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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/Nov05/Google-Colaboratory/blob/master/20230908_loading_huggingface_models.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **\\<TOP>**\n",
        "\n",
        "created by nov05 on 2023-09-08  \n",
        "  "
      ],
      "metadata": {
        "id": "XAYZBpvQDg8b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **how to load deberta-v3 properly**  \n",
        "https://stackoverflow.com/questions/76868251/how-to-load-deberta-v3-properly    \n",
        "\n",
        "* github issue: **Load deberta-v3-large but got deberta-v2 model** #132   \n",
        "https://github.com/microsoft/DeBERTa/issues/132  \n",
        "\n",
        "* 2022-kaggle-nlp赛事: **Feedback Prize - English Language Learning**      \n",
        "发布于 2023-06-24 16:31  \n",
        "https://zhuanlan.zhihu.com/p/639275167  \n",
        "\n",
        "* [**Feedback 3 competition review**] I tried a good method of Winning Solution  \n",
        "Published on 2022/12/19  \n",
        "https://zenn.dev/aidemy/articles/73886662eda995, ([google translate](https://zenn-dev.translate.goog/aidemy/articles/73886662eda995?_x_tr_sl=auto&_x_tr_tl=en-EN&_x_tr_hl=en&_x_tr_pto=wapp&_x_tr_hist=true))  "
      ],
      "metadata": {
        "id": "jSry9U7HGSK7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## clear model files in cache\n",
        "!rm -rf /root/.cache/huggingface/hub"
      ],
      "metadata": {
        "id": "me6PJMZsnTFk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "## Successfully installed huggingface-hub-0.16.4 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.33.1"
      ],
      "metadata": {
        "id": "pyqU2twuG3ht"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **download huggingface repo**  \n",
        "\n",
        "* Hugging Face - Downloading models  \n",
        "https://huggingface.co/docs/hub/models-downloading  \n",
        "\n",
        "* get access tokens  \n",
        "https://huggingface.co/settings/tokens  \n",
        "\n",
        "* git over ssh  \n",
        "https://huggingface.co/docs/hub/security-git-ssh  "
      ],
      "metadata": {
        "id": "8Z8mW-cHTeSs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **python library huggingface_hub**"
      ],
      "metadata": {
        "id": "k0GbAFEaDvhW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface_hub"
      ],
      "metadata": {
        "id": "-QT2M2Rmc8b8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import hf_hub_download\n",
        "REPO_ID = \"microsoft/deberta-v3-base\" ## https://huggingface.co/microsoft/deberta-v3-base/tree/main\n",
        "FILENAME = \"config.json\"\n",
        "config_json = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)\n",
        "config_json\n",
        "## /root/.cache/huggingface/hub/models--microsoft--deberta-v3-base/snapshots/8ccc9b6f36199bec6961081d44eb72fb3f7353f3/config.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "gTvHoONDfFvt",
        "outputId": "9a45504e-7b33-4b55-a836-6922938f50a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/root/.cache/huggingface/hub/models--microsoft--deberta-v3-base/snapshots/8ccc9b6f36199bec6961081d44eb72fb3f7353f3/config.json'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "print(pickle.format_version) ## colab default pickle 4.0\n",
        "import joblib\n",
        "print(joblib.__version__) ## colab default joblib 1.3.2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iQZOk5zDgMB-",
        "outputId": "bde28c25-f362-44c5-e2b9-5c94fe4dc97e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.0\n",
            "1.3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **git clone**  "
      ],
      "metadata": {
        "id": "nqGILCZeN9le"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git lfs install"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVzdx9PBTkAC",
        "outputId": "95e7af38-abda-4827-85e8-d33c6f107b2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Git LFS initialized.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !git clone git@hf.co:microsoft/deberta-v3-base\n",
        "!git clone https://huggingface.co/microsoft/deberta-v3-base\n",
        "## downloaded to /content/deberta-v3-base"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6AZ1pl0LTt4U",
        "outputId": "5e766b44-7f7f-45f7-a33b-3ca82788da7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'deberta-v3-base'...\n",
            "remote: Enumerating objects: 59, done.\u001b[K\n",
            "remote: Total 59 (delta 0), reused 0 (delta 0), pack-reused 59\u001b[K\n",
            "Unpacking objects: 100% (59/59), 8.56 KiB | 417.00 KiB/s, done.\n",
            "Filtering content: 100% (4/4), 1.72 GiB | 28.46 MiB/s, done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "【TIPS】  \n",
        "when you run `!git clone git@hf.co:microsoft/deberta-v3-base`.  \n",
        "you might run into the following error.  \n",
        "\n",
        "```\n",
        "Cloning into 'deberta-v3-base'...\n",
        "Host key verification failed.\n",
        "fatal: Could not read from remote repository.\n",
        "\n",
        "Please make sure you have the correct access rights\n",
        "and the repository exists.\n",
        "```\n",
        "\n",
        "well, just use https.   \n",
        "`!git clone https://huggingface.co/microsoft/deberta-v3-base`  \n",
        "\n",
        "* git@github.com: **Permission denied (publickey) when fetching** #1943  \n",
        "Closed, iedmrc opened this issue on Nov 25, 2019    \n",
        "https://github.com/huggingface/transformers/issues/1943  "
      ],
      "metadata": {
        "id": "bNWl-OaheGCo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **huggingface-cli**  "
      ],
      "metadata": {
        "id": "K-af5kVWD4vp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login\n",
        "## Your token has been saved to /root/.cache/huggingface/token"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "urHmLc91dWSJ",
        "outputId": "5e160e38-5bdb-424e-a2ba-3eac738f331e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: read).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git config --global credential.helper store"
      ],
      "metadata": {
        "id": "0r4XioAeeuVe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **microsoft/deberta-v3-base**   \n",
        "https://huggingface.co/microsoft/deberta-v3-base    "
      ],
      "metadata": {
        "id": "Q8jHrnv5SmGd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel, AutoConfig, AutoTokenizer\n",
        "MODEL_NAME = 'microsoft/deberta-v3-base'\n",
        "model = AutoModel.from_pretrained(MODEL_NAME)\n",
        "## no error messages"
      ],
      "metadata": {
        "id": "M3MxUla1Mru0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "00e8a53c813648b0abdac9a23ea4b81a",
            "fa58910f15be42358ad9ee5599993be8",
            "71e983bca74c430d9a06189f9213ed6a",
            "078317f63be14146994379e2dbcb9d54",
            "fb9b3076c7144018b42dc95257498658",
            "832388b716c24f7da656022f471d6e80",
            "1dd5bb14e4a845c2a74212a4ab30d6f1",
            "e24d120362444356b3de8082107bf557",
            "0754f306f4cc49a28aabc28cfd154e38",
            "a0e99757fe664916b9f63d9d54823a6a",
            "6c5c261de8d249b1815cf2703bd0843e",
            "d2d0f91011fc47cb88d1ff9cd773a3f7",
            "953dfbbe16f6462998e1bb7d647ce5a5",
            "1a44552e549a489cb102a311320a8285",
            "b142f80e0bf7485681a40ed1e6a45b21",
            "268e47539d494a08aa8738c98a62b3a1",
            "625347bdadf94a28a91beca013bab44a",
            "3c252766fe1a409ca4ae37442e95d526",
            "cac41da48e394f6ab426f2d90b1cd770",
            "49445adbf5574eccbc51561ff414b2e6",
            "88e0a189a39d43daba24bcbafcb2bb1e",
            "e02412263f174ea49e73db023ba34a06"
          ]
        },
        "outputId": "02fd9047-4edd-4518-f971-e1cfbc44ed63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/579 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "00e8a53c813648b0abdac9a23ea4b81a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/371M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d2d0f91011fc47cb88d1ff9cd773a3f7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "config = AutoConfig.from_pretrained(MODEL_NAME)\n",
        "config\n",
        "## no error messages\n",
        "## note that it is DebertaV2."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DAJElW5JcTgs",
        "outputId": "7c81bd71-f8e9-479d-d5bd-590e5216c383"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DebertaV2Config {\n",
              "  \"_name_or_path\": \"microsoft/deberta-v3-base\",\n",
              "  \"attention_probs_dropout_prob\": 0.1,\n",
              "  \"hidden_act\": \"gelu\",\n",
              "  \"hidden_dropout_prob\": 0.1,\n",
              "  \"hidden_size\": 768,\n",
              "  \"initializer_range\": 0.02,\n",
              "  \"intermediate_size\": 3072,\n",
              "  \"layer_norm_eps\": 1e-07,\n",
              "  \"max_position_embeddings\": 512,\n",
              "  \"max_relative_positions\": -1,\n",
              "  \"model_type\": \"deberta-v2\",\n",
              "  \"norm_rel_ebd\": \"layer_norm\",\n",
              "  \"num_attention_heads\": 12,\n",
              "  \"num_hidden_layers\": 12,\n",
              "  \"pad_token_id\": 0,\n",
              "  \"pooler_dropout\": 0,\n",
              "  \"pooler_hidden_act\": \"gelu\",\n",
              "  \"pooler_hidden_size\": 768,\n",
              "  \"pos_att_type\": [\n",
              "    \"p2c\",\n",
              "    \"c2p\"\n",
              "  ],\n",
              "  \"position_biased_input\": false,\n",
              "  \"position_buckets\": 256,\n",
              "  \"relative_attention\": true,\n",
              "  \"share_att_key\": true,\n",
              "  \"transformers_version\": \"4.33.1\",\n",
              "  \"type_vocab_size\": 0,\n",
              "  \"vocab_size\": 128100\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
        "## no error messages"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 172,
          "referenced_widgets": [
            "08447603679b4b98873743a9a2eab021",
            "f2437c8baad7460aa8e663e23248c86b",
            "369f337db6d24d2db5f30a59d4e90db1",
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            "9333bbd323e24ef8acb014714230b229",
            "48390ef91ff64cdf9e4e741d6d105118",
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            "e1180535f20c455ab87b14f44cc459a6",
            "964efc4ab5f946e5b2bc38320fc067c9",
            "03ca7947b3a341f3b92983b90649bb64",
            "e6e3df7135854a97a84f5d67502373a4",
            "200ca0ab66c2414e8b37918e02aa3684",
            "385d2f272d8a4c1386a94c998749f73d",
            "58a2c87a5f62492ebf0fd4c76f36e9d6",
            "d410edb858364525926fb3c602d93a51",
            "2d6a58727f3747b3bd9bb1cd39e76f63",
            "77ebb2a297c64cd5a0f08a5cf6e2253c",
            "7cf2682cdc3641f0808031502999a06a",
            "c1c6a2ac4260489eade662b9423d0ca4"
          ]
        },
        "id": "DbITf5xBchnE",
        "outputId": "7264b00e-0c42-41bf-863e-cd28968c4dce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "08447603679b4b98873743a9a2eab021"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spm.model:   0%|          | 0.00/2.46M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "964efc4ab5f946e5b2bc38320fc067c9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/convert_slow_tokenizer.py:473: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
            "  warnings.warn(\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## https://github.com/google/sentencepiece#installation\n",
        "!pip install sentencepiece\n",
        "## Successfully installed sentencepiece-0.1.99\n",
        "## remember to restart the runtime"
      ],
      "metadata": {
        "id": "kMa41hyRdu_4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## clear model files in cache, then do it again\n",
        "!rm -rf /root/.cache/huggingface/hub\n",
        "\n",
        "from transformers import AutoModel, AutoConfig, AutoTokenizer\n",
        "MODEL_NAME = 'microsoft/deberta-v3-base'\n",
        "model = AutoModel.from_pretrained(MODEL_NAME)\n",
        "config = AutoConfig.from_pretrained(MODEL_NAME)\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
        "## no error messages\n",
        "\n",
        "## model files are stored here.\n",
        "## /root/.cache/huggingface/hub/models--microsoft--deberta-v3-base"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236,
          "referenced_widgets": [
            "8f3cde33d9db46618a04c9b98702c2a1",
            "09cf1ea17afb4fe0a3938b660ddecd24",
            "1f3b2fb1f47f4ed68da53518d751d6af",
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            "69cdafef4c6d40b68f4000e14a1794ef",
            "152958a4e92f4d3f91d2fac7f95aa000",
            "bb508402340f4ab4a0b9eb1389b2db3a",
            "3dbf1339fcf043fbb1850bedcd730883",
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            "774337976f6e462091954a17f75c227c",
            "1c853fa20eaa4b5b896c0f4cf28dd2e7",
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            "bbb58cc606d249af8d42c890b9814bcf",
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            "dcf10b13df424de8bd02790370556eaa",
            "68c3e3cd759e401f8377342b0b28e1be",
            "f7c4d834acf84a13a2099f624daeb662",
            "8d14817fff344a088d1bcf850516b46b",
            "6d8620504847484493c7dee2b027ffcc",
            "277d2d2bcf884c2db85d7bb4a0ddfe5a",
            "ee00713df7204b54838a69019487d19d",
            "7ef769ba18604922afaa5c453c0dea62",
            "831b7b5da0584e049a0776892ebd0d77",
            "c98f67382c2f47b2bb896685e4594bb9",
            "3b0464f139984b219d68e0a9edd49653"
          ]
        },
        "id": "HtXvH_u0mhgH",
        "outputId": "86d1a17e-f756-4b66-be1e-2e54a7824cc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/579 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8f3cde33d9db46618a04c9b98702c2a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/371M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "774337976f6e462091954a17f75c227c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f4dacb094ea84c758c89d6beff144791"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spm.model:   0%|          | 0.00/2.46M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dcf10b13df424de8bd02790370556eaa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/convert_slow_tokenizer.py:473: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
            "  warnings.warn(\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163,
          "referenced_widgets": [
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          ]
        },
        "id": "h3XvTg-_Fse_",
        "outputId": "18319869-45c8-44c4-91db-e04510004133"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/579 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0edb134095c34401b6160717efca1da3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/371M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "39490b08cec64a9baf6cd12727a0f284"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spm.model:   0%|          | 0.00/2.46M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a715199c9f9b4e2ea99de90a32ad5435"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c0551c6bb2484d1386e87b104e0a5074"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        }
      ],
      "source": [
        "## clear model files in cache, then do it with some other modules\n",
        "!rm -rf /root/.cache/huggingface/hub\n",
        "\n",
        "\"\"\"\n",
        "ImportError:\n",
        "DebertaV2Tokenizer requires the SentencePiece library but it was not found in your environment.\n",
        "Checkout the instructions on the installation page of its repo: https://github.com/google/sentencepiece#installation\n",
        "and follow the ones that match your environment. Please note that you may need to restart your runtime after installation.\n",
        "\"\"\"\n",
        "import sentencepiece\n",
        "\n",
        "from transformers import DebertaV2Model, DebertaV2Config, DebertaV2Tokenizer\n",
        "MODEL_NAME = 'microsoft/deberta-v3-base'\n",
        "model = DebertaV2Model.from_pretrained(MODEL_NAME)\n",
        "config = DebertaV2Config.from_pretrained(MODEL_NAME)\n",
        "tokenizer = DebertaV2Tokenizer.from_pretrained(MODEL_NAME)\n",
        "## no error messages"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **microsoft/deberta-base**  "
      ],
      "metadata": {
        "id": "lyKqP0B4Sxuv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import DebertaModel, DebertaConfig, DebertaTokenizer\n",
        "MODEL_NAME = 'microsoft/deberta-base'\n",
        "model = DebertaModel.from_pretrained(MODEL_NAME)\n",
        "config = DebertaConfig.from_pretrained(MODEL_NAME)\n",
        "tokenizer = DebertaTokenizer.from_pretrained(MODEL_NAME)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
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        "id": "jIAOot65OneC",
        "outputId": "5bde7061-e655-401b-bea1-d185a9d06612"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a12384256f44785a6d1105331b6cb85"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "18eb089fac8b49fc913e0675e3f9004d"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "d7997f848f0041d2abb9568633524625"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)okenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel, AutoConfig, AutoTokenizer\n",
        "MODEL_NAME = 'microsoft/deberta-base'\n",
        "model = AutoModel.from_pretrained(MODEL_NAME)\n",
        "config = AutoConfig.from_pretrained(MODEL_NAME) ## Configuration loaded from AutoConfig\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) ## Tokenizer loaded from AutoTokenizer"
      ],
      "metadata": {
        "id": "YHRgXzTkR3Wo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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        "outputId": "13841bce-ff0e-4322-fc6d-ef564a611589"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/474 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b76607c5052d4818a7940087141e511c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/559M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba897cf7c46041c9a5bd0949b017884d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d7860001e84e4e3d97e2c5326be577d8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1101512612354b0a9e676a6b84d16a75"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1b2eb6c64cbf4bee983d34e910035c20"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **bert-base-uncased**  "
      ],
      "metadata": {
        "id": "jNV2GbeDS4i3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## example code\n",
        "if 0:\n",
        "    from transformers import BertConfig, BertModel\n",
        "\n",
        "    ## Download model and configuration from huggingface.co and cache.\n",
        "    model = BertModel.from_pretrained(\"bert-base-uncased\")\n",
        "\n",
        "    ## Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).\n",
        "    model = BertModel.from_pretrained(\"./test/saved_model/\")\n",
        "\n",
        "    ## Update configuration during loading.\n",
        "    model = BertModel.from_pretrained(\"bert-base-uncased\", output_attentions=True)\n",
        "    assert model.config.output_attentions == True\n",
        "\n",
        "    ## Loading from a TF checkpoint file instead of a PyTorch model (slower, for example purposes, not runnable).\n",
        "    config = BertConfig.from_json_file(\"./tf_model/my_tf_model_config.json\")\n",
        "    model = BertModel.from_pretrained(\"./tf_model/my_tf_checkpoint.ckpt.index\", from_tf=True, config=config)\n",
        "\n",
        "    ## Loading from a Flax checkpoint file instead of a PyTorch model (slower)\n",
        "    model = BertModel.from_pretrained(\"bert-base-uncased\", from_flax=True)"
      ],
      "metadata": {
        "id": "GYDcUSJvLVJs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel, AutoConfig, AutoTokenizer\n",
        "MODEL_NAME = 'bert-base-uncased'\n",
        "model = AutoModel.from_pretrained(MODEL_NAME)\n",
        "config = AutoConfig.from_pretrained(MODEL_NAME)\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
        "## model files are store here.\n",
        "## /root/.cache/huggingface/hub/models--bert-base-uncased"
      ],
      "metadata": {
        "id": "xFDupe6IM9e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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        },
        "outputId": "6cca5b8c-3d7c-4e6b-998e-e473bf370162"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11f6a842709a4ab0ba145bc8630cf3da"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dd3c095f815f4ccbaf0888356deabb5c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "4b20e0dc78ca41ae8d611b026879b225"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "ae87b511554342bc8b3aaa9a2b316347"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BertModel, BertConfig, BertTokenizer\n",
        "model_path = \"/root/.cache/huggingface/hub/models--bert-base-uncased/snapshots/1dbc166cf8765166998eff31ade2eb64c8a40076\"\n",
        "model = BertModel.from_pretrained(model_path)\n",
        "config = BertConfig.from_pretrained(model_path)\n",
        "tokenizer = BertTokenizer.from_pretrained(model_path)\n",
        "## ran successfully"
      ],
      "metadata": {
        "id": "Ah9rW7QKqE10"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModel, AutoConfig, AutoTokenizer\n",
        "model_path = \"/root/.cache/huggingface/hub/models--bert-base-uncased/snapshots/1dbc166cf8765166998eff31ade2eb64c8a40076\"\n",
        "model = AutoModel.from_pretrained(model_path)\n",
        "config = AutoConfig.from_pretrained(model_path)\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
        "## ran successfully"
      ],
      "metadata": {
        "id": "RastvnKUqj0q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://raw.githubusercontent.com/Nov05/pictures/master/colab/2023-09-08%2016_23_56-20230908_transformers.AutoModel.from_pretrained('microsoft_deberta-v3-base').ipy-min.jpg\" width=800>"
      ],
      "metadata": {
        "id": "h2V2ePHDsNfU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **\\<BUTTOM>**"
      ],
      "metadata": {
        "id": "XIm3HawnscZU"
      }
    }
  ]
}