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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/mrm8488/shared_colab_notebooks/blob/master/HF_SetFit_spam.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SetFit for Text Classification | Spam or Ham\n",
        "\n",
        "SetFit is an efficient and prompt-free framework for few-shot fine-tuning of [Sentence Transformers](https://sbert.net/). It achieves high accuracy with little labeled data - for instance, with only 8 labeled examples per class on the Customer Reviews sentiment dataset, SetFit is competitive with fine-tuning RoBERTa Large on the full training set of 3k examples 🤯!\n",
        "\n",
        "- [Post](https://huggingface.co/blog/setfit)\n",
        "\n",
        "- [Paper](https://arxiv.org/abs/2209.11055)\n",
        "> Created by [Manu Romero](https://twitter.com/mrm8488) [Narrativa](https://narrativa.com/) NLP/G Senior Engineer and HuggingFace 🤗 Fellow and Ambassador,"
      ],
      "metadata": {
        "id": "r2VU_QssIS6-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gm6eymYhDzlg",
        "outputId": "bd25bb6f-bdef-40df-e671-da5e76bbafda"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Oct 10 17:43:21 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   68C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "wZPcwhboB8md"
      },
      "outputs": [],
      "source": [
        "! pip install -q transformers datasets setfit"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RBQPMUvnEUpf",
        "outputId": "5ee67231-ef36-4337-b17a-ce608cf0a9f3"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "        _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "        _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "        To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/tokens .\n",
            "        \n",
            "Token: \n",
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! git config --global credential.helper store"
      ],
      "metadata": {
        "id": "FIQwJyikFvu2"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, concatenate_datasets\n",
        "from sentence_transformers.losses import CosineSimilarityLoss\n",
        "\n",
        "from setfit import SetFitModel, SetFitTrainer"
      ],
      "metadata": {
        "id": "NsM0VNeiD8Rn"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"sms_spam\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "dc6267c6ba464216ad11521ac8861597",
            "21e8d7ab140f49099b219959aed58599",
            "1d4d4d13d8b94dab80e5e856d16cf8a5",
            "1d340af0c44643ccba087569bff353bd",
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        },
        "id": "WWnMFbbBD_Yb",
        "outputId": "cbc07816-ce85-489f-f949-316287f1ec8b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Reusing dataset sms_spam (/root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "dc6267c6ba464216ad11521ac8861597"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFitm6d0EGLG",
        "outputId": "3ee94ad5-2260-4ef1-d666-99a50d0c653e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['sms', 'label'],\n",
              "        num_rows: 5574\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset['train'].train_test_split(test_size=0.15, shuffle=True, seed=49)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VMMlTmnF6c0g",
        "outputId": "8a98786c-583e-49df-d34e-098cc83ea184"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached split indices for dataset at /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c/cache-e1402c953e7520b4.arrow and /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c/cache-19a7de1c57beb248.arrow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BIi9NGgG6tJ5",
        "outputId": "8fc1548c-34ee-4a2b-dc76-1f3228554d6d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['sms', 'label'],\n",
              "        num_rows: 4737\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['sms', 'label'],\n",
              "        num_rows: 837\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "i3j8APAoIrI3"
      },
      "outputs": [],
      "source": [
        "import datasets\n",
        "import random\n",
        "import pandas as pd\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "def show_random_elements(dataset, num_examples=5):\n",
        "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
        "    picks = []\n",
        "    for _ in range(num_examples):\n",
        "        pick = random.randint(0, len(dataset)-1)\n",
        "        while pick in picks:\n",
        "            pick = random.randint(0, len(dataset)-1)\n",
        "        picks.append(pick)\n",
        "    \n",
        "    df = pd.DataFrame(dataset[picks])\n",
        "    for column, typ in dataset.features.items():\n",
        "        if isinstance(typ, datasets.ClassLabel):\n",
        "            df[column] = df[column].transform(lambda i: typ.names[i])\n",
        "    display(HTML(df.to_html()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "SZy5tRB_IrI7",
        "outputId": "26692863-07ef-4c58-ed4c-97ef31f319e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sms</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Did I forget to tell you ? I want you , I need you, I crave you ... But most of all ... I love you my sweet Arabian steed ... Mmmmmm ... Yummy\\n</td>\n",
              "      <td>ham</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Just wondering, the others just took off\\n</td>\n",
              "      <td>ham</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Okay same with me. Well thanks for the clarification\\n</td>\n",
              "      <td>ham</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>My fri ah... Okie lor,goin 4 my drivin den go shoppin after tt...\\n</td>\n",
              "      <td>ham</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cool, want me to go to kappa or should I meet you outside mu\\n</td>\n",
              "      <td>ham</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "show_random_elements(dataset[\"train\"])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Select N examples per class (8 in this case)\n",
        "train_ds_pos = dataset[\"train\"].shuffle(seed=49).filter(lambda example: example[\"label\"] == 0).select(range(8))\n",
        "train_ds_neg = dataset[\"train\"].shuffle(seed=49).filter(lambda example: example[\"label\"] == 1).select(range(8))\n",
        "train_ds = concatenate_datasets([train_ds_pos, train_ds_neg])"
      ],
      "metadata": {
        "id": "4ZXcLuZkEExv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37f73447-275a-4a7e-ae2c-ce4aa9ce27d5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c/cache-c2bdf5c3e89f9896.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c/cache-f2cf700b0ca63f86.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c/cache-c2bdf5c3e89f9896.arrow\n",
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c/cache-62f4a809330573c7.arrow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = train_ds.shuffle(seed=21)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pxDlCkVa9Phb",
        "outputId": "378a4aed-d05e-4ba7-fe98-7d260f354c9a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c/cache-447a7e3803534e4c.arrow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_random_elements(train_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "17lpVa4wGcpj",
        "outputId": "ee858d6d-4a81-4a5d-9005-471f66799978"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sms</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FreeMsg: Txt: CALL to No: 86888 &amp; claim your reward of 3 hours talk time to use from your phone now! Subscribe6GBP/mnth inc 3hrs 16 stop?txtStop\\n</td>\n",
              "      <td>spam</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>EASTENDERS TV Quiz. What FLOWER does DOT compare herself to? D= VIOLET E= TULIP F= LILY txt D E or F to 84025 NOW 4 chance 2 WIN £100 Cash WKENT/150P16+\\n</td>\n",
              "      <td>spam</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Valentines Day Special! Win over £1000 in our quiz and take your partner on the trip of a lifetime! Send GO to 83600 now. 150p/msg rcvd. CustCare:08718720201\\n</td>\n",
              "      <td>spam</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Dear good morning how you feeling dear\\n</td>\n",
              "      <td>ham</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Thanx 4 sending me home...\\n</td>\n",
              "      <td>ham</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = train_ds.rename_column('sms', 'text')\n",
        "test_ds = dataset['test']\n",
        "test_ds = test_ds.rename_column('sms', 'text')"
      ],
      "metadata": {
        "id": "YZzulVvz-Ac6"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load SetFit model from Hub\n",
        "model = SetFitModel.from_pretrained(\"sentence-transformers/paraphrase-mpnet-base-v2\")\n",
        "\n",
        "# Create trainer\n",
        "trainer = SetFitTrainer(\n",
        "    model=model,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=test_ds,\n",
        "    loss_class=CosineSimilarityLoss,\n",
        "    batch_size=16,\n",
        "    num_epochs=20,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UVhsfkUWEP3H",
        "outputId": "c9b734c1-326f-4a9e-c995-813542ff9105"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "model_head.pkl not found on HuggingFace Hub, initialising classification head with random weights. You should TRAIN this model on a downstream task to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train and evaluate!\n",
        "trainer.train()"
      ],
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        "colab": {
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        "id": "GSDqm5CyETXU",
        "outputId": "24d05a04-42e1-4049-b5f6-1264fc81abfa"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running training *****\n",
            "  Num examples = 640\n",
            "  Num epochs = 20\n",
            "  Total optimization steps = 40\n",
            "  Total train batch size = 16\n"
          ]
        },
        {
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            "text/plain": [
              "Epoch:   0%|          | 0/20 [00:00<?, ?it/s]"
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    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
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        "id": "y3y2I62LEj8T",
        "outputId": "1510289c-8101-4a46-da19-62745a6bbaf8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/4.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running evaluation *****\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.9438470728793309}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! git config --global user.email \"user@email.com\""
      ],
      "metadata": {
        "id": "sOfQQAgDHZ0J"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(\"setfit-mpnet-base-v2-finetuned-spam-detection\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "0RbVxtwoEvGP",
        "outputId": "eb479961-3a1d-4040-a987-4ce0891d1db8"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_deprecation.py:42: FutureWarning: Deprecated positional argument(s) used in 'push_to_hub': pass repo_path_or_name='setfit-mpnet-base-v2-finetuned-spam-detection', repo_url=None, commit_message='Add SetFit model', organization=None, private=None, api_endpoint=None, use_auth_token=None, git_user=None, git_email=None, config=None, skip_lfs_files=False as keyword args. From version 0.12 passing these as positional arguments will result in an error,\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_deprecation.py:97: FutureWarning: Deprecated argument(s) used in 'push_to_hub': repo_path_or_name. Will not be supported from version '0.12'.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "Cloning https://huggingface.co/mrm8488/setfit-mpnet-base-v2-finetuned-spam-detection into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/mrm8488/setfit-mpnet-base-v2-finetuned-spam-detection into local empty directory.\n"
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          "data": {
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              "Upload file pytorch_model.bin:   0%|          | 3.34k/418M [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
              "Upload file model_head.pkl:  48%|####8     | 3.34k/6.95k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21130221399943ed9426539280e9a303"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "remote: Scanning LFS files for validity, may be slow...        \n",
            "remote: LFS file scan complete.        \n",
            "To https://huggingface.co/mrm8488/setfit-mpnet-base-v2-finetuned-spam-detection\n",
            "   8a485d3..80bae7a  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:remote: Scanning LFS files for validity, may be slow...        \n",
            "remote: LFS file scan complete.        \n",
            "To https://huggingface.co/mrm8488/setfit-mpnet-base-v2-finetuned-spam-detection\n",
            "   8a485d3..80bae7a  main -> main\n",
            "\n"
          ]
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/mrm8488/setfit-mpnet-base-v2-finetuned-spam-detection/commit/80bae7a965d19b1001c14d8a3be72bccf91b995e'"
            ],
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZfyVAmvMFCBE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}