Jim Collins, the Termeer Professor of Medical Engineering and Science in the Department of Biological Engineering at MIT and the life sciences faculty lead at the Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic), along with a number of colleagues decided to tackle this problem when facing a similar conundrum. An open-access paper on their proposed solution, called BioAutoMATED, was published on June 21 in Cell Systems.', 'Recruiting machine-learning researchers can be a time-consuming and financially costly process for science and engineering labs. Even with a machine-learning expert, selecting the appropriate model, formatting the dataset for the model, then fine-tuning it can dramatically change how the model performs, and takes a lot of work.', '\\u201cIn your machine-learning project, how much time will you typically spend on data preparation and transformation?\\u201d asks a 2022 Google course on the Foundations of Machine Learning (ML). The two choices offered are either \\u201cLess than half the project time\\u201d or \\u201cMore than half the project time.\\u201d If you guessed the latter, you would be correct; Google states that it takes over 80 percent of project time to format the data, and that\\u2019s not even taking into account the time needed to frame the problem in machine-learning terms.', '\\u201cIt would take many weeks of effort to figure out the appropriate model for our dataset, and this is a really prohibitive step for a lot of folks that want to use machine learning or biology,\\u201d says Jacqueline Valeri, a fifth-year PhD student of biological engineering in Collins\\u2019s lab who is first co-author of the paper.', \\\"BioAutoMATED is an automated machine-learning system that can select and build an appropriate model for a given dataset and even take care of the laborious task of data preprocessing, whittling down a months-long process to just a few hours. Automated machine-learning (AutoML) systems are still in a relatively nascent stage of development, with current usage primarily focused on image and text recognition, but largely unused in subfields of biology, points out first co-author and Jameel Clinic postdoc Luis Soenksen PhD '20.\\\", '\\u201cThe fundamental language of biology is based on sequences,\\u201d explains Soenksen, who earned his doctorate in the MIT Department of Mechanical Engineering. \\u201cBiological sequences such as DNA, RNA, proteins, and glycans have the amazing informational property of being intrinsically standardized, like an alphabet. A lot of AutoML tools are developed for text, so it made sense to extend it to [biological] sequences.\\u201d', 'Moreover, most AutoML tools can only explore and build reduced types of models. \\u201cBut you can\\u2019t really know from the start of a project which model will be best for your dataset,\\u201d Valeri says. \\u201cBy incorporating multiple tools under one umbrella tool, we really allow a much larger search space than any individual AutoML tool could achieve on its own.\\u201d', 'BioAutoMATED\\u2019s repertoire of supervised ML models includes three types: binary classification models (dividing data into two classes), multi-class classification models (dividing data into multiple classes), and regression models (fitting continuous numerical values or measuring the strength of key relationships between variables). BioAutoMATED is even able to help determine how much data is required to appropriately train the chosen model.', '\\\"Our tool explores models that are better-suited for smaller, sparser biological datasets as well as more complex neural networks,\\u201d Valeri says. This is an advantage for research groups with new data that may or may not be suited for a machine learning problem.', '\\\"Conducting novel and successful experiments at the intersection of biology and machine learning can cost a lot of money,\\u201d Soenksen explains. \\\"Currently, biology-centric labs need to invest in significant digital infrastructure and AI-ML trained human resources before they can even see if their ideas are poised to pan out. We want to lower these barriers for domain experts in biology.\\u201d With BioAutoMATED, researchers have the freedom to run initial experiments to assess if it\\u2019s worthwhile to hire a machine-learning expert to build a different model for further experimentation.', 'The open-source code is publicly available and, researchers emphasize, it is easy to run. \\u201cWhat we would love to see is for people to take our code, improve it, and collaborate with larger communities to make it a tool for all,\\u201d Soenksen says. \\u201cWe want to prime the biological research community and generate awareness related to AutoML techniques, as a seriously useful pathway that could merge rigorous biological practice with fast-paced AI-ML practice better than it is achieved today.\\u201d', \\\"Collins, the senior author on the paper, is also affiliated with the MIT Institute for Medical Engineering and Science, the Harvard-MIT Program in Health Sciences and Technology, the Broad Institute of MIT and Harvard, and the Wyss Institute. Additional MIT contributors to the paper include Katherine M. Collins '21; Nicolaas M. Angenent-Mari PhD '21; Felix Wong, a former postdoc in the Department of Biological Engineering, IMES, and the Broad Institute; and Timothy K. Lu, a professor of biological engineering and of electrical engineering and computer science.\\\", 'This work was supported, in part, by a Defense Threat Reduction Agency grant, the Defense Advance Research Projects Agency SD2 program, the Paul G. Allen Frontiers Group, the Wyss Institute for Biologically Inspired Engineering of Harvard University; an MIT-Takeda Fellowship, a Siebel Foundation Scholarship, a CONACyT grant, an MIT-TATA Center fellowship, a Johnson & Johnson Undergraduate Research Scholarship, a Barry Goldwater Scholarship, a Marshall Scholarship, Cambridge Trust, and the National Institute of Allergy and Infectious Diseases of the National Institutes of Health. This work is part of the Antibiotics-AI Project, which is supported by the Audacious Project, Flu Lab, LLC, the Sea Grape Foundation, Rosamund Zander and Hansjorg Wyss for the Wyss Foundation, and an anonymous donor.']\",\n          \"['From cameras to self-driving cars, many of today\\u2019s technologies depend on artificial intelligence to extract meaning from visual information. Today\\u2019s AI technology has artificial neural networks at its core, and most of the time we can trust these AI computer vision systems to see things the way we do \\u2014 but sometimes they falter. According to MIT and IBM research scientists, one way to improve computer vision is to instruct the artificial neural networks that they rely on to deliberately mimic the way the brain\\u2019s biological neural network processes visual images.', 'Researchers led by MIT Professor James DiCarlo, the director of MIT\\u2019s Quest for Intelligence and member of the MIT-IBM Watson AI Lab, have made a computer vision model more robust by training it to work like a part of the brain that humans and other primates rely on for object recognition. This May, at the International Conference on Learning Representations, the team reported that when they trained an artificial neural network using neural activity patterns in the brain\\u2019s inferior temporal (IT) cortex, the artificial neural network was more robustly able to identify objects in images than a model that lacked that neural training. And the model\\u2019s interpretations of images more closely matched what humans saw, even when images included minor distortions that made the task more difficult.', 'Comparing neural circuits', 'Many of the artificial neural networks used for computer vision already resemble the multilayered brain circuits that process visual information in humans and other primates. Like the brain, they use neuron-like units that work together to process information. As they are trained for a particular task, these layered components collectively and progressively process the visual information to complete the task \\u2014 determining, for example, that an image depicts a bear or a car or a tree.', 'DiCarlo and others previously found that when such deep-learning computer vision systems establish efficient ways to solve visual problems, they end up with artificial circuits that work similarly to the neural circuits that process visual information in our own brains. That is, they turn out to be surprisingly good scientific models of the neural mechanisms underlying primate and human vision.', 'That resemblance is helping neuroscientists deepen their understanding of the brain. By demonstrating ways visual information can be processed to make sense of images, computational models suggest hypotheses about how the brain might accomplish the same task. As developers continue to refine computer vision models, neuroscientists have found new ideas to explore in their own work.', '\\u201cAs vision systems get better at performing in the real world, some of them turn out to be more human-like in their internal processing. That\\u2019s useful from an understanding-biology point of view,\\u201d says DiCarlo, who is also a professor of brain and cognitive sciences and an investigator at the McGovern Institute for Brain Research.', 'Engineering a more brain-like AI', 'While their potential is promising, computer vision systems are not yet perfect models of human vision. DiCarlo suspected one way to improve computer vision may be to incorporate specific brain-like features into these models.', 'To test this idea, he and his collaborators built a computer vision model using neural data previously collected from vision-processing neurons in the monkey IT cortex \\u2014 a key part of the primate ventral visual pathway involved in the recognition of objects \\u2014 while the animals viewed various images. More specifically, Joel Dapello, a Harvard University graduate student and former MIT-IBM Watson AI Lab intern; and Kohitij Kar, assistant professor and Canada Research Chair (Visual Neuroscience) at York University and visiting scientist at MIT; in collaboration with David Cox, IBM Research\\u2019s vice president for AI models and IBM director of the MIT-IBM Watson AI Lab; and other researchers at IBM Research and MIT asked an artificial neural network to emulate the behavior of these primate vision-processing neurons while the network learned to identify objects in a standard computer vision task.', '\\u201cIn effect, we said to the network, \\u2018please solve this standard computer vision task, but please also make the function of one of your inside simulated \\u201cneural\\u201d layers be as similar as possible to the function of the corresponding biological neural layer,\\u2019\\u201d DiCarlo explains. \\u201cWe asked it to do both of those things as best it could.\\u201d This forced the artificial neural circuits to find a different way to process visual information than the standard, computer vision approach, he says.', 'After training the artificial model with biological data, DiCarlo\\u2019s team compared its activity to a similarly-sized neural network model trained without neural data, using the standard approach for computer vision. They found that the new, biologically informed model IT layer was\\\\xa0\\u2014 as instructed \\u2014 a better match for IT neural data.\\\\xa0 That is, for every image tested, the population of artificial IT neurons in the model responded more similarly to the corresponding population of biological IT neurons.', 'The researchers also found that the model IT was also a better match to IT neural data collected from another monkey, even though the model had never seen data from that animal, and even when that comparison was evaluated on that monkey\\u2019s IT responses to new images. This indicated that the team\\u2019s new, \\u201cneurally aligned\\u201d computer model may be an improved model of the neurobiological function of the primate IT cortex \\u2014 an interesting finding, given that it was previously unknown whether the amount of neural data that can be currently collected from the primate visual system is capable of directly guiding model development.', 'With their new computer model in hand, the team asked whether the \\u201cIT neural alignment\\u201d procedure also leads to any changes in the overall behavioral performance of the model. Indeed, they found that the neurally-aligned model was more human-like in its behavior \\u2014 it tended to succeed in correctly categorizing objects in images for which humans also succeed, and it tended to fail when humans also fail.', 'Adversarial attacks', 'The team also found that the neurally aligned model was more resistant to \\u201cadversarial attacks\\u201d that developers use to test computer vision and AI systems. In computer vision, adversarial attacks introduce small distortions into images that are meant to mislead an artificial neural network.', '\\u201cSay that you have an image that the model identifies as a cat. Because you have the knowledge of the internal workings of the model, you can then design very small changes in the image so that the model suddenly thinks it\\u2019s no longer a cat,\\u201d DiCarlo explains.', 'These minor distortions don\\u2019t typically fool humans, but computer vision models struggle with these alterations. A person who looks at the subtly distorted cat still reliably and robustly reports that it\\u2019s a cat. But standard computer vision models are more likely to mistake the cat for a dog, or even a tree.', '\\u201cThere must be some internal differences in the way our brains process images that lead to our vision being more resistant to those kinds of attacks,\\u201d DiCarlo says. And indeed, the team found that when they made their model more neurally aligned, it became more robust, correctly identifying more images in the face of adversarial attacks. The model could still be fooled by stronger \\u201cattacks,\\u201d but so can people, DiCarlo says. His team is now exploring the limits of adversarial robustness in humans.', 'A few years ago, DiCarlo\\u2019s team found they could also improve a model\\u2019s resistance to adversarial attacks by designing the first layer of the artificial network to emulate the early visual processing layer in the brain. One key next step is to combine such approaches \\u2014 making new models that are simultaneously neurally aligned at multiple visual processing layers.', 'The new work is further evidence that an exchange of ideas between neuroscience and computer science can drive progress in both fields. \\u201cEverybody gets something out of the exciting virtuous cycle between natural/biological intelligence and artificial intelligence,\\u201d DiCarlo says. \\u201cIn this case, computer vision and AI researchers get new ways to achieve robustness, and neuroscientists and cognitive scientists get more accurate mechanistic models of human vision.\\u201d', 'This work was supported by the MIT-IBM Watson AI Lab, Semiconductor Research Corporation, the U.S. Defense Research Projects Agency, the MIT Shoemaker Fellowship, U.S. Office of Naval Research, the Simons Foundation, and Canada Research Chair Program.']\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Url\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"https://news.mit.edu/2023/learning-language-molecules-predict-properties-0707\",\n          \"https://news.mit.edu/2023/bioautomated-open-source-machine-learning-platform-for-research-labs-0706\",\n          \"https://news.mit.edu/2023/when-computer-vision-works-like-human-brain-0630\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "subset_news.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ad7e9329",
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          "status": "completed"
        },
        "tags": [],
        "id": "ad7e9329"
      },
      "outputs": [],
      "source": [
        "articles = subset_news[DOCUMENT].tolist()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "359993aa",
      "metadata": {
        "papermill": {
          "duration": 0.013052,
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      "source": [
        "# Load the Models and create the summaries\n",
        "\n",
        "Both models are available on Hugging Face, so we will work with the Transformers library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "906947a6",
      "metadata": {
        "execution": {
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          "status": "completed"
        },
        "tags": [],
        "id": "906947a6"
      },
      "outputs": [],
      "source": [
        "model_name_base = \"t5-base\"\n",
        "model_name_finetuned = \"flax-community/t5-base-cnn-dm\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "544b7159",
      "metadata": {
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          "status": "completed"
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        "tags": [],
        "id": "544b7159"
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      "outputs": [],
      "source": [
        "#This function returns the tokenizer and the Model.\n",
        "def get_model(model_id):\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "    model = AutoModelForSeq2SeqLM.from_pretrained(model_id)\n",
        "\n",
        "    return tokenizer, model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fbbaa67f",
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        {
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      "source": [
        "tokenizer_base, model_base = get_model(model_name_base)"
      ]
    },
    {
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      "id": "7934f671",
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        "outputId": "4ec6bf23-b83d-4e0f-801d-affe3d0afb63"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.92k [00:00<?, ?B/s]"
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        {
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          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/892M [00:00<?, ?B/s]"
            ],
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              "model_id": "69c8f45da0ac44759cba0f6956287b5b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer_finetuned, model_finetuned = get_model(model_name_finetuned)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a423d166",
      "metadata": {
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          "duration": 0.015182,
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          "status": "completed"
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        "tags": [],
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      },
      "source": [
        "With both models downloaded and ready, we create a function that will perform the summaries.\n",
        "\n",
        "The function takes fourth parameters:\n",
        "\n",
        "* the list of texts to summarize.\n",
        "* the tokenizer.\n",
        "* the model.\n",
        "* the maximum length for the generated summary"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time"
      ],
      "metadata": {
        "id": "Rs2D656lEzCW"
      },
      "id": "Rs2D656lEzCW",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7fc6d899",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T22:03:35.805553Z",
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        },
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        "tags": [],
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      "source": [
        "def create_summaries(texts_list, tokenizer, model, max_l=125):\n",
        "\n",
        "    # We are going to add a prefix to each article to be summarized\n",
        "    # so that the model knows what it should do\n",
        "    prefix = \"Summarize this news: \"\n",
        "    summaries_list = [] #Will contain all summaries\n",
        "\n",
        "    texts_list = [prefix + text for text in texts_list]\n",
        "\n",
        "    for text in texts_list:\n",
        "\n",
        "        summary=\"\"\n",
        "\n",
        "        #calculate the encodings\n",
        "        input_encodings = tokenizer(text,\n",
        "                                    max_length=1024,\n",
        "                                    return_tensors='pt',\n",
        "                                    padding=True,\n",
        "                                    truncation=True)\n",
        "\n",
        "        # Generate summaries\n",
        "        start = time.time()\n",
        "        output = model.generate(\n",
        "            input_ids=input_encodings.input_ids,\n",
        "            attention_mask=input_encodings.attention_mask,\n",
        "            max_length=max_l,  # Set the maximum length of the generated summary\n",
        "            num_beams=2,     # Set the number of beams for beam search\n",
        "            early_stopping=True\n",
        "        )\n",
        "\n",
        "        #Decode to get the text\n",
        "        summary = tokenizer.batch_decode(output, skip_special_tokens=True)\n",
        "        end = time.time()\n",
        "        #Add the summary to summaries list\n",
        "        elapsed_time = end - start\n",
        "        print(f\"Time taken: {elapsed_time:.3f} seconds\")\n",
        "        summaries_list += summary\n",
        "    return summaries_list\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4ffdb4fa",
      "metadata": {
        "papermill": {
          "duration": 0.015207,
          "end_time": "2023-08-14T22:03:35.84634",
          "exception": false,
          "start_time": "2023-08-14T22:03:35.831133",
          "status": "completed"
        },
        "tags": [],
        "id": "4ffdb4fa"
      },
      "source": [
        "To create the summaries, we call the 'create_summaries' function, passing both the news articles and the corresponding tokenizer and model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ddd00e31",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T22:03:35.879443Z",
          "iopub.status.busy": "2023-08-14T22:03:35.878646Z",
          "iopub.status.idle": "2023-08-14T22:04:05.971552Z",
          "shell.execute_reply": "2023-08-14T22:04:05.970547Z"
        },
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          "duration": 30.112965,
          "end_time": "2023-08-14T22:04:05.974752",
          "exception": false,
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          "status": "completed"
        },
        "tags": [],
        "id": "ddd00e31",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2cd74231-a5d5-40bc-b34c-375253e6a7e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken: 14.084 seconds\n",
            "Time taken: 22.327 seconds\n",
            "Time taken: 18.965 seconds\n"
          ]
        }
      ],
      "source": [
        "# Creating the summaries for both models.\n",
        "summaries_base = create_summaries(articles,\n",
        "                                  tokenizer_base,\n",
        "                                  model_base)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2002eec6",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T22:04:06.009738Z",
          "iopub.status.busy": "2023-08-14T22:04:06.008788Z",
          "iopub.status.idle": "2023-08-14T22:04:37.085514Z",
          "shell.execute_reply": "2023-08-14T22:04:37.084489Z"
        },
        "papermill": {
          "duration": 31.095807,
          "end_time": "2023-08-14T22:04:37.088051",
          "exception": false,
          "start_time": "2023-08-14T22:04:05.992244",
          "status": "completed"
        },
        "tags": [],
        "id": "2002eec6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e3d01a9-8264-4476-fd3d-e8baba7538e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken: 14.765 seconds\n",
            "Time taken: 18.770 seconds\n",
            "Time taken: 23.711 seconds\n"
          ]
        }
      ],
      "source": [
        "summaries_finetuned = create_summaries(articles,\n",
        "                                      tokenizer_finetuned,\n",
        "                                      model_finetuned)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "df297d1b",
      "metadata": {
        "execution": {
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        },
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        "id": "df297d1b",
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          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['MIT and MIT-Watson AI Lab have developed a unified framework. the system can simultaneously predict molecular properties and generate new molecules. it uses this grammar to construct viable molecules and predict their properties.',\n",
              " '\\'BioAutoMATED\\' is an automated machine-learning system that can select and build an appropriate model for a given dataset. it can even take care of the laborious task of data preprocessing, whittling down a months-long process to just a few hours. \\'\"We want to lower these barriers for a lot of folks that want to use machine learning or biology,\" says first co-author Jacqueline Valeri.',\n",
              " \"MIT and IBM research scientists have made a computer vision model more robust by training it to work like a part of the brain that humans and other primates rely on for object recognition. 'we asked the artificial neural network to make the function of one of your inside simulated “neural” layers as similar as possible to the corresponding biological neural layer,' says MIT professor.\"]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "summaries_base"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "887bcc1a",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T22:04:37.163043Z",
          "iopub.status.busy": "2023-08-14T22:04:37.16222Z",
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          "shell.execute_reply": "2023-08-14T22:04:37.168144Z"
        },
        "papermill": {
          "duration": 0.026038,
          "end_time": "2023-08-14T22:04:37.171289",
          "exception": false,
          "start_time": "2023-08-14T22:04:37.145251",
          "status": "completed"
        },
        "tags": [],
        "id": "887bcc1a",
        "outputId": "15555597-4644-4385-91a3-84eb78d06fac",
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          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Researchers created a machine-learning system that automatically learns the \"language\" of molecules using only a small, domain-specific dataset. The system learns to construct viable molecules and predict their properties. Computational design and Fabrication Group will be presented at the International Conference for Machine Learning.',\n",
              " \"Automated machine-learning system can select and build an appropriate model for a given dataset. 'BioAutoMATED' is an automated machine-learning system. The tool includes binary classification models, multi-class classification models, and more complex neural networks.\",\n",
              " \"MIT and IBM researchers have found that artificial neural networks resemble the multilayered brain circuits that process visual information in humans and other primates. 'We asked it to do both of those things as well as the standard, computer vision approach,' said one expert. The network found to be more robust by training it to work like a part of the brain that humans rely on for object recognition.\"]"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "summaries_finetuned"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5feaed8e",
      "metadata": {
        "papermill": {
          "duration": 0.015366,
          "end_time": "2023-08-14T22:04:37.20224",
          "exception": false,
          "start_time": "2023-08-14T22:04:37.186874",
          "status": "completed"
        },
        "tags": [],
        "id": "5feaed8e"
      },
      "source": [
        "At first glance, it's evident that the summaries are different.\n",
        "\n",
        "However, it's challenging to determine which one is better.\n",
        "\n",
        "It's even difficult to discern whether they are significantly distinct or if there are just subtle differences between them.\n",
        "\n",
        "This is what we are going to verify now using ROUGE. When comparing the summaries of one model with those of the other, we don't get an idea of which one is better, but rather an idea of how much the summaries have changed with the fine-tuning applied to the model."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6d77e02c",
      "metadata": {
        "papermill": {
          "duration": 0.015293,
          "end_time": "2023-08-14T22:04:37.23328",
          "exception": false,
          "start_time": "2023-08-14T22:04:37.217987",
          "status": "completed"
        },
        "tags": [],
        "id": "6d77e02c"
      },
      "source": [
        "# ROUGE\n",
        "Let's load the ROUEGE evaluator."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ffc7d23e",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T15:44:38.982027Z",
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          "shell.execute_reply.started": "2023-08-14T15:44:38.981986Z"
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        },
        "tags": [],
        "id": "ffc7d23e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "c2b7129c42754993945ee62dbd0ee65c",
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        "outputId": "6cff2199-9594-4e1a-8f58-cb45d1ab5260"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        }
      ],
      "source": [
        "#With the function load of the library evaluate\n",
        "#we create a rouge_score object\n",
        "rouge_score = evaluate.load(\"rouge\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "80e413b0",
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      },
      "source": [
        "Calculating ROUGE is as simple as calling the *compute* function of the *rouge_score* object we created earlier. This function takes the texts to compare as arguments and a third value *use_stemmer*, which indicates whether it should use *stemmer* or full words for the comparison.\n",
        "\n",
        "A *stemmer* is the base of the word. Transform differents forms of a word in a same base.\n",
        "\n",
        "Some samples of steammer are:\n",
        "* Jumping -> Jump.\n",
        "* Running -> Run.\n",
        "* Cats -> Cat."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d42c5f2f",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T16:04:20.076601Z",
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      },
      "outputs": [],
      "source": [
        "def compute_rouge_score(generated, reference):\n",
        "\n",
        "    #We need to add '\\n' to each line before send it to ROUGE\n",
        "    generated_with_newlines = [\"\\n\".join(sent_tokenize(s.strip())) for s in generated]\n",
        "    reference_with_newlines = [\"\\n\".join(sent_tokenize(s.strip())) for s in reference]\n",
        "\n",
        "    return rouge_score.compute(\n",
        "        predictions=generated_with_newlines,\n",
        "        references=reference_with_newlines,\n",
        "        use_stemmer=True,\n",
        "\n",
        "    )"
      ]
    },
    {
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      "execution_count": null,
      "id": "6eab715f",
      "metadata": {
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        },
        "outputId": "035ce6fd-25a5-44ca-fff2-15b980e6a018"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'rouge1': 0.47018752391886715,\n",
              " 'rouge2': 0.3209013209013209,\n",
              " 'rougeL': 0.34330271718331423,\n",
              " 'rougeLsum': 0.44692881745120555}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "compute_rouge_score(summaries_base, summaries_finetuned)"
      ]
    },
    {
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      "id": "8a26c505",
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        },
        "tags": [],
        "id": "8a26c505"
      },
      "source": [
        "We can see that there is a difference between the two models when performing summarization.\n",
        "\n",
        "For example, in ROUGE-1, the similarity is 47%, while in ROUGE-2, it's a 32%. This indicates that the results are different, with some similarities but differents enough.\n",
        "\n",
        "However, we still don't know which model is better since we have compared them to each other and not to a reference text. But at the very least, we know that the fine-tuning process applied to the second model has significantly altered its results."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bb4d432c",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-07T17:21:28.296636Z",
          "iopub.status.busy": "2023-08-07T17:21:28.296173Z",
          "iopub.status.idle": "2023-08-07T17:21:28.301906Z",
          "shell.execute_reply": "2023-08-07T17:21:28.300702Z",
          "shell.execute_reply.started": "2023-08-07T17:21:28.296601Z"
        },
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      },
      "source": [
        "# Comparing to a Dataset with real summaries.\n",
        "We are going to load the Dataset cnn_dailymail. This is a well-known dataset available in the **Datasets** library, and it suits our purpose perfectly.\n",
        "\n",
        "Apart from the news, it also contains pre-existing summaries.\n",
        "\n",
        "We will compare the summaries generated by the two models we are using with those from the dataset to determine which model creates summaries that are closer to the reference ones."
      ]
    },
    {
      "cell_type": "code",
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        },
        "outputId": "2f3cc8cd-f2c1-4339-8366-a39bd68cc4b6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/9.27k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/13.9k [00:00<?, ?B/s]"
            ],
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            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for ccdv/cnn_dailymail contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/ccdv/cnn_dailymail.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] y\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/159M [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/376M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "72acff4838ec4e3ea71a3d6cb7ec3454"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/572k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c838359dd8e64b899a14dc9be0336ef8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/12.3M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef0c70fab7974b7bbc34b96c0a83f62f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/661k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "model_id": "491f990ee02e486e87728b430c9bd50e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee651e91a6af49b481bdb0103efb0da8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "00ea991192984ca98eab556ac001f789"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8e4951d6b0994681b7e94874ffce4f0c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "cnn_dataset = load_dataset(\"ccdv/cnn_dailymail\", \"3.0.0\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get just a few news to test\n",
        "sample_cnn = cnn_dataset[\"test\"].select(range(MAX_NEWS))\n",
        "\n",
        "sample_cnn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_eGIHx-lBlVC",
        "outputId": "c5775fd1-8f8d-499e-9091-8f5285594820"
      },
      "id": "_eGIHx-lBlVC",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['article', 'highlights', 'id'],\n",
              "    num_rows: 3\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "be652a76",
      "metadata": {
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "id": "be652a76"
      },
      "source": [
        "We retrieve the maximum length of the summaries to give the models the option to generate summaries of the same length, if they choose to do so."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e8546de4",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T16:29:24.079892Z",
          "iopub.status.busy": "2023-08-14T16:29:24.078748Z",
          "iopub.status.idle": "2023-08-14T16:29:24.0898Z",
          "shell.execute_reply": "2023-08-14T16:29:24.085476Z",
          "shell.execute_reply.started": "2023-08-14T16:29:24.079854Z"
        },
        "papermill": {
          "duration": null,
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          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "id": "e8546de4"
      },
      "outputs": [],
      "source": [
        "max_length = max(len(item['highlights']) for item in sample_cnn)\n",
        "max_length = max_length + 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9389d76f",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T16:29:24.093133Z",
          "iopub.status.busy": "2023-08-14T16:29:24.091136Z",
          "iopub.status.idle": "2023-08-14T16:29:46.747981Z",
          "shell.execute_reply": "2023-08-14T16:29:46.746942Z",
          "shell.execute_reply.started": "2023-08-14T16:29:24.093079Z"
        },
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "id": "9389d76f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f61a90f4-6106-441b-bf48-30f26f63f9f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken: 16.551 seconds\n",
            "Time taken: 13.505 seconds\n",
            "Time taken: 13.629 seconds\n"
          ]
        }
      ],
      "source": [
        "summaries_t5_base = create_summaries(sample_cnn[\"article\"],\n",
        "                                      tokenizer_base,\n",
        "                                      model_base,\n",
        "                                      max_l=max_length)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "67ab5985",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T16:29:46.751951Z",
          "iopub.status.busy": "2023-08-14T16:29:46.750888Z",
          "iopub.status.idle": "2023-08-14T16:30:05.204078Z",
          "shell.execute_reply": "2023-08-14T16:30:05.203025Z",
          "shell.execute_reply.started": "2023-08-14T16:29:46.7519Z"
        },
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "id": "67ab5985",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e6f16eec-bd5b-47c8-b086-a34a0e425e11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken: 17.991 seconds\n",
            "Time taken: 9.375 seconds\n",
            "Time taken: 12.378 seconds\n"
          ]
        }
      ],
      "source": [
        "summaries_t5_finetuned = create_summaries(sample_cnn[\"article\"],\n",
        "                                      tokenizer_finetuned,\n",
        "                                      model_finetuned,\n",
        "                                      max_l=max_length)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b80c0c1",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T16:35:57.777001Z",
          "iopub.status.busy": "2023-08-14T16:35:57.776036Z",
          "iopub.status.idle": "2023-08-14T16:35:57.784049Z",
          "shell.execute_reply": "2023-08-14T16:35:57.782555Z",
          "shell.execute_reply.started": "2023-08-14T16:35:57.776958Z"
        },
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "id": "7b80c0c1"
      },
      "outputs": [],
      "source": [
        "#Get the real summaries from the cnn_dataset\n",
        "real_summaries = sample_cnn['highlights']"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a8dc3a10",
      "metadata": {
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "id": "a8dc3a10"
      },
      "source": [
        "Let's take a look at the generated summaries alongside the reference summaries provided by the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d6ea4e8c",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T16:36:00.561736Z",
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          "shell.execute_reply.started": "2023-08-14T16:36:00.561681Z"
        },
        "papermill": {
          "duration": null,
          "end_time": null,
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          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "id": "d6ea4e8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "9ba32392-9101-457f-c32c-f2e1fb804fd3"
      },
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              "type": "dataframe",
              "variable_name": "summaries",
              "summary": "{\n  \"name\": \"summaries\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"base\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"best died in hospice in Hickory, north Carolina, of complications from pneumonia. he played bumbling sheriff Rosco P. Coltrane on \\\"the Dukes of Hazzard\\\" he was born in Kentucky and raised in rural Indiana.\",\n          \"\\\"it doesn't matter what anyone says, he is presumed to be innocent,\\\" attorney says. cardiologist's lawyer says allegations against his client are \\\"completely unsubstantiated\\\" prosecutors say he pleaded not guilty to all charges. he faces charges in connection with a plot to take out a rival doctor.\",\n          \"president Barack Obama took part in a roundtable discussion this week on climate change. he refocused on the issue from a public health vantage point. the average american can also do their part to reduce their own carbon footprint.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"finetuned\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Jimmie Best was \\\"the most constantly creative person I have ever known,\\\" co-star John Schneider says. \\\"The Dukes of Hazzard\\\" ran until 1985 and spawned TV movies, an animated series and video games. \\\"Jimmie Best was my teacher, mentor, close friend and collaborator for 26 years,\\\" friend says.\",\n          \"Dr. Anthony Moschetto's attorney calls the allegations \\\"completely unsubstantiated\\\" He is accused of plotting to kill a rival doctor on Long Island.\",\n          \"President Obama says climate change is a public health issue that affects all of us. He says the President wants the average American to take away the health impacts of climate change.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"reference\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"James Best, who played the sheriff on \\\"The Dukes of Hazzard,\\\" died Monday at 88 .\\n\\\"Hazzard\\\" ran from 1979 to 1985 and was among the most popular shows on TV .\",\n          \"A lawyer for Dr. Anthony Moschetto says the charges against him are baseless .\\nMoschetto, 54, was arrested for selling drugs and weapons, prosecutors say .\\nAuthorities allege Moschetto hired accomplices to burn down the practice of former associate .\",\n          \"\\\"No challenge poses more of a public threat than climate change,\\\" the President says .\\nHe credits the Clean Air Act with making Americans \\\"a lot\\\" healthier .\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "summaries = pd.DataFrame.from_dict(\n",
        "        {\n",
        "            \"base\": summaries_t5_base,\n",
        "            \"finetuned\": summaries_t5_finetuned,\n",
        "            \"reference\": real_summaries,\n",
        "        }\n",
        "    )\n",
        "summaries.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c73e7277",
      "metadata": {
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "id": "c73e7277"
      },
      "source": [
        "Now we can calculate the ROUGE scores for the two models."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "summaries_t5_base"
      ],
      "metadata": {
        "id": "F5zYqTY4vsQ1",
        "outputId": "c35ed9ee-d342-44bc-db56-0d05d0b702c8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "F5zYqTY4vsQ1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['best died in hospice in Hickory, north Carolina, of complications from pneumonia. he played bumbling sheriff Rosco P. Coltrane on \"the Dukes of Hazzard\" he was born in Kentucky and raised in rural Indiana.',\n",
              " '\"it doesn\\'t matter what anyone says, he is presumed to be innocent,\" attorney says. cardiologist\\'s lawyer says allegations against his client are \"completely unsubstantiated\" prosecutors say he pleaded not guilty to all charges. he faces charges in connection with a plot to take out a rival doctor.',\n",
              " 'president Barack Obama took part in a roundtable discussion this week on climate change. he refocused on the issue from a public health vantage point. the average american can also do their part to reduce their own carbon footprint.']"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "real_summaries"
      ],
      "metadata": {
        "id": "vSwf6biYvz6P",
        "outputId": "82a18428-0b2f-4a9e-c7ac-4db6527a2177",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "vSwf6biYvz6P",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['James Best, who played the sheriff on \"The Dukes of Hazzard,\" died Monday at 88 .\\n\"Hazzard\" ran from 1979 to 1985 and was among the most popular shows on TV .',\n",
              " 'A lawyer for Dr. Anthony Moschetto says the charges against him are baseless .\\nMoschetto, 54, was arrested for selling drugs and weapons, prosecutors say .\\nAuthorities allege Moschetto hired accomplices to burn down the practice of former associate .',\n",
              " '\"No challenge poses more of a public threat than climate change,\" the President says .\\nHe credits the Clean Air Act with making Americans \"a lot\" healthier .']"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c0bda484",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T16:37:53.508688Z",
          "iopub.status.busy": "2023-08-14T16:37:53.506732Z",
          "iopub.status.idle": "2023-08-14T16:37:53.827647Z",
          "shell.execute_reply": "2023-08-14T16:37:53.826103Z",
          "shell.execute_reply.started": "2023-08-14T16:37:53.508613Z"
        },
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "id": "c0bda484",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4be9dd97-4133-4605-e4b7-76ab5129ed85"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'rouge1': 0.3050834824090638,\n",
              " 'rouge2': 0.07211128178870115,\n",
              " 'rougeL': 0.2095520274299344,\n",
              " 'rougeLsum': 0.2662418008348241}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "compute_rouge_score(summaries_t5_base, real_summaries)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a4045072",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-08-14T16:37:55.512869Z",
          "iopub.status.busy": "2023-08-14T16:37:55.512163Z",
          "iopub.status.idle": "2023-08-14T16:37:55.822842Z",
          "shell.execute_reply": "2023-08-14T16:37:55.821446Z",
          "shell.execute_reply.started": "2023-08-14T16:37:55.512794Z"
        },
        "papermill": {
          "duration": null,
          "end_time": null,
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          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "id": "a4045072",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17c4c584-fd1a-450b-8773-697d6d754ec8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'rouge1': 0.31659149328289443,\n",
              " 'rouge2': 0.11065084340946411,\n",
              " 'rougeL': 0.22002036956205442,\n",
              " 'rougeLsum': 0.24877540132887144}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "compute_rouge_score(summaries_t5_finetuned, real_summaries)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c91b3f50",
      "metadata": {
        "papermill": {
          "duration": null,
          "end_time": null,
          "exception": null,
          "start_time": null,
          "status": "pending"
        },
        "tags": [],
        "id": "c91b3f50"
      },
      "source": [
        "With these results, I would say that the fine-tuned model performs slightly better than the T5-Base model. It consistently achieves higher ROUGE scores in all metrics except for LSUM, where the difference is minimal.\n",
        "\n",
        "Additionally, the ROUGE metrics are quite interpretable.\n",
        "\n",
        "LSUM indicates the percentage of the longest common subsequence, regardless of word order, in relation to the total length of the text.\n",
        "\n",
        "This can be a good indicator of overall similarity between texts. However, both models have very similar LSUM scores, and the fine-tuned model has better scores in other ROUGE metrics.\n",
        "\n",
        "Personally, I would lean towards the fine-tuned model, although the difference may not be very significant.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Comparing entities with ROUGE"
      ],
      "metadata": {
        "id": "vX2Z-ALxUsbt"
      },
      "id": "vX2Z-ALxUsbt"
    },
    {
      "cell_type": "code",
      "source": [
        "entities=['Paris, Londres, Barcelona, Reus']\n",
        "entities_ref=['Reus, Paris, Londres, Barcelona']"
      ],
      "metadata": {
        "id": "B_Gg7nk3wGqS"
      },
      "id": "B_Gg7nk3wGqS",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "compute_rouge_score(entities, entities_ref)"
      ],
      "metadata": {
        "id": "RKHNNE86wZwz",
        "outputId": "8297dc0e-ac0b-4585-c16c-5ba885537aa0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "RKHNNE86wZwz",
      "execution_count": null,
      "outputs": [
        {
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