{
  "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/train_bert_tiny_spam_classifier_HF.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yHXJETBZgJmY",
        "outputId": "79929c52-a288-4442-e0a0-799b42ae930a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU 0: Tesla T4 (UUID: GPU-2a959423-ab3b-5c71-772b-edb727a4773d)\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi -L"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "PwUgANiCgUSB"
      },
      "outputs": [],
      "source": [
        "!pip install -q transformers datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ttpU22VD3xnQ",
        "outputId": "70010cf0-d2f8-4b83-df08-0924486f7b68"
      },
      "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"
          ]
        }
      ],
      "source": [
        "! huggingface-cli login"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "4831e3e05b3c467d9f57cf9ada656753",
            "f4446ca43043482999c02a62831723b3",
            "0c76482bd2c4411cadd21bb399026b63",
            "26b5d3004ff6478e8145adf00b644237",
            "588422cf049b49019958992139fd9994",
            "395469cbc4194ec4984394304c30e952",
            "10bde806c8214657960e8fec065a7dea",
            "a76d8381d70643739fdba87bb9c59168",
            "2a3f297602c342bf846d44f206fecec8",
            "68731c6eab6f4470b897a34f65fb833c",
            "57106be185a64730a92c6655384236bf"
          ]
        },
        "id": "xZpgZaQ-iIsT",
        "outputId": "9d9b773d-31e2-44c7-8a73-dea2ab241f22"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration SetFit--enron_spam-a9b66f8201be682e\n",
            "WARNING:datasets.builder:Found cached dataset json (/root/.cache/huggingface/datasets/SetFit___json/SetFit--enron_spam-a9b66f8201be682e/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4831e3e05b3c467d9f57cf9ada656753"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset, load_metric\n",
        "\n",
        "dataset =  load_dataset('SetFit/enron_spam')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_jse23A8gDd",
        "outputId": "630a990f-1e75-436b-ed33-8be7389f8ee9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['message_id', 'text', 'label', 'label_text', 'subject', 'message', 'date'],\n",
              "        num_rows: 31716\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['message_id', 'text', 'label', 'label_text', 'subject', 'message', 'date'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Kvg_WrKfwTuw"
      },
      "outputs": [],
      "source": [
        "from collections import Counter\n",
        "counts = Counter(dataset['train']['label'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "xA7QBmIRwpbZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4af4ee3-ba1e-40bf-eb53-b1161af74cb9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Counter({1: 16163, 0: 15553})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "counts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ZG6E4Do3kWhr"
      },
      "outputs": [],
      "source": [
        "model_checkpoint = 'google/bert_uncased_L-2_H-128_A-2' # aka bert-tiny"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "QKT0898BkSd6"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer\n",
        "    \n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "EHeKkvgllegq"
      },
      "outputs": [],
      "source": [
        "def preprocess_function(examples):\n",
        "  return tokenizer(examples['text'], truncation=True, max_length=512)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86,
          "referenced_widgets": [
            "a911de782b9e477182ba29d0c879d3fc",
            "2e559672af9940e8a074581062f8cc39",
            "a88936e085dd47ebb9bd4e3d8fc5455c",
            "8e39fd7c08f74aa094333485030ad1e2",
            "cc37baf4afe44f7a884da5e8add468fc",
            "bbbca046f5004f3fa042b49985be2dc3",
            "c0de3af013884ed8ab332f3b275fe97f",
            "d3cf367a12e14adb9a62fe9cde83f8b6",
            "5a7eee38d5f0424fb00a4ec3fa425f07",
            "f883961120f8406fa5b3567731427953",
            "ba7b16642ee3472595dbbc1434d6e4ac"
          ]
        },
        "id": "RwNWVi5DlFV5",
        "outputId": "b00bfe7c-f0ac-46fb-95cc-b6ea483bcf20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/SetFit___json/SetFit--enron_spam-a9b66f8201be682e/0.0.0/e6070c77f18f01a5ad4551a8b7edfba20b8438b7cad4d94e6ad9378022ce4aab/cache-b775d3ec7c78eb0d.arrow\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a911de782b9e477182ba29d0c879d3fc"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset = dataset.map(preprocess_function, batched=True, remove_columns=['message_id', 'text', 'label_text', 'subject', 'message', 'date'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wT00jBaKTOP3",
        "outputId": "52e328ce-ab7f-40c6-fdf0-fa243ac075e2"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 31716\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 2000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vAUAaoXLmLe-",
        "outputId": "cc10c2d4-195c-4ea1-b4a2-4aea8945d698"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at google/bert_uncased_L-2_H-128_A-2 were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at google/bert_uncased_L-2_H-128_A-2 and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
        "\n",
        "\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "aXbJ5PC9meoS"
      },
      "outputs": [],
      "source": [
        "metric_name = \"f1\"\n",
        "\n",
        "args = TrainingArguments(\n",
        "    \"bert-tiny-finetuned-enron-spam-detection\",\n",
        "    evaluation_strategy = \"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=32,\n",
        "    num_train_epochs=4,\n",
        "    weight_decay=0.01,\n",
        "    load_best_model_at_end=True,\n",
        "    save_strategy=\"epoch\",\n",
        "    metric_for_best_model=metric_name,\n",
        "    push_to_hub=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "0Ha3Bfx16TFT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9346fd8-6268-44b9-eba3-fa01192e461d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
      ],
      "source": [
        "metric = load_metric('f1')\n",
        "metric2 = load_metric(\"precision\")\n",
        "metric3 = load_metric(\"recall\")\n",
        "metric4 = load_metric(\"accuracy\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "F7-kXysqmy7x"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "  predictions, labels = eval_pred\n",
        "  predictions = np.argmax(predictions, axis=1)\n",
        "  f1 = metric.compute(predictions=predictions, references=labels)[\"f1\"]\n",
        "  precision = metric2.compute(predictions=predictions, references=labels)[\"precision\"]\n",
        "  recall = metric3.compute(predictions=predictions, references=labels)[\"recall\"]\n",
        "  acc = metric4.compute(predictions=predictions, references=labels)[\"accuracy\"]\n",
        "  return {\"precision\": precision, \"recall\": recall, \"accuracy\": acc, \"f1\": f1}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "bXZXhvENnAPt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ce9da94-dde8-48e5-9f72-76b50d2148a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/mrm8488/bert-tiny-finetuned-enron-spam-detection into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/mrm8488/bert-tiny-finetuned-enron-spam-detection into local empty directory.\n"
          ]
        }
      ],
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    args,\n",
        "    train_dataset=dataset['train'],\n",
        "    eval_dataset=dataset['test'],\n",
        "    tokenizer=tokenizer,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821,
          "resources": {
            "https://localhost:6006/?tensorboardColab=true": {
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            },
            "https://localhost:6006/data/plugins_listing": {
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                [
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                  "application/json"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/data/runs": {
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                [
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              "ok": true,
              "status": 200,
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            },
            "https://localhost:6006/font-roboto/RxZJdnzeo3R5zSexge8UUZBw1xU1rKptJj_0jans920.woff2": {
              "data": "[encoded data, 14384 characters]",
              "headers": [
                [
                  "content-type",
                  "font/woff2"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/font-roboto/d-6IYplOFocCacKzxwXSOJBw1xU1rKptJj_0jans920.woff2": {
              "data": "[encoded data, 14352 characters]",
              "headers": [
                [
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                  "font/woff2"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2": {
              "data": "[encoded data, 14332 characters]",
              "headers": [
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              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/icon_bundle.svg": {
              "data": "[encoded data, 18556 characters]",
              "headers": [
                [
                  "content-type",
                  "image/svg+xml; charset=utf-8"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/index.js?_file_hash=b2ecab87": {
              "data": "[encoded data, 10278276 characters]",
              "ok": true,
              "headers": [
                [
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                  "application/javascript; charset=utf-8"
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              ],
              "status": 200,
              "status_text": ""
            },
            "https://localhost:6006/data/plugin/hparams/experiment?request=%7B%22experimentName%22%3A%22%22%7D": {
              "data": "[encoded data, 17928 characters]",
              "ok": true,
              "headers": [
                [
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              ],
              "status": 200,
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        "outputId": "084359a6-a605-48dd-906b-a654ccb84843"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        (async () => {\n",
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              "            iframe.setAttribute('width', '100%');\n",
              "            iframe.setAttribute('height', '800');\n",
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              "            document.body.appendChild(iframe);\n",
              "        })();\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Start tensorboard.\n",
        "%load_ext tensorboard\n",
        "%tensorboard --logdir /content/bert-tiny-finetuned-enron-spam-detection/runs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oG4uhzDsnQix",
        "outputId": "b6b67aae-ad15-49a9-9728-fc2dfadd06c8"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 31716\n",
            "  Num Epochs = 4\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 7932\n",
            "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
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              "      [7932/7932 04:43, Epoch 4/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.112500</td>\n",
              "      <td>0.079702</td>\n",
              "      <td>0.983887</td>\n",
              "      <td>0.969246</td>\n",
              "      <td>0.976500</td>\n",
              "      <td>0.976512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.061000</td>\n",
              "      <td>0.061752</td>\n",
              "      <td>0.982213</td>\n",
              "      <td>0.986111</td>\n",
              "      <td>0.984000</td>\n",
              "      <td>0.984158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.048600</td>\n",
              "      <td>0.059291</td>\n",
              "      <td>0.985149</td>\n",
              "      <td>0.987103</td>\n",
              "      <td>0.986000</td>\n",
              "      <td>0.986125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.048000</td>\n",
              "      <td>0.058838</td>\n",
              "      <td>0.987039</td>\n",
              "      <td>0.982143</td>\n",
              "      <td>0.984500</td>\n",
              "      <td>0.984585</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 2000\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to bert-tiny-finetuned-enron-spam-detection/checkpoint-1983\n",
            "Configuration saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-1983/config.json\n",
            "Model weights saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-1983/pytorch_model.bin\n",
            "tokenizer config file saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-1983/tokenizer_config.json\n",
            "Special tokens file saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-1983/special_tokens_map.json\n",
            "tokenizer config file saved in bert-tiny-finetuned-enron-spam-detection/tokenizer_config.json\n",
            "Special tokens file saved in bert-tiny-finetuned-enron-spam-detection/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2000\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to bert-tiny-finetuned-enron-spam-detection/checkpoint-3966\n",
            "Configuration saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-3966/config.json\n",
            "Model weights saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-3966/pytorch_model.bin\n",
            "tokenizer config file saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-3966/tokenizer_config.json\n",
            "Special tokens file saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-3966/special_tokens_map.json\n",
            "tokenizer config file saved in bert-tiny-finetuned-enron-spam-detection/tokenizer_config.json\n",
            "Special tokens file saved in bert-tiny-finetuned-enron-spam-detection/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2000\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to bert-tiny-finetuned-enron-spam-detection/checkpoint-5949\n",
            "Configuration saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-5949/config.json\n",
            "Model weights saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-5949/pytorch_model.bin\n",
            "tokenizer config file saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-5949/tokenizer_config.json\n",
            "Special tokens file saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-5949/special_tokens_map.json\n",
            "tokenizer config file saved in bert-tiny-finetuned-enron-spam-detection/tokenizer_config.json\n",
            "Special tokens file saved in bert-tiny-finetuned-enron-spam-detection/special_tokens_map.json\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 2000\n",
            "  Batch size = 32\n",
            "Saving model checkpoint to bert-tiny-finetuned-enron-spam-detection/checkpoint-7932\n",
            "Configuration saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-7932/config.json\n",
            "Model weights saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-7932/pytorch_model.bin\n",
            "tokenizer config file saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-7932/tokenizer_config.json\n",
            "Special tokens file saved in bert-tiny-finetuned-enron-spam-detection/checkpoint-7932/special_tokens_map.json\n",
            "tokenizer config file saved in bert-tiny-finetuned-enron-spam-detection/tokenizer_config.json\n",
            "Special tokens file saved in bert-tiny-finetuned-enron-spam-detection/special_tokens_map.json\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from bert-tiny-finetuned-enron-spam-detection/checkpoint-5949 (score: 0.9861248761149654).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=7932, training_loss=0.09356041647121874, metrics={'train_runtime': 284.1987, 'train_samples_per_second': 446.392, 'train_steps_per_second': 27.91, 'total_flos': 160072811103840.0, 'train_loss': 0.09356041647121874, 'epoch': 4.0})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
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      "source": [
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 2000\n",
            "  Batch size = 32\n"
          ]
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              "      [63/63 00:01]\n",
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              "    "
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          "data": {
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              "{'eval_loss': 0.059290628880262375,\n",
              " 'eval_precision': 0.9851485148514851,\n",
              " 'eval_recall': 0.9871031746031746,\n",
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            "Dropping the following result as it does not have all the necessary fields:\n",
            "{'task': {'name': 'Text Classification', 'type': 'text-classification'}, 'metrics': [{'name': 'Precision', 'type': 'precision', 'value': 0.9851485148514851}, {'name': 'Recall', 'type': 'recall', 'value': 0.9871031746031746}, {'name': 'Accuracy', 'type': 'accuracy', 'value': 0.986}, {'name': 'F1', 'type': 'f1', 'value': 0.9861248761149654}]}\n"
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            "Saving model checkpoint to bert-tiny-finetuned-enron-spam-detection\n",
            "Configuration saved in bert-tiny-finetuned-enron-spam-detection/config.json\n",
            "Model weights saved in bert-tiny-finetuned-enron-spam-detection/pytorch_model.bin\n",
            "tokenizer config file saved in bert-tiny-finetuned-enron-spam-detection/tokenizer_config.json\n",
            "Special tokens file saved in bert-tiny-finetuned-enron-spam-detection/special_tokens_map.json\n",
            "Several commits (2) will be pushed upstream.\n",
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            "The progress bars may be unreliable.\n",
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            "remote: LFS file scan complete.        \n",
            "To https://huggingface.co/mrm8488/bert-tiny-finetuned-enron-spam-detection\n",
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            "WARNING:huggingface_hub.repository:remote: Scanning LFS files for validity, may be slow...        \n",
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