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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ericyoc/huggingface-model-metadata-blockchain-poc/blob/main/huggingface_model_metadata_blockchain_poc.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "dfzsrXf0JxS5"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "import random\n",
        "import csv\n",
        "import hashlib\n",
        "import datetime\n",
        "import json\n",
        "from prettytable import PrettyTable\n",
        "from transformers import AutoModel, AutoTokenizer, AutoConfig"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_LIST_URL = \"https://huggingface.co/api/models\""
      ],
      "metadata": {
        "id": "4Kl0WNXRTbKJ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Block:\n",
        "    def __init__(self, index, timestamp, data, previous_hash):\n",
        "        self.index = index\n",
        "        self.timestamp = timestamp\n",
        "        self.data = data\n",
        "        self.previous_hash = previous_hash\n",
        "        self.hash = self.calculate_hash()\n",
        "\n",
        "    def calculate_hash(self):\n",
        "        return hashlib.sha256(\n",
        "            str(self.index).encode('utf-8') +\n",
        "            str(self.timestamp).encode('utf-8') +\n",
        "            str(self.data).encode('utf-8') +\n",
        "            str(self.previous_hash).encode('utf-8')\n",
        "        ).hexdigest()\n",
        "\n",
        "class Blockchain:\n",
        "    def __init__(self):\n",
        "        self.chain = [self.create_genesis_block()]\n",
        "\n",
        "    def create_genesis_block(self):\n",
        "        return Block(0, datetime.datetime.now(), \"Genesis Block\", \"0\")\n",
        "\n",
        "    def add_block(self, data):\n",
        "        previous_block = self.chain[-1]\n",
        "        new_block = Block(len(self.chain), datetime.datetime.now(), data, previous_block.hash)\n",
        "        self.chain.append(new_block)\n",
        "\n",
        "    def print_chain(self):\n",
        "        for block in self.chain:\n",
        "            print(\"Block #\", block.index)\n",
        "            print(\"Timestamp:\", block.timestamp)\n",
        "            print(\"Data:\", block.data)\n",
        "            print(\"Previous Hash:\", block.previous_hash)\n",
        "            print(\"Hash:\", block.hash)\n",
        "            print()\n",
        "\n",
        "    def serialize_chain(self):\n",
        "        serialized_chain = []\n",
        "        for block in self.chain:\n",
        "            serialized_block = {\n",
        "                \"index\": block.index,\n",
        "                \"timestamp\": block.timestamp.isoformat(),\n",
        "                \"data\": block.data,\n",
        "                \"previous_hash\": block.previous_hash,\n",
        "                \"hash\": block.hash\n",
        "            }\n",
        "            serialized_chain.append(serialized_block)\n",
        "        return serialized_chain"
      ],
      "metadata": {
        "id": "RynxFRqYTjOZ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def get_random_models(n=10):\n",
        "    response = requests.get(MODEL_LIST_URL)\n",
        "    if response.status_code == 200:\n",
        "        models = response.json()\n",
        "        return random.sample(models, n)\n",
        "    else:\n",
        "        print(f\"Failed to fetch model list. Status code: {response.status_code}\")\n",
        "        return []"
      ],
      "metadata": {
        "id": "AKhk-0s4ToZj"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def load_model_and_tokenizer(model_name):\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "    model = AutoModel.from_pretrained(model_name)\n",
        "    return model, tokenizer"
      ],
      "metadata": {
        "id": "msWf2UOZTqyu"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_model_config(model_name):\n",
        "    return AutoConfig.from_pretrained(model_name)"
      ],
      "metadata": {
        "id": "PpHebIB7TuG4"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_model_parameters(model):\n",
        "    return [(name, param) for name, param in model.named_parameters()]"
      ],
      "metadata": {
        "id": "lpxj1MrkTxPp"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_tokenizer_info(tokenizer):\n",
        "    return {\n",
        "        \"vocab_size\": tokenizer.vocab_size,\n",
        "        \"special_tokens\": tokenizer.special_tokens_map\n",
        "    }"
      ],
      "metadata": {
        "id": "Sra0Cq8YT0T2"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def fetch_model_metadata(model_name):\n",
        "    url = f\"https://huggingface.co/api/models/{model_name}\"\n",
        "    response = requests.get(url)\n",
        "    if response.status_code == 200:\n",
        "        return response.json()\n",
        "    else:\n",
        "        print(f\"Failed to fetch model information for {model_name}. Status code: {response.status_code}\")\n",
        "        return None"
      ],
      "metadata": {
        "id": "KObQYvLhT273"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def classify_model(model_metadata):\n",
        "    tags = model_metadata.get(\"tags\", [])\n",
        "\n",
        "    categories = {\n",
        "        \"multimodal\": [\"multimodal\"],\n",
        "        \"computer vision\": [\"image-classification\", \"image-segmentation\", \"object-detection\", \"vision\"],\n",
        "        \"natural language processing\": [\"text-classification\", \"token-classification\", \"question-answering\", \"text-generation\", \"translation\", \"nlp\"],\n",
        "        \"audio\": [\"audio-classification\", \"speech-recognition\", \"audio\"],\n",
        "        \"tabular\": [\"tabular-classification\", \"tabular\"],\n",
        "        \"reinforcement learning\": [\"reinforcement-learning\"],\n",
        "        \"other\": [\"graph-machine-learning\", \"graph\"]\n",
        "    }\n",
        "\n",
        "    for category, category_tags in categories.items():\n",
        "        if any(tag in tags for tag in category_tags):\n",
        "            return category\n",
        "\n",
        "    return \"other\""
      ],
      "metadata": {
        "id": "2j6XpPFqT5QV"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_final_table():\n",
        "    table = PrettyTable()\n",
        "    table.field_names = [\n",
        "        \"Model Name\", \"Architecture\", \"Vocabulary Size\", \"Special Tokens\",\n",
        "        \"Model Category\", \"Model Type\", \"Last Modified\"\n",
        "    ]\n",
        "    return table"
      ],
      "metadata": {
        "id": "m1w_u2NKT8Qs"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def add_model_to_table(table, config, tokenizer_info, model_category, model_name, model_type, last_modified):\n",
        "    table.add_row([\n",
        "        model_name,\n",
        "        config.architectures[0] if config.architectures else \"N/A\",\n",
        "        tokenizer_info[\"vocab_size\"],\n",
        "        tokenizer_info[\"special_tokens\"],\n",
        "        model_category,\n",
        "        model_type,\n",
        "        last_modified\n",
        "    ])"
      ],
      "metadata": {
        "id": "-QyeQmapT-aN"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def save_table_to_csv(table, file_name):\n",
        "    with open(file_name, mode='w', newline='') as file:\n",
        "        writer = csv.writer(file)\n",
        "        writer.writerow(table.field_names)\n",
        "        writer.writerows(table._rows)"
      ],
      "metadata": {
        "id": "I7_Ta3BxUAxT"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def save_blockchain_to_file(blockchain, file_name):\n",
        "    serialized_chain = blockchain.serialize_chain()\n",
        "    with open(file_name, \"w\") as file:\n",
        "        json.dump(serialized_chain, file, indent=4)"
      ],
      "metadata": {
        "id": "I3UgjUrzUC99"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def main():\n",
        "    # Get random models\n",
        "    random_models = get_random_models(10)\n",
        "    final_table = create_final_table()\n",
        "    blockchain = Blockchain()\n",
        "\n",
        "    for model_info in random_models:\n",
        "        model_name = model_info['modelId']\n",
        "\n",
        "        try:\n",
        "            # Load model and tokenizer\n",
        "            model, tokenizer = load_model_and_tokenizer(model_name)\n",
        "\n",
        "            # Get model configuration\n",
        "            config = get_model_config(model_name)\n",
        "\n",
        "            # Get tokenizer information\n",
        "            tokenizer_info = get_tokenizer_info(tokenizer)\n",
        "\n",
        "            # Fetch additional metadata from Hugging Face API\n",
        "            model_metadata = fetch_model_metadata(model_name)\n",
        "\n",
        "            # Classify the model\n",
        "            model_category = classify_model(model_metadata)\n",
        "\n",
        "            # Get model type and last modified date\n",
        "            model_type = model_metadata.get(\"pipeline_tag\", \"N/A\")\n",
        "            last_modified = model_metadata.get(\"lastModified\", \"N/A\")\n",
        "\n",
        "            # Add model information to the final table\n",
        "            add_model_to_table(\n",
        "                final_table, config, tokenizer_info,\n",
        "                model_category, model_name, model_type, last_modified\n",
        "            )\n",
        "\n",
        "            # Add model information to the blockchain\n",
        "            blockchain.add_block({\n",
        "                \"Model Name\": model_name,\n",
        "                \"Architecture\": config.architectures[0] if config.architectures else \"N/A\",\n",
        "                \"Vocabulary Size\": tokenizer_info[\"vocab_size\"],\n",
        "                \"Special Tokens\": tokenizer_info[\"special_tokens\"],\n",
        "                \"Model Category\": model_category,\n",
        "                \"Model Type\": model_type,\n",
        "                \"Last Modified\": last_modified\n",
        "            })\n",
        "\n",
        "        except Exception as e:\n",
        "            print(f\"Error processing model {model_name}: {e}\")\n",
        "\n",
        "    # Print the final table\n",
        "    print(final_table)\n",
        "\n",
        "    # Save table to CSV\n",
        "    save_table_to_csv(final_table, \"models_metadata.csv\")\n",
        "    print(\"Table saved to models_metadata.csv\")\n",
        "\n",
        "    # Save the blockchain to a file\n",
        "    save_blockchain_to_file(blockchain, \"blockchain_data.json\")\n",
        "    print(\"Blockchain data saved to blockchain_data.json\")\n",
        "\n",
        "    # Print the blockchain\n",
        "    print(\"\\nBlockchain:\")\n",
        "    blockchain.print_chain()"
      ],
      "metadata": {
        "id": "55WikJ62UFEb"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
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          "height": 1000,
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        },
        "id": "8IlMPzp_UGnB",
        "outputId": "fcd52eae-bfb1-4d54-ef6a-a07f8dca6166"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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": "stream",
          "name": "stdout",
          "text": [
            "Error processing model Andy1621/uniformer: Andy1621/uniformer does not appear to have a file named config.json. Checkout 'https://huggingface.co/Andy1621/uniformer/main' for available files.\n",
            "Error processing model Aleksandar/bert-srb-ner-setimes-lr: Aleksandar/bert-srb-ner-setimes-lr does not appear to have a file named config.json. Checkout 'https://huggingface.co/Aleksandar/bert-srb-ner-setimes-lr/main' for available files.\n",
            "Error processing model AdapterHub/roberta-base-pf-duorc_p: AdapterHub/roberta-base-pf-duorc_p does not appear to have a file named config.json. Checkout 'https://huggingface.co/AdapterHub/roberta-base-pf-duorc_p/main' for available files.\n",
            "Error processing model AJ/rick-sanchez-bot: AJ/rick-sanchez-bot does not appear to have a file named config.json. Checkout 'https://huggingface.co/AJ/rick-sanchez-bot/main' for available files.\n"
          ]
        },
        {
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              "model_id": "11086d3f123949c388546f15883729d6"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of AlbertModel were not initialized from the model checkpoint at Akari/albert-base-v2-finetuned-squad and are newly initialized: ['albert.pooler.bias', 'albert.pooler.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": [
            "Error processing model ArcQ/gpt-experiments: ArcQ/gpt-experiments does not appear to have a file named config.json. Checkout 'https://huggingface.co/ArcQ/gpt-experiments/main' for available files.\n",
            "Error processing model Aviora/news2vec: Aviora/news2vec does not appear to have a file named config.json. Checkout 'https://huggingface.co/Aviora/news2vec/main' for available files.\n"
          ]
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f94895823ed34add9c13d6f818137f45"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "060c84ffc5fb40e38b402cd421d1e144"
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          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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            "application/vnd.jupyter.widget-view+json": {
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        {
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            "application/vnd.jupyter.widget-view+json": {
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            "application/vnd.jupyter.widget-view+json": {
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            "application/vnd.jupyter.widget-view+json": {
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        {
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        {
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n"
          ]
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "df8e0d16c5f64c399d893a1a56380978"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+------------------------------------------------------------------+-------------------------------+-----------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------+----------------------+--------------------------+\n",
            "|                            Model Name                            |          Architecture         | Vocabulary Size |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Special Tokens                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |        Model Category       |      Model Type      |      Last Modified       |\n",
            "+------------------------------------------------------------------+-------------------------------+-----------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------+----------------------+--------------------------+\n",
            "|               Akari/albert-base-v2-finetuned-squad               |   AlbertForQuestionAnswering  |      30000      |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                {'bos_token': '[CLS]', 'eos_token': '[SEP]', 'unk_token': '<unk>', 'sep_token': '[SEP]', 'pad_token': '<pad>', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                | natural language processing |  question-answering  | 2021-12-02T05:36:13.000Z |\n",
            "| AnonymousSub/rule_based_twostagequadruplet_hier_epochs_1_shard_1 |           BertModel           |      30522      |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      {'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |            other            |  feature-extraction  | 2022-01-10T21:09:40.000Z |\n",
            "| AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1_wikiqa  | BertForSequenceClassification |      30522      |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      {'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | natural language processing | text-classification  | 2022-01-23T01:42:47.000Z |\n",
            "|                         0x7o/keyt5-base                          |   T5ForConditionalGeneration  |      32100      | {'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>', 'additional_special_tokens': ['<extra_id_0>', '<extra_id_1>', '<extra_id_2>', '<extra_id_3>', '<extra_id_4>', '<extra_id_5>', '<extra_id_6>', '<extra_id_7>', '<extra_id_8>', '<extra_id_9>', '<extra_id_10>', '<extra_id_11>', '<extra_id_12>', '<extra_id_13>', '<extra_id_14>', '<extra_id_15>', '<extra_id_16>', '<extra_id_17>', '<extra_id_18>', '<extra_id_19>', '<extra_id_20>', '<extra_id_21>', '<extra_id_22>', '<extra_id_23>', '<extra_id_24>', '<extra_id_25>', '<extra_id_26>', '<extra_id_27>', '<extra_id_28>', '<extra_id_29>', '<extra_id_30>', '<extra_id_31>', '<extra_id_32>', '<extra_id_33>', '<extra_id_34>', '<extra_id_35>', '<extra_id_36>', '<extra_id_37>', '<extra_id_38>', '<extra_id_39>', '<extra_id_40>', '<extra_id_41>', '<extra_id_42>', '<extra_id_43>', '<extra_id_44>', '<extra_id_45>', '<extra_id_46>', '<extra_id_47>', '<extra_id_48>', '<extra_id_49>', '<extra_id_50>', '<extra_id_51>', '<extra_id_52>', '<extra_id_53>', '<extra_id_54>', '<extra_id_55>', '<extra_id_56>', '<extra_id_57>', '<extra_id_58>', '<extra_id_59>', '<extra_id_60>', '<extra_id_61>', '<extra_id_62>', '<extra_id_63>', '<extra_id_64>', '<extra_id_65>', '<extra_id_66>', '<extra_id_67>', '<extra_id_68>', '<extra_id_69>', '<extra_id_70>', '<extra_id_71>', '<extra_id_72>', '<extra_id_73>', '<extra_id_74>', '<extra_id_75>', '<extra_id_76>', '<extra_id_77>', '<extra_id_78>', '<extra_id_79>', '<extra_id_80>', '<extra_id_81>', '<extra_id_82>', '<extra_id_83>', '<extra_id_84>', '<extra_id_85>', '<extra_id_86>', '<extra_id_87>', '<extra_id_88>', '<extra_id_89>', '<extra_id_90>', '<extra_id_91>', '<extra_id_92>', '<extra_id_93>', '<extra_id_94>', '<extra_id_95>', '<extra_id_96>', '<extra_id_97>', '<extra_id_98>', '<extra_id_99>']} |            other            | text2text-generation | 2022-01-11T03:52:53.000Z |\n",
            "+------------------------------------------------------------------+-------------------------------+-----------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------+----------------------+--------------------------+\n",
            "Table saved to models_metadata.csv\n",
            "Blockchain data saved to blockchain_data.json\n",
            "\n",
            "Blockchain:\n",
            "Block # 0\n",
            "Timestamp: 2024-05-17 16:09:30.577741\n",
            "Data: Genesis Block\n",
            "Previous Hash: 0\n",
            "Hash: 201577e05a4fc8fc08070d1a38c1a373d507f670c4768480c5a01c451d7cbaa2\n",
            "\n",
            "Block # 1\n",
            "Timestamp: 2024-05-17 16:09:40.974353\n",
            "Data: {'Model Name': 'Akari/albert-base-v2-finetuned-squad', 'Architecture': 'AlbertForQuestionAnswering', 'Vocabulary Size': 30000, 'Special Tokens': {'bos_token': '[CLS]', 'eos_token': '[SEP]', 'unk_token': '<unk>', 'sep_token': '[SEP]', 'pad_token': '<pad>', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, 'Model Category': 'natural language processing', 'Model Type': 'question-answering', 'Last Modified': '2021-12-02T05:36:13.000Z'}\n",
            "Previous Hash: 201577e05a4fc8fc08070d1a38c1a373d507f670c4768480c5a01c451d7cbaa2\n",
            "Hash: 028ba86f4ea485aeffdc2147c4927bc6d5e4fe6145ec8dde04b04373ab5a953c\n",
            "\n",
            "Block # 2\n",
            "Timestamp: 2024-05-17 16:09:52.918372\n",
            "Data: {'Model Name': 'AnonymousSub/rule_based_twostagequadruplet_hier_epochs_1_shard_1', 'Architecture': 'BertModel', 'Vocabulary Size': 30522, 'Special Tokens': {'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, 'Model Category': 'other', 'Model Type': 'feature-extraction', 'Last Modified': '2022-01-10T21:09:40.000Z'}\n",
            "Previous Hash: 028ba86f4ea485aeffdc2147c4927bc6d5e4fe6145ec8dde04b04373ab5a953c\n",
            "Hash: 1686c889801d988b1f7af2ceab402621b40a66815195f23d05be70f0c187bc37\n",
            "\n",
            "Block # 3\n",
            "Timestamp: 2024-05-17 16:09:58.646899\n",
            "Data: {'Model Name': 'AnonymousSub/rule_based_hier_quadruplet_epochs_1_shard_1_wikiqa', 'Architecture': 'BertForSequenceClassification', 'Vocabulary Size': 30522, 'Special Tokens': {'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, 'Model Category': 'natural language processing', 'Model Type': 'text-classification', 'Last Modified': '2022-01-23T01:42:47.000Z'}\n",
            "Previous Hash: 1686c889801d988b1f7af2ceab402621b40a66815195f23d05be70f0c187bc37\n",
            "Hash: 47ac7d44f1f5c3fcad9f40facae47820b1743c8bdc47c36c7d0db76d0d03b551\n",
            "\n",
            "Block # 4\n",
            "Timestamp: 2024-05-17 16:10:27.566505\n",
            "Data: {'Model Name': '0x7o/keyt5-base', 'Architecture': 'T5ForConditionalGeneration', 'Vocabulary Size': 32100, 'Special Tokens': {'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>', 'additional_special_tokens': ['<extra_id_0>', '<extra_id_1>', '<extra_id_2>', '<extra_id_3>', '<extra_id_4>', '<extra_id_5>', '<extra_id_6>', '<extra_id_7>', '<extra_id_8>', '<extra_id_9>', '<extra_id_10>', '<extra_id_11>', '<extra_id_12>', '<extra_id_13>', '<extra_id_14>', '<extra_id_15>', '<extra_id_16>', '<extra_id_17>', '<extra_id_18>', '<extra_id_19>', '<extra_id_20>', '<extra_id_21>', '<extra_id_22>', '<extra_id_23>', '<extra_id_24>', '<extra_id_25>', '<extra_id_26>', '<extra_id_27>', '<extra_id_28>', '<extra_id_29>', '<extra_id_30>', '<extra_id_31>', '<extra_id_32>', '<extra_id_33>', '<extra_id_34>', '<extra_id_35>', '<extra_id_36>', '<extra_id_37>', '<extra_id_38>', '<extra_id_39>', '<extra_id_40>', '<extra_id_41>', '<extra_id_42>', '<extra_id_43>', '<extra_id_44>', '<extra_id_45>', '<extra_id_46>', '<extra_id_47>', '<extra_id_48>', '<extra_id_49>', '<extra_id_50>', '<extra_id_51>', '<extra_id_52>', '<extra_id_53>', '<extra_id_54>', '<extra_id_55>', '<extra_id_56>', '<extra_id_57>', '<extra_id_58>', '<extra_id_59>', '<extra_id_60>', '<extra_id_61>', '<extra_id_62>', '<extra_id_63>', '<extra_id_64>', '<extra_id_65>', '<extra_id_66>', '<extra_id_67>', '<extra_id_68>', '<extra_id_69>', '<extra_id_70>', '<extra_id_71>', '<extra_id_72>', '<extra_id_73>', '<extra_id_74>', '<extra_id_75>', '<extra_id_76>', '<extra_id_77>', '<extra_id_78>', '<extra_id_79>', '<extra_id_80>', '<extra_id_81>', '<extra_id_82>', '<extra_id_83>', '<extra_id_84>', '<extra_id_85>', '<extra_id_86>', '<extra_id_87>', '<extra_id_88>', '<extra_id_89>', '<extra_id_90>', '<extra_id_91>', '<extra_id_92>', '<extra_id_93>', '<extra_id_94>', '<extra_id_95>', '<extra_id_96>', '<extra_id_97>', '<extra_id_98>', '<extra_id_99>']}, 'Model Category': 'other', 'Model Type': 'text2text-generation', 'Last Modified': '2022-01-11T03:52:53.000Z'}\n",
            "Previous Hash: 47ac7d44f1f5c3fcad9f40facae47820b1743c8bdc47c36c7d0db76d0d03b551\n",
            "Hash: 5b6f7e586e6026f015802dac2b4a5c0893272fa4c8695a5f6e67371bdd3bca83\n",
            "\n"
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}