HF_SetFit.ipynb

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          "text": [
            "Mon Sep 26 18:46:24 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 V100-SXM2...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0    24W / 300W |      0MiB / 16160MiB |      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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wZPcwhboB8md",
        "outputId": "29d96da0-c0c3-4afa-faaf-1fa8ecbf2590"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 4.9 MB 12.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 431 kB 58.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 120 kB 69.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 63.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 115 kB 68.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 212 kB 54.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 127 kB 59.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 69 kB 8.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 362 kB 48.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 85 kB 1.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.3 MB 60.4 MB/s \n",
            "\u001b[?25h  Building wheel for sentence-transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "! pip install -q transformers datasets setfit"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RBQPMUvnEUpf",
        "outputId": "c30f679b-5019-420c-9db6-42b4ce8345a8"
      },
      "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": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "from sentence_transformers.losses import CosineSimilarityLoss\n",
        "\n",
        "from setfit import SetFitModel, SetFitTrainer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "fd6efec7550a4bffa793aba62e68dcd7",
            "86feadc8f33447fd986c7f4f71ae8d00",
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            "3fe404274f6a4e5c8c87e875d84462e6",
            "d2adde0a996d4b2a90d4dc1063986aef",
            "32e98887e26c4227b1292e0244d7ffb7",
            "7ce5d8872ef44e6c9d7816313a0b7c64",
            "a7dee971dab8423683f9383ec39520e7"
          ]
        },
        "id": "NsM0VNeiD8Rn",
        "outputId": "416db3f5-acab-4920-eb07-5931d1c54318"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moving 0 files to the new cache system\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd6efec7550a4bffa793aba62e68dcd7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(\"SetFit/SentEval-CR\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249,
          "referenced_widgets": [
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        },
        "id": "WWnMFbbBD_Yb",
        "outputId": "b0ae38e2-5cc5-4f25-a972-89bee840d719"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration SetFit--SentEval-CR-3d6d995d44023096\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset json/SetFit--SentEval-CR to /root/.cache/huggingface/datasets/SetFit___json/SetFit--SentEval-CR-3d6d995d44023096/0.0.0/da492aad5680612e4028e7f6ddc04b1dfcec4b64db470ed7cc5f2bb265b9b6b5...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "895e104c99f94546b4c0c7c958a70894"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/427k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e4e8fd214594909b1a860293cda04ca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/109k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1468d3e1679e4089b9db6af338b222a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "89f398191bb64f9fa2d2d276d254a989"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 tables [00:00, ? tables/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "964457c8b5c346b984522169c6b198ab"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 tables [00:00, ? tables/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/SetFit___json/SetFit--SentEval-CR-3d6d995d44023096/0.0.0/da492aad5680612e4028e7f6ddc04b1dfcec4b64db470ed7cc5f2bb265b9b6b5. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  0%|          | 0/2 [00:00<?, ?it/s]"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YFitm6d0EGLG",
        "outputId": "40962bed-e286-414b-f83d-53c07f3328ee"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 3012\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label', 'label_text'],\n",
              "        num_rows: 753\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "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=10):\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": 9,
      "metadata": {
        "id": "SZy5tRB_IrI7",
        "outputId": "e787734d-f593-4476-c191-750d3326f7bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "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>text</th>\n",
              "      <th>label</th>\n",
              "      <th>label_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>video output stopped working .</td>\n",
              "      <td>0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>. images taken indoor were n 't so clear .</td>\n",
              "      <td>0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>this thing seriously is a good player though .</td>\n",
              "      <td>1</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>if anyone is thinking of getting an ipod mini , forget it and go with this player .</td>\n",
              "      <td>1</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>have used it frequently and have had no problems .</td>\n",
              "      <td>1</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>the daylight pictures are brilliant and the indoor shots are very good provided that your subject is no more than about 8 feet away .</td>\n",
              "      <td>1</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>however , the battery life is good enough you should n 't be overly concerned with turning this phone off .</td>\n",
              "      <td>1</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>maybe this new design is more durable than the last , but i kind of doubt it .</td>\n",
              "      <td>0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>size - extremely small and convenient will fit in your pocket and not bother you .</td>\n",
              "      <td>1</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>you also need to go in and manually remove many symantec files from the registry .</td>\n",
              "      <td>0</td>\n",
              "      <td>negative</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 = dataset[\"train\"].shuffle(seed=42).select(range(8 * 2))\n",
        "test_ds = dataset[\"test\"]"
      ],
      "metadata": {
        "id": "4ZXcLuZkEExv"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "show_random_elements(train_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "17lpVa4wGcpj",
        "outputId": "2f7d00b6-a238-491f-cd22-c2767a2538be"
      },
      "execution_count": 11,
      "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>text</th>\n",
              "      <th>label</th>\n",
              "      <th>label_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>it fits into a hand well , it has a removable battery ( this is important ) , great sound quality , fm stereo , recorder , smooth ui , and a feature that most uni pods lack . . . char ! .</td>\n",
              "      <td>1</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>for a price that 's still less than even the lowest level ipod i was able to get this 40gb monster , and the best part is it works as great as it was advertised to and then some .</td>\n",
              "      <td>1</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>i bought the player this week and i like it by far .</td>\n",
              "      <td>1</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>neither message was answered ( they ask for 24 hours before replying - i 've been waiting 27 days . )</td>\n",
              "      <td>0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>the thought of not having to buy refills and just using regular bags is awesome ! .</td>\n",
              "      <td>1</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>as for bluetooth , no problems at all .</td>\n",
              "      <td>1</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>the pc-side software can be goofy and takes a little getting used to but it 's not as bad as some reviewers have indicated .</td>\n",
              "      <td>0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>symantec should give us all a refund and apology ! .</td>\n",
              "      <td>0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>the day finally arrived when i was sure i 'd leave sprint .</td>\n",
              "      <td>0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>only problem is that is a bit heavy .</td>\n",
              "      <td>0</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "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",
        ")"
      ],
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        "outputId": "1d890b54-4a70-4ce0-950c-97dfa6e350fb"
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      "execution_count": 12,
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            "model_head.pkl not found on HuggingFace Hub, initialising classification head with random weights.\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "# Train and evaluate!\n",
        "trainer.train()"
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            "3073eac97a384aaabe561315e76ee3ba",
            "d752ee3542834d27af3b05d9208bffa4",
            "fab77d2a073c4e45b929edbdae65b228",
            "bc64e465862e469a97a2bb14863e8462",
            "66830c14dd66497cb55c7f56feca390d",
            "532bc6b3b1c04185be6b3c11387e6fa7"
          ]
        },
        "id": "y3y2I62LEj8T",
        "outputId": "9383cd19-cf19-4494-cdcc-cea18645a827"
      },
      "execution_count": 14,
      "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,
              "version_minor": 0,
              "model_id": "5c172cc36bb54b47b4eb91db9a59010a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': 0.8193891102257637}"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! git config --global user.email \"your@email.com\""
      ],
      "metadata": {
        "id": "sOfQQAgDHZ0J"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub(\"setfit-mpnet-base-v2-finetuned-sentEval-CR\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205,
          "referenced_widgets": [
            "b9eb6a2e56fb438989af1c0450dc0ff4",
            "7ac877aa12ad4f63a693621ff6f3caff",
            "a1d4ed0a700d4f798901d426ee6e924d",
            "bed8e876596043fc8ea3cb1ab1586abf",
            "2243fe410e2340a9a928a22d41e4b776",
            "9f24ce6794ee42a186c747ab62675733",
            "b027ecda8d9645e2aecb091f5e431a45",
            "bcd8176a3edb4807862c1c6e1e7ac592",
            "04b52f43506f419693b02962027e4dfb",
            "e9b52c0fc3b64b5eae8187c3569a6956",
            "8c338ff9e13849678fc3611e857caa81",
            "2161c1b8ca4d43bc844b3af8370874cb",
            "f8fd72f1e33740b58f314dc53427dee5",
            "6e8185a9faa94111834f0ee7f196af76",
            "28148b4c78b54231a617a7d8d7d2ef3d",
            "6b1e69d14e914dca8ad5109320791ebb",
            "0a6ecdae84ae4f749a9fae476b96925b",
            "c95877ac1cf649a38ded47c340858c7d",
            "fec9f5002268404d8a84b603ae2f8b24",
            "c14cbfd94eca4504a13d2a9c4fc3705c",
            "05aaabf1b0bf4bfa8918724c0e918438",
            "05c3e7507697442499454e330e98c426"
          ]
        },
        "id": "0RbVxtwoEvGP",
        "outputId": "cd000308-5258-4814-c609-f6dbc5d8b062"
      },
      "execution_count": null,
      "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-sentEval-CR', repo_url=None, commit_message='Add 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:92: FutureWarning: Deprecated argument(s) used in 'push_to_hub': repo_path_or_name, repo_url, organization, use_auth_token, git_user, git_email, skip_lfs_files. Will not be supported from version '0.12'.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "Cloning https://huggingface.co/mrm8488/setfit-mpnet-base-v2-finetuned-sentEval-CR into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/mrm8488/setfit-mpnet-base-v2-finetuned-sentEval-CR into local empty directory.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 3.34k/418M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b9eb6a2e56fb438989af1c0450dc0ff4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file model_head.pkl:  49%|####8     | 3.34k/6.88k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2161c1b8ca4d43bc844b3af8370874cb"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZfyVAmvMFCBE"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}