Dataset_creation_for_fine_tuning_LLM.ipynb

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        "# DataCamp Code-along - Dataset creation\n",
        "\n",
        "❤️ Created by [@maximelabonne](https://twitter.com/maximelabonne) as part of the 🗣️ [Large Language Model Course](https://github.com/mlabonne/llm-course).\n",
        "\n",
        "You can run this notebook on a free-tier Google Colab (T4 GPU).\n",
        "\n",
        "## 1. Introduction\n",
        "\n",
        "High-quality data is fundamental to produce a good model. The higher the quality, the better the model. In this notebook, we will create a dataset for fine-tuning LLMs.\n",
        "\n",
        "![](https://i.imgur.com/IDNhAWH.png)\n",
        "\n",
        "There are different types of datasets we can use to fine-tune LLMs:\n",
        "\n",
        "1. **Instruction datasets**: inputs are instructions (e.g., questions) and outputs correspond to the expected responses (e.g., answers). *Example: Open-Orca.*\n",
        "2. **Raw completion**: this is a continuation of the pre-training objective (next token prediction). In this case, the trained model is not designed to be used as an assistant. *Example: MADLAD-400.*\n",
        "3. **Preference datasets**: these datasets are used with reinforcement learning to rank candidate responses. They can provide multiple answers to the same instruction, and help the model to select the best response. *Example: ultrafeedback_binarized.*\n",
        "4. **Others**: a fill-in-the-middle objective is very popular with code completion models (e.g., Codex from GitHub Copilot). Other datasets can be designed for classification, where the outputs correspond to the labels we want to predict (the model requires an additional classfication head in this case).\n",
        "\n",
        "In practice, supervised fine-tuning only leverages the first type of dataset. We can either **create our own** instruction dataset or **modify an existing one** to filter, improve, or enrich it.\n",
        "\n",
        "## 2. Load and analyze the dataset\n",
        "\n",
        "In this notebook, we will filter the excellent [Open-Platypus dataset](https://huggingface.co/datasets/garage-bAInd/Open-Platypus). It's already a collection of different datasets:\n",
        "\n",
        "| Dataset Name                                                 | License Type |\n",
        "|--------------------------------------------------------------|--------------|\n",
        "| [PRM800K](https://github.com/openai/prm800k)                 | MIT          |\n",
        "| [ScienceQA](https://github.com/lupantech/ScienceQA) | [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)    |\n",
        "| [SciBench](https://github.com/mandyyyyii/scibench)    | MIT      |\n",
        "| [ReClor](https://whyu.me/reclor/)                            | Non-commercial      |\n",
        "| [TheoremQA](https://huggingface.co/datasets/wenhu/TheoremQA) | MIT      |\n",
        "| [`nuprl/leetcode-solutions-python-testgen-gpt4`](https://huggingface.co/datasets/nuprl/leetcode-solutions-python-testgen-gpt4/viewer/nuprl--leetcode-solutions-python-testgen-gpt4/train?p=1) | None listed |\n",
        "| [`jondurbin/airoboros-gpt4-1.4.1`](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1) | other |\n",
        "| [`TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k`](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k/viewer/TigerResearch--tigerbot-kaggle-leetcodesolutions-en-2k/train?p=2) | apache-2.0 |\n",
        "| [openbookQA](https://huggingface.co/datasets/openbookqa/viewer/additional/train?row=35) | apache-2.0      |\n",
        "| [ARB](https://arb.duckai.org)                                | MIT      |\n",
        "| [`timdettmers/openassistant-guanaco`](https://huggingface.co/datasets/timdettmers/openassistant-guanaco)                                | apache-2.0      |"
      ],
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      "execution_count": null,
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      },
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          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/521.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m153.6/521.2 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m491.5/521.2 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.0/86.0 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.5/85.5 MB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m12.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m74.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "# Install libraries\n",
        "!pip install -q datasets transformers sentence_transformers faiss-gpu"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can find your token at this address: https://huggingface.co/settings/tokens and copy paste it in the \"Secrets\" tab in Google Colab (name: \"huggingface\")."
      ],
      "metadata": {
        "id": "r42tfZWBD4up"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "\n",
        "# Defined in the secrets tab in Google Colab\n",
        "hf_token = userdata.get('huggingface')\n",
        "\n",
        "#upload the datasets in your hugging face account"
      ],
      "metadata": {
        "id": "q0FYKJ9Vfwir"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load the dataset\n",
        "dataset = load_dataset(\"garage-bAInd/Open-Platypus\")\n",
        "dataset"
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              "Downloading readme:   0%|          | 0.00/5.34k [00:00<?, ?B/s]"
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              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input', 'output', 'instruction', 'data_source'],\n",
              "        num_rows: 24926\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Read as pandas DataFrame\n",
        "dataset['train'].to_pandas()  # google colab to convert to ineractive table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "v-tDqCm_JUlg",
        "outputId": "27d9bcc3-e2f0-49c7-93af-11d33f4ab2f6"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "      input                                             output  \\\n",
              "0            To find the probability of the spinner landing...   \n",
              "1            I need to choose 6 people out of 14, and the o...   \n",
              "2            First we count the number of all 4-letter word...   \n",
              "3            She can do this if and only if at least one of...   \n",
              "4            Think of the problem as a sequence of H's and ...   \n",
              "...     ...                                                ...   \n",
              "24921        There are several formulas that generate all p...   \n",
              "24922        Sure, here are some of the best universities:\\...   \n",
              "24923        [Enter stage left]\\n\\nFirst Citizen: Good morr...   \n",
              "24924        That's a very thoughtful idea! Here are some s...   \n",
              "24925        Certainly! Here's an implementation of the Tim...   \n",
              "\n",
              "                                             instruction    data_source  \n",
              "0      A board game spinner is divided into three par...  MATH/PRM-800K  \n",
              "1      My school's math club has 6 boys and 8 girls. ...  MATH/PRM-800K  \n",
              "2      How many 4-letter words with at least one cons...  MATH/PRM-800K  \n",
              "3      Melinda will roll two standard six-sided dice ...  MATH/PRM-800K  \n",
              "4      Let $p$ be the probability that, in the proces...  MATH/PRM-800K  \n",
              "...                                                  ...            ...  \n",
              "24921  Can we find a formula to generate all prime nu...        guanaco  \n",
              "24922  What are some of the best university's for stu...        guanaco  \n",
              "24923  Write me a SPL (Shakespeare Programming Langua...        guanaco  \n",
              "24924  Hi. I want to make an embossed picture for my ...        guanaco  \n",
              "24925  Please implement the Timsort algorithm on Lean...        guanaco  \n",
              "\n",
              "[24926 rows x 4 columns]"
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              "      <td>Please implement the Timsort algorithm on Lean...</td>\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next steps is to remove samples that are of not high quality\n",
        "and use tokenize ,since we are using llama model we will use llama  tokenizer"
      ],
      "metadata": {
        "id": "sl1ub1ceyzlf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# 1. Load the tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"NousResearch/Llama-2-7b-hf\")\n",
        "\n",
        "# 2. Tokenize each row and count the number of tokens\n",
        "instruction_token_counts = [len(tokenizer.tokenize(example[\"instruction\"])) for example in dataset['train']]\n",
        "output_token_counts = [len(tokenizer.tokenize(example[\"output\"])) for example in dataset['train']]\n",
        "combined_token_counts = [instruction + output for instruction, output in zip(instruction_token_counts, output_token_counts)]\n",
        "\n",
        "# Helper function to plot the distributions\n",
        "def plot_distribution(token_counts, title):\n",
        "    sns.set_style(\"whitegrid\")\n",
        "    plt.figure(figsize=(15, 6))\n",
        "    plt.hist(token_counts, bins=50, color='#3498db', edgecolor='black')\n",
        "    plt.title(title, fontsize=16)\n",
        "    plt.xlabel(\"Number of tokens\", fontsize=14)\n",
        "    plt.ylabel(\"Number of examples\", fontsize=14)\n",
        "    plt.xticks(fontsize=12)\n",
        "    plt.yticks(fontsize=12)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "# Plot the distribution of token counts\n",
        "plot_distribution(instruction_token_counts, \"Distribution of token counts for instruction only\")\n",
        "plot_distribution(output_token_counts, \"Distribution of token counts for output only\")\n",
        "plot_distribution(combined_token_counts, \"Distribution of token counts for combined instruction + output\")"
      ],
      "metadata": {
        "id": "GCoXNvkB9_5d",
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          "base_uri": "https://localhost:8080/",
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        },
        "outputId": "75fa2424-81d1-48de-9769-07e022d95cad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/746 [00:00<?, ?B/s]"
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            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
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            "text/plain": [
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Filter out rows with more than 2048 tokens\n",
        "\n",
        "We can remove samples with more than 2048 tokens (max context size of Llama 2 by default = 4096)."
      ],
      "metadata": {
        "id": "_RXe958fNLwH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter out rows with more than 2048 tokens\n",
        "valid_indices = [i for i, count in enumerate(combined_token_counts) if count <= 2048]\n",
        "print(f\"Number of valid rows: {len(valid_indices)}\")\n",
        "print(f\"Removing {len(dataset['train']) - len(valid_indices)} rows...\")\n",
        "\n",
        "# Extract valid rows based on indices\n",
        "dataset['train'] = dataset['train'].select(valid_indices)\n",
        "\n",
        "# Get token counts for valid rows\n",
        "token_counts = [combined_token_counts[i] for i in valid_indices]\n",
        "\n",
        "plot_distribution(token_counts, \"New distribution of token counts for combined instruction + output\")"
      ],
      "metadata": {
        "id": "RgnsxLWdNKuZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "outputId": "db4b543b-a84b-4953-a792-c301975d42a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of valid rows: 24895\n",
            "Removing 31 rows...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Near-deduplication using embeddings\n",
        "\n",
        "How to choose the embedding model? Check the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). In this example, we're not using the best embedding model because it would take too long.\n",
        "\n",
        "![](https://i.imgur.com/Fbx1Ivz.png)"
      ],
      "metadata": {
        "id": "7zyZAduiEv9Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#embedding is just vectors\n",
        "# used to calculate similarities and remove similar vectors\n",
        "\n",
        "from sentence_transformers import SentenceTransformer\n",
        "import faiss # vector database from facebook\n",
        "from datasets import Dataset, DatasetDict\n",
        "from tqdm.autonotebook import tqdm # for nice loading bar\n",
        "import numpy as np\n",
        "\n",
        "def deduplicate_dataset(dataset: Dataset, model: str, threshold: float):  # the model for embedding we will use is gpte-large\n",
        "    sentence_model = SentenceTransformer(model)\n",
        "    outputs = [example[\"output\"] for example in dataset['train']]\n",
        "\n",
        "    print(\"Converting text to embeddings...\")\n",
        "    embeddings = sentence_model.encode(outputs, show_progress_bar=True)\n",
        "    dimension = embeddings.shape[1]\n",
        "    index = faiss.IndexFlatIP(dimension)                                      # vector database\n",
        "    normalized_embeddings = embeddings / np.linalg.norm(embeddings, axis=1, keepdims=True)  # normalize the embeddings is important using numpy\n",
        "    index.add(normalized_embeddings)\n",
        "\n",
        "    print(\"Filtering out near-duplicates...\")\n",
        "    D, I = index.search(normalized_embeddings, k=2)\n",
        "    to_keep = []\n",
        "\n",
        "    for i in tqdm(range(len(embeddings)), desc=\"Filtering\"):\n",
        "        # If the second closest vector (D[i, 1]) has cosine similarity above the threshold\n",
        "        if D[i, 1] >= threshold:\n",
        "            # Check if either the current item or its nearest neighbor is already in the to_keep list\n",
        "            nearest_neighbor = I[i, 1]\n",
        "            if i not in to_keep and nearest_neighbor not in to_keep:\n",
        "                # If not, add the current item to the list\n",
        "                to_keep.append(i)\n",
        "        else:\n",
        "            # If the similarity is below the threshold, always keep the current item\n",
        "            to_keep.append(i)\n",
        "\n",
        "    dataset = dataset['train'].select(to_keep)\n",
        "    return DatasetDict({\"train\": dataset})\n",
        "\n",
        "deduped_dataset = deduplicate_dataset(dataset, \"thenlper/gte-large\", 0.95) # 0.95 means 95% similar instructions and similar outputs"
      ],
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              "pytorch_model.bin:   0%|          | 0.00/670M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a1d4d8dab59f4ee6b051fe3b69bcde7a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/57.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "23ce2e24de3644cfa6de197805872b74"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/125 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "47d83766beaa441d80c814fea5b551dc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/712k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d51d4e0d795947a5810c36407955164b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/342 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f18f4c1b64940d29c587fbf45c1c36b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7bb72e928ffe49d6b968cdc5ddaefb1b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "modules.json:   0%|          | 0.00/385 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3658bb40617e46b5ba5bc1927a057cb0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Converting text to embeddings...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/778 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1d3ffef5e0064677b4027195e17757ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Filtering out near-duplicates...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filtering:   0%|          | 0/24895 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "269449fb946046ab9bc4e6dccd403917"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Number of samples in the original dataset: {len(dataset['train'])}\")\n",
        "print(f\"Number of samples in the deduped dataset: {len(deduped_dataset['train'])}\")\n",
        "print(f\"Number of samples that were removed: {len(dataset['train']) - len(deduped_dataset['train'])}\")"
      ],
      "metadata": {
        "id": "BcKmLesFaB2R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61d1a2f3-0c42-4ad8-b82b-2a0cb64b3cfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of samples in the original dataset: 24895\n",
            "Number of samples in the deduped dataset: 18168\n",
            "Number of samples that were removed: 6727\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Top-k sampling\n",
        "\n",
        "Only keep the top k samples with the most tokens."
      ],
      "metadata": {
        "id": "TOCspcgXNOav"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the top k rows with the most tokens\n",
        "def get_top_k_rows(dataset, token_counts, k):\n",
        "    # Sort by descending token count and get top k indices\n",
        "    sorted_indices = sorted(range(len(token_counts)), key=lambda i: token_counts[i], reverse=True)\n",
        "    top_k_indices = sorted_indices[:k]\n",
        "\n",
        "    # Extract top k rows\n",
        "    top_k_data = {\n",
        "        \"instruction\": [dataset['train'][i][\"instruction\"] for i in top_k_indices],\n",
        "        \"output\": [dataset['train'][i][\"output\"] for i in top_k_indices]\n",
        "    }\n",
        "\n",
        "    return Dataset.from_dict(top_k_data)\n",
        "\n",
        "# Get token counts\n",
        "instruction_token_counts = [len(tokenizer.tokenize(example[\"instruction\"])) for example in deduped_dataset['train']]\n",
        "output_token_counts = [len(tokenizer.tokenize(example[\"output\"])) for example in deduped_dataset['train']]\n",
        "combined_token_counts = [instruction + output for instruction, output in zip(instruction_token_counts, output_token_counts)]\n",
        "\n",
        "k = 1000  # You can adjust this value as needed\n",
        "top_k_dataset = get_top_k_rows(deduped_dataset, combined_token_counts, k)\n",
        "\n",
        "# Save these rows in a Dataset object with a 'train' split\n",
        "dataset = DatasetDict({\"train\": top_k_dataset})"
      ],
      "metadata": {
        "id": "iUIy1SGqeyFo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "instruction_token_counts = [len(tokenizer.tokenize(example[\"instruction\"])) for example in dataset['train']]\n",
        "output_token_counts = [len(tokenizer.tokenize(example[\"output\"])) for example in dataset['train']]\n",
        "combined_token_counts = [instruction + output for instruction, output in zip(instruction_token_counts, output_token_counts)]\n",
        "\n",
        "# Plot the distribution of token counts\n",
        "plot_distribution(instruction_token_counts, \"Distribution of token counts for instruction only\")\n",
        "plot_distribution(output_token_counts, \"Distribution of token counts for output only\")\n",
        "plot_distribution(combined_token_counts, \"Distribution of token counts for combined instruction + output\")"
      ],
      "metadata": {
        "id": "oUxPB-KOKEmH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0630b6e8-f495-4c52-d56a-8af14a785d7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Read as pandas DataFrame\n",
        "dataset['train'].to_pandas()"
      ],
      "metadata": {
        "id": "k4VvBKYuR4fg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "662b39d6-3303-4d81-a97c-d4c8e7087c2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           instruction  \\\n",
              "0    Let's come up with a rich and complex alien wo...   \n",
              "1    Let\\n$$p(x,y) = a_0 + a_1x + a_2y + a_3x^2 + a...   \n",
              "2    Given the code below, refactor it, add comment...   \n",
              "3    Find the area of the region described by $x \\g...   \n",
              "4    Let $P$ be the plane passing through the origi...   \n",
              "..                                                 ...   \n",
              "995  Hello. My name is Mike. I have a message left ...   \n",
              "996  Given a prime $p$ and an integer $a$, we say t...   \n",
              "997  Let $f$ be defined by  \\[f(x) = \\left\\{\\n\\begi...   \n",
              "998  BEGININPUT\\nBEGINCONTEXT\\ndate: August 5, 2021...   \n",
              "999  BEGININPUT\\nBEGINCONTEXT\\ndate: August 14, 201...   \n",
              "\n",
              "                                                output  \n",
              "0    Planet Name: Xylothar\\n\\nXylothar is a diverse...  \n",
              "1    Observe that  \\begin{align*}\\np(0,0) &= a_0 = ...  \n",
              "2    Here is the refactored and commented version:\\...  \n",
              "3    Let $n = \\lfloor x \\rfloor,$ and let $\\{x\\} = ...  \n",
              "4    Let $\\mathbf{v} = \\begin{pmatrix} x \\\\ y \\\\ z ...  \n",
              "..                                                 ...  \n",
              "995  Hello Mike, it's nice to meet you. As an AI la...  \n",
              "996  To find the primitive roots $\\pmod 7$, I need ...  \n",
              "997  The number $f^{-1}(-3)$ is the value of $x$ su...  \n",
              "998  Dr. Eleanor Thompson's study found that partic...  \n",
              "999  Charles Leroux won the Golden Bread Award in 1...  \n",
              "\n",
              "[1000 rows x 2 columns]"
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              "      <td>Let's come up with a rich and complex alien wo...</td>\n",
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              "      <td>Charles Leroux won the Golden Bread Award in 1...</td>\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a614e274-4405-4223-9730-89387e522b14 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a614e274-4405-4223-9730-89387e522b14');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dca88845-155c-4d7d-b67d-65389e9f104f button');\n",
              "      quickchartButtonEl.style.display =\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Chat templates\n",
        "\n",
        "We need to define a **chat template**, which formats the instructions and responses in a way that is suitable for the model. Here's an example of raw instructions and responses from a [Hugging Face blog post](https://huggingface.co/blog/chat-templates):\n",
        "\n",
        "```\n",
        "[\n",
        "    {\"role\": \"user\", \"content\": \"Hi there!\"},\n",
        "    {\"role\": \"assistant\", \"content\": \"Nice to meet you!\"}\n",
        "]\n",
        "```\n",
        "\n",
        "There are different ways to format it. The most straightforward one would be:\n",
        "\n",
        "```\n",
        "User: Hey there!\n",
        "Assistant: Nice to meet you!\n",
        "```\n",
        "\n",
        "In the case of Llama 2, the authors used the following template for the **chat models**:\n",
        "\n",
        "```\n",
        "<s>[INST] <<SYS>>\n",
        "System prompt\n",
        "<</SYS>>\n",
        "\n",
        "User prompt [/INST] Model answer </s>\n",
        "```\n",
        "\n",
        "Currently, the most popular and standardized chat template is ChatML from OpenAI, which looks like this:\n",
        "\n",
        "```\n",
        "<|im_start|>user\n",
        "Hey there!<|im_end|>\n",
        "<|im_start|>assistant\n",
        "Nice to meet you!<|im_end|>\n",
        "```\n",
        "\n",
        "The impact of chat templates on the performance of the model is unclear. In most cases, we fine-tune base models that have not been trained with a particular template, which is also why there's no clear standard. However, they are important as they can cause many issues and limit the compatibility of your models."
      ],
      "metadata": {
        "id": "qun3GC4GkESL"
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    {
      "cell_type": "code",
      "source": [
        "def chat_template(example):\n",
        "    example[\"instruction\"] = f\"### Instruction:\\n{example['instruction']}\\n\\n### Response:\\n\"\n",
        "    return example\n",
        "\n",
        "dataset = dataset.map(chat_template)"
      ],
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          "height": 49,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
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      "cell_type": "code",
      "source": [
        "dataset['train'].to_pandas()"
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          "height": 424
        },
        "id": "hP2a_BLtmIMt",
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              "                                           instruction  \\\n",
              "0    ### Instruction:\\nLet's come up with a rich an...   \n",
              "1    ### Instruction:\\nLet\\n$$p(x,y) = a_0 + a_1x +...   \n",
              "2    ### Instruction:\\nGiven the code below, refact...   \n",
              "3    ### Instruction:\\nFind the area of the region ...   \n",
              "4    ### Instruction:\\nLet $P$ be the plane passing...   \n",
              "..                                                 ...   \n",
              "995  ### Instruction:\\nHello. My name is Mike. I ha...   \n",
              "996  ### Instruction:\\nGiven a prime $p$ and an int...   \n",
              "997  ### Instruction:\\nLet $f$ be defined by  \\[f(x...   \n",
              "998  ### Instruction:\\nBEGININPUT\\nBEGINCONTEXT\\nda...   \n",
              "999  ### Instruction:\\nBEGININPUT\\nBEGINCONTEXT\\nda...   \n",
              "\n",
              "                                                output  \n",
              "0    Planet Name: Xylothar\\n\\nXylothar is a diverse...  \n",
              "1    Observe that  \\begin{align*}\\np(0,0) &= a_0 = ...  \n",
              "2    Here is the refactored and commented version:\\...  \n",
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              "4    Let $\\mathbf{v} = \\begin{pmatrix} x \\\\ y \\\\ z ...  \n",
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              "995  Hello Mike, it's nice to meet you. As an AI la...  \n",
              "996  To find the primitive roots $\\pmod 7$, I need ...  \n",
              "997  The number $f^{-1}(-3)$ is the value of $x$ su...  \n",
              "998  Dr. Eleanor Thompson's study found that partic...  \n",
              "999  Charles Leroux won the Golden Bread Award in 1...  \n",
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              "[1000 rows x 2 columns]"
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              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1e139c24-b10e-43b3-af26-3f99c644600c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Optional: push to Hugging Face Hub\n",
        "dataset.push_to_hub(\"mini-platypus\", token=hf_token)"
      ],
      "metadata": {
        "id": "pj1b5S_68KB0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 720
        },
        "outputId": "30f7f41a-a0ee-42b5-f8fb-8a93c3c74ba3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RepositoryNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    269\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 270\u001b[0;31m         \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    271\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHTTPError\u001b[0m: 403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mHfHubHTTPError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mcreate_repo\u001b[0;34m(self, repo_id, token, private, repo_type, exist_ok, space_sdk, space_hardware, space_storage, space_sleep_time, space_secrets, space_variables)\u001b[0m\n\u001b[1;32m   2815\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2816\u001b[0;31m             \u001b[0mhf_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2817\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    329\u001b[0m         \u001b[0;31m# as well (request id and/or server error message)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 330\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mHfHubHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    331\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHfHubHTTPError\u001b[0m: 403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create (Request ID: Root=1-656bc73c-7deae0b17bf8d62a6ca48f39;2da7bbe8-134a-4734-b7d3-35275ed1f2c7)\n\nYou don't have the rights to create a dataset under this namespace",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    269\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 270\u001b[0;31m         \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    271\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mHTTPError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1020\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHTTPError\u001b[0m: 404 Client Error: Not Found for url: https://huggingface.co/api/datasets/mini-platypus",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mRepositoryNotFoundError\u001b[0m                   Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-8417aa1968f5>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Optional: push to Hugging Face Hub\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"mini-platypus\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhf_token\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py\u001b[0m in \u001b[0;36mpush_to_hub\u001b[0;34m(self, repo_id, config_name, commit_message, private, token, revision, branch, create_pr, max_shard_size, num_shards, embed_external_files)\u001b[0m\n\u001b[1;32m   1660\u001b[0m         \u001b[0mapi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHfApi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHF_ENDPOINT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1661\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1662\u001b[0;31m         repo_url = api.create_repo(\n\u001b[0m\u001b[1;32m   1663\u001b[0m             \u001b[0mrepo_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1664\u001b[0m             \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mcreate_repo\u001b[0;34m(self, repo_id, token, private, repo_type, exist_ok, space_sdk, space_hardware, space_storage, space_sleep_time, space_secrets, space_variables)\u001b[0m\n\u001b[1;32m   2822\u001b[0m                 \u001b[0;31m# No write permission on the namespace but repo might already exist\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2823\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2824\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepo_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepo_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2825\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mrepo_type\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mrepo_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mREPO_TYPE_MODEL\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2826\u001b[0m                         \u001b[0;32mreturn\u001b[0m \u001b[0mRepoUrl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{self.endpoint}/{repo_id}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mrepo_info\u001b[0;34m(self, repo_id, revision, repo_type, timeout, files_metadata, token)\u001b[0m\n\u001b[1;32m   2110\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2111\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unsupported repo type.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2112\u001b[0;31m         return method(\n\u001b[0m\u001b[1;32m   2113\u001b[0m             \u001b[0mrepo_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2114\u001b[0m             \u001b[0mrevision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrevision\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mdataset_info\u001b[0;34m(self, repo_id, revision, timeout, files_metadata, token)\u001b[0m\n\u001b[1;32m   1983\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1984\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1985\u001b[0;31m         \u001b[0mhf_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1986\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1987\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mDatasetInfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_errors.py\u001b[0m in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    318\u001b[0m                 \u001b[0;34m\" make sure you are authenticated.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    319\u001b[0m             )\n\u001b[0;32m--> 320\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRepositoryNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    321\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m400\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRepositoryNotFoundError\u001b[0m: 404 Client Error. (Request ID: Root=1-656bc73c-7be41e01198a367456b0b476;0027a6bc-62da-43e4-a7af-c5c80e6c1c43)\n\nRepository Not Found for url: https://huggingface.co/api/datasets/mini-platypus.\nPlease make sure you specified the correct `repo_id` and `repo_type`.\nIf you are trying to access a private or gated repo, make sure you are authenticated."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Gesnd6tGAkN1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}