6_Capstone_Model_Evaluation.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T5quGQEXOPJE",
        "outputId": "2619e95d-49da-405e-a6ed-5a7bd8a1ac28"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
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            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.1.4)\n",
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            "Collecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
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            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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            "\u001b[?25hInstalling collected packages: xxhash, pyarrow, dill, multiprocess, datasets, evaluate\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "\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",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "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[0mSuccessfully installed datasets-3.0.0 dill-0.3.8 evaluate-0.4.3 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers datasets evaluate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "dataset_name = \"aumghag/Data-Analytics-Digital-Marketing-Project-Management-QA_DB\"\n",
        "dataset = load_dataset(dataset_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240,
          "referenced_widgets": [
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        },
        "id": "yFq7_3sTOsby",
        "outputId": "afd048c7-5229-4782-d83a-e7a3ed121968"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/157 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "final_qa_db.csv:   0%|          | 0.00/92.1k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/489 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "# Load your fine-tuned model and tokenizer\n",
        "model_name = \"aumghag/ft-llama-3.1-8B-capstone-personifAI-edu\"\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)"
      ],
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        "id": "YqmENvi5OvP1",
        "outputId": "f7b918a4-5e98-4f0f-c3ee-a16c9b6ff2a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "# Select a batch of examples (assuming dataset is structured in QA pairs)\n",
        "questions = dataset['train']['question'][:10]  # Adjust the size accordingly\n",
        "answers = dataset['train']['answer'][:10]"
      ],
      "metadata": {
        "id": "R1HgS7SpOx6E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = []\n",
        "for question in questions:\n",
        "    inputs = tokenizer(question, return_tensors=\"pt\").input_ids\n",
        "    outputs = model.generate(inputs, max_length=50)\n",
        "    predicted_answer = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "    predictions.append(predicted_answer)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xb-JEopk6fNn",
        "outputId": "b6038186-8a24-4200-d031-ee185f1653bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
            "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n",
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "questions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iSQftzD7AY5b",
        "outputId": "11c08dc8-05cd-4992-f310-085647747e07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['What is the difference between data science and data analytics?',\n",
              " 'What are the types of analytics used in business decision-making?',\n",
              " 'What is data literacy and why is it important?',\n",
              " 'What is the data ecosystem and lifecycle?',\n",
              " 'What is data privacy and what are its key components?',\n",
              " 'What is disparate impact and why is it unlawful?',\n",
              " 'What is secondary data analysis?',\n",
              " 'Why is it important to follow established and systematic procedures when collecting secondary data?',\n",
              " 'What are some common steps in preparing secondary data for statistical analysis?',\n",
              " 'How do you collect variables of interest in an established and systematic fashion?']"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y1T3pxG5An9t",
        "outputId": "b5f6ec65-c4f7-4b1c-b565-43835ddef957"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Data science focuses on extracting insights from data using advanced statistical and computational methods, whereas data analytics focuses on analyzing historical data to inform business decisions.',\n",
              " 'Types of analytics used in business decision-making include Descriptive, Diagnostic, Predictive, Prescriptive, and Exploratory analytics.',\n",
              " \"Data literacy refers to the ability to read, understand, and work with data; it's important for informed decision-making and competitiveness in a data-driven world.\",\n",
              " 'The data ecosystem includes data sources, storage, processing, and consumption, while the lifecycle includes plan, collect, store, process, analyze, share, and archive phases.',\n",
              " 'Data privacy ensures sensitive info is protected; key components include confidentiality, integrity, availability, authenticity, and anonymity.',\n",
              " \"Disparate impact occurs when policies unfairly affect certain groups; it's unlawful as it perpetuates discrimination and violates equality laws.\",\n",
              " 'Secondary data analysis involves analyzing existing data collected by others, often for research or decision-making purposes.',\n",
              " 'Following systematic procedures ensures data quality, reliability, and accuracy when collecting secondary data.',\n",
              " 'Steps to prepare secondary data include cleaning, transforming, merging, and formatting data for statistical analysis.',\n",
              " 'Variables of interest are collected systematically through surveys, observation, or existing databases, ensuring data quality and relevance.']"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "answers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0M3rSWqsEvS1",
        "outputId": "e4626854-bf60-4fb9-a1cf-6a0d38c34093"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Data science focuses on asking questions and finding answers using data, while data analytics is used to extract insights from data to inform business decisions.',\n",
              " 'There are four types of analytics: descriptive, diagnostic, predictive, and prescriptive. Each type provides different insights and is used for different purposes.',\n",
              " 'Data literacy is the ability to read, understand, and utilize data in different ways. It is important because it enables non-data professionals to make informed decisions using data.',\n",
              " 'The data ecosystem refers to the programming languages, packages, algorithms, cloud-computing services, and general infrastructure an organization uses to collect, store, analyze, and leverage data. The data lifecycle describes the path data takes from generation to interpretation.',\n",
              " 'Data privacy is a subcategory of data protection that encompasses the ethical and legal obligation to protect access to personally identifiable information (PII). Its key components include what data is collected, how it is stored, and who can access it.',\n",
              " 'Disparate impact refers to the unintentional harm caused by the use of data to an individual or group of people. It is unlawful because it discriminates against certain groups of people.',\n",
              " 'Secondary data analysis is a research methodology that involves analyzing data collected by someone else, often for another purpose.',\n",
              " \"It's crucial to follow established procedures to ensure accuracy and reliability of the results.\",\n",
              " 'The steps include planning, data collection, data storage, and data cleaning.',\n",
              " 'You can collect variables of interest by downloading the necessary data files, collecting only the required variables, and using procedures like PROC MEANS to verify distributions.']"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install bert-score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "H_gORe7gDkKE",
        "outputId": "1f2a73c1-dce8-48ab-94ea-6c6bfc16c217"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting bert-score\n",
            "  Downloading bert_score-0.3.13-py3-none-any.whl.metadata (15 kB)\n",
            "Requirement already satisfied: torch>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from bert-score) (2.4.1+cu121)\n",
            "Requirement already satisfied: pandas>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from bert-score) (2.1.4)\n",
            "Requirement already satisfied: transformers>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from bert-score) (4.44.2)\n",
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            "Downloading bert_score-0.3.13-py3-none-any.whl (61 kB)\n",
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            "\u001b[?25hInstalling collected packages: bert-score\n",
            "Successfully installed bert-score-0.3.13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from bert_score import score\n",
        "\n",
        "# Compute BERTScore for predictions\n",
        "P, R, F1 = score(predictions, answers, lang=\"en\", verbose=True)\n",
        "\n",
        "print(f\"Average BERT Precision: {P.mean().item()*100}\")\n",
        "print(f\"Average BERT Recall: {R.mean().item()*100}\")\n",
        "print(f\"Average BERT F1 Score: {F1.mean().item()*100}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297,
          "referenced_widgets": [
            "94328d2d3e4f41ceb64808834bb25c17",
            "ef746bde9cc7477a9bd44e75a30b5f13",
            "16f0cfb7193345449a0b84dbda4d30e5",
            "a4b8a0a9be0241bb975f0e832b9617d7",
            "d2fe615f48a444e3995a46aeb90c2827",
            "ea4591af4df34eb1ba03dc0679a9a669",
            "0b8db7ce56ae4a76af89411af8c77d6a",
            "1d21b7b73486413092c0cbe61cf8b54c",
            "f10c1280e94f41db9625e94e3015250b",
            "edf4c15010a64bdf8b141ff7a41e8ab1",
            "97c3fb4d547142148a07d635019309ff",
            "a0b395fd08a74a269f7b59858f2d30be",
            "4d2211b850a648f6b8502dd40a8ab894",
            "c5acd748ae4f4dfb8aaaa89c76135adf",
            "de5aaf82272b4ef396ed90b472962149",
            "e8ba99479ce940d0b0b5a99b15e2a967",
            "6e6f5407dcb448af89bc64a242c3cc60",
            "6a79c8e054b94cf199c27e36a99d9486",
            "312318fa13ce4a38a95065b21ae778a5",
            "e24340e77a144a66874ef10d411906c8",
            "f054c445bbdc4ecfa0cb7b9cfa70442a",
            "41d682d5a1384cacac68edfabd552fe6"
          ]
        },
        "id": "w8jUF4dgDo1O",
        "outputId": "7d2a69fa-71a8-449e-e081-cedc640c6cbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n",
            "Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "calculating scores...\n",
            "computing bert embedding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "94328d2d3e4f41ceb64808834bb25c17"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "computing greedy matching.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a0b395fd08a74a269f7b59858f2d30be"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "done in 0.11 seconds, 91.88 sentences/sec\n",
            "Average BERT Precision: 91.20826721191406\n",
            "Average BERT Recall: 90.66015481948853\n",
            "Average BERT F1 Score: 90.9280002117157\n"
          ]
        }
      ]
    }
  ]
}