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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/TurkuNLP/Text_Mining_Course/blob/master/train_bert_for_text_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3LPd4TaxwbMR"
      },
      "source": [
        "# Train BERT text classifier using the Transformers library\n",
        "\n",
        "This notebook shows how to train a simple text classifier by fine-tuning a pre-trained BERT model using the Hugging Face [Transformers](https://huggingface.co/transformers/) library.\n",
        "\n",
        "This notebook is based in part on the [Text classification on GLUE](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/text_classification.ipynb) notebook.\n",
        "\n",
        "**NOTE**: it's recommended to run this using a runtime with a GPU. Select \"Runtime\" -> \"Change runtime type\" from the top menu in Colab and set \"Hardware accelerator\" to \"GPU\" when starting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u3uu9vMewmM6"
      },
      "source": [
        "## Install libraries\n",
        "\n",
        "First, we'll use [`pip`](https://pypi.org/project/pip/) to install two Python libraries that are used in this notebook: [`transformers`](https://huggingface.co/transformers/) and [`datasets`](https://huggingface.co/docs/datasets/)."
      ]
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      "source": [
        "!pip --quiet install transformers\n",
        "!pip --quiet install datasets"
      ],
      "execution_count": 1,
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            "\u001b[K     |████████████████████████████████| 890kB 49.4MB/s \n",
            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ETfE2d5Ryisz"
      },
      "source": [
        "## Import libraries\n",
        "\n",
        "Next, let's import the classes and functions we'll be using (click links for documentation):\n",
        "\n",
        "* [AutoTokenizer](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoTokenizer): tokenizer class with support for the tokenization conventions of various pre-trained models\n",
        "* [AutoModelForSequenceClassification](https://huggingface.co/transformers/model_doc/auto.html#automodelforsequenceclassification): an [AutoModel](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoModel) class that can load various pre-trained models and supports fine-tuning for text (sequence) classification\n",
        "* [TrainingArguments](https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments): simple class for holding hyperparameters and other settings for model training\n",
        "* [Trainer](https://huggingface.co/transformers/main_classes/trainer.html): class supporting various forms of training transformer models\n",
        "* [load_dataset](https://huggingface.co/docs/datasets/package_reference/loading_methods.html#datasets.load_dataset): function for loading datasets e.g. from the [datasets](https://huggingface.co/datasets) collection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q21P5qTsyyzO"
      },
      "source": [
        "from transformers import AutoTokenizer\n",
        "from transformers import AutoModelForSequenceClassification\n",
        "from transformers import TrainingArguments\n",
        "from transformers import Trainer\n",
        "from datasets import load_dataset"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOM98a-B1gT2"
      },
      "source": [
        "## Set model, dataset and hyperparameters\n",
        "\n",
        "Let's then set some global variables such as the name of the pre-trained model and the hyperparameters we'll use for fine-tuning it.\n",
        "\n",
        "* `MODEL_NAME`: the name of a pretrained model included in the [model repository](https://huggingface.co/models)\n",
        "* `DATASET`: the path and name of a dataset included in the [dataset repository](https://huggingface.co/datasets)\n",
        "* `LEARNING_RATE`, `BATCH_SIZE`, and `TRAIN_EPOCHS`: hyperparameters to use for fine-turning the model. (Try different values here!)\n",
        "\n",
        "Here, we'll use the [Stanford Sentiment Treebank](https://huggingface.co/datasets/sst) dataset as prepared for the [GLUE](https://huggingface.co/datasets/glue) collection. This is a binary text classification task where the objective is to determine if sentences express a positive or negative sentiment."
      ]
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      "cell_type": "code",
      "metadata": {
        "id": "cH-oojr_1sOq"
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      "source": [
        "MODEL_NAME = 'bert-base-cased'\n",
        "DATASET = ('glue', 'sst2')\n",
        "\n",
        "# Hyperparameters\n",
        "LEARNING_RATE=1e-5\n",
        "BATCH_SIZE=64\n",
        "TRAIN_EPOCHS=1"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DB9k8bU_6ZFK"
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      "source": [
        "## Load dataset\n",
        "\n",
        "We'll first load the dataset with [`load_dataset`](https://huggingface.co/docs/datasets/package_reference/loading_methods.html#datasets.load_dataset) and determine the number of distinct labels in the (training) data."
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset glue downloaded and prepared to /root/.cache/huggingface/datasets/glue/sst2/1.0.0/7c99657241149a24692c402a5c3f34d4c9f1df5ac2e4c3759fadea38f6cb29c4. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nu17UF9D7EFB"
      },
      "source": [
        "The loaded dataset is a simple dictionary-like container for distinct `DataSet` objects for training, development (validation), and test data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVEvezq37PqH",
        "outputId": "71655ac9-e6d7-4552-97f2-f6f5e6631b23"
      },
      "source": [
        "print(dataset)\n",
        "print(f'number of distinct labels: {num_labels}')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['sentence', 'label', 'idx'],\n",
            "        num_rows: 67349\n",
            "    })\n",
            "    validation: Dataset({\n",
            "        features: ['sentence', 'label', 'idx'],\n",
            "        num_rows: 872\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['sentence', 'label', 'idx'],\n",
            "        num_rows: 1821\n",
            "    })\n",
            "})\n",
            "number of distinct labels: 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6yVSGu_C8o8D"
      },
      "source": [
        "To reduce training time a bit, let's just take every 10th item in the `train` subset of the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "937d039519224412ab11b59d1d01a451",
            "6d2c931b206349eeb2d4897bfc0613b9",
            "e8cf848b019b4b238b48b84e8ee35b8c",
            "83e2e4bee3aa4ef4993680e2881eac01",
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            "ab74d6b832fb4bb886c59a7356f9356f",
            "a1ae8cbba94d4fc19bcffbf72e87ab89"
          ]
        },
        "id": "K-zoQFxy8xq6",
        "outputId": "2b3ce0a0-7679-4683-9f57-7931fa92dad0"
      },
      "source": [
        "dataset['train'] = dataset['train'].filter(lambda example, idx: idx % 10 == 0, with_indices=True)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "937d039519224412ab11b59d1d01a451",
              "version_minor": 0,
              "version_major": 2
            },
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              "HBox(children=(FloatProgress(value=0.0, max=68.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yf_R3aYC38bM"
      },
      "source": [
        "## Load tokenizer and tokenize data\n",
        "\n",
        "We'll then load an appropriate tokenizer for the pre-trained model we'll be using with [`AutoTokenizer.from_pretrained`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoTokenizer.from_pretrained)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216,
          "referenced_widgets": [
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            "dc82e85a86264618a7c190cbd468453b"
          ]
        },
        "id": "_zsBiAyj4q4i",
        "outputId": "e604685a-d279-4b82-9fc3-8a67bfe6c47b"
      },
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d17a7e567ee54da9a23e6351f0195686",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4554a97cd01e4ee5af502ef3c6166838",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=213450.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c4c5e3299bc2452c97bbc2d1f0e00e9a",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=435797.0, style=ProgressStyle(descripti…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6c33fbaae8c642cc9b1d087a5a065f3f",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=29.0, style=ProgressStyle(description_w…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mvzXyW504yaR"
      },
      "source": [
        "The tokenizer can take a string (or e.g. a list of strings) and performs all the necessary preprocessing steps to prepare its input for use by the model, including splitting input texts into tokens, adding padding and special characters, and mapping those to integer IDs (`input_ids`). The tokenizer also creates attention weights (`attention_mask`) for the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WTj0eaev5MN-",
        "outputId": "6b61e35e-3692-41b7-a7fb-7c031f6b13d3"
      },
      "source": [
        "tokenizer('Hello, BERT!')    # Demonstrate tokenizer output"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': [101, 8667, 117, 139, 9637, 1942, 106, 102], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufKlKoqsBpuq"
      },
      "source": [
        "We'll then define an encoding function applying the tokenizer to the text data  (key `\"sentence\"`) of a [`Dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=dataset#datasets.Dataset) object, and use the `map` function of the `DatasetDict` to tokenize the train, development, and test datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166,
          "referenced_widgets": [
            "10d1f05fd0144bd9a0277fa0420870f8",
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            "5df550be77954daaafd36fdefe88d1a1",
            "95c7d07bfae443b889803e161c4259a5",
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        },
        "id": "m-9TlSVDBx0E",
        "outputId": "da75ba97-d7c0-42d6-c9d4-5340c449b14c"
      },
      "source": [
        "def encode_dataset(d):\n",
        "  return tokenizer(d['sentence'])\n",
        "\n",
        "encoded_dataset = dataset.map(encode_dataset)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "10d1f05fd0144bd9a0277fa0420870f8",
              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, max=6735.0), HTML(value='')))"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a9f75f7be686419591fa9cdb77c84fc2",
              "version_minor": 0,
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              "HBox(children=(FloatProgress(value=0.0, max=872.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "42cee91730d34d3bbf03e88b9d0d5853",
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              "HBox(children=(FloatProgress(value=0.0, max=1821.0), HTML(value='')))"
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aN2CBQnp5pEF"
      },
      "source": [
        "## Load pre-trained model\n",
        "\n",
        "Next, we'll load the pre-trained model with support for text classification output using [`AutoModelForSequenceClassification.from_prertained`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoModelForSequenceClassification.from_pretrained).\n",
        "\n",
        "Note that we need to provide the number of labels in the data when loading the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175,
          "referenced_widgets": [
            "8d1cc343f9e74c13ab29ec932aa2dc7b",
            "c80ff7cf2f274b61a417310d6e8ae3ee",
            "08f35afed5e64652babdf4049824c69f",
            "4b65436cfffd4677bfee4a118fccb5f2",
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        },
        "id": "6Pq-BuIm86YD",
        "outputId": "54b97604-dfd6-4241-d69f-6dd53a198d30"
      },
      "source": [
        "model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME, num_labels=num_labels)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8d1cc343f9e74c13ab29ec932aa2dc7b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=435779157.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.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 bert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qCcf-EZF9x0j"
      },
      "source": [
        "## Training parameters and metrics\n",
        "We're almost ready to train. We'll next create a [`TrainingArguments`](https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments) object to hold the hyperparameters and other settings that are appropriate for training on Colab:\n",
        "\n",
        "* `save_strategy`: set to `\"no\"` so that model checkpoints are not saved to avoids exhausting Colab storage space\n",
        "* `evaluation_strategy` and `logging_strategy` set to `\"epoch\"` so that evaluation and logging are performed once per epoch\n",
        "* The hyperparameters `LEARNING_RATE`, `BATCH_SIZE` and `TRAIN_EPOCHS` set above are passed to the training process through this object"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9p0qlQrl-NSp"
      },
      "source": [
        "train_args = TrainingArguments(\n",
        "    'output_dir',    # output directory for checkpoints and predictions\n",
        "    save_strategy='no',\n",
        "    evaluation_strategy='epoch',\n",
        "    logging_strategy='epoch',\n",
        "    learning_rate=LEARNING_RATE,\n",
        "    per_device_train_batch_size=BATCH_SIZE,\n",
        "    num_train_epochs=TRAIN_EPOCHS,\n",
        ")"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vV_hOr7y_xaM"
      },
      "source": [
        "Finally, we'll define a simple accuracy metric measuring how many predictions match their correct values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Wd3_VaQ_94D"
      },
      "source": [
        "def compute_accuracy(pred):\n",
        "    y_pred = pred.predictions.argmax(axis=1)\n",
        "    y_true = pred.label_ids\n",
        "    return { 'accuracy': sum(y_pred == y_true) / len(y_true) }"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nyRihTPA_SOU"
      },
      "source": [
        "# Training (fine-tuning)\n",
        "\n",
        "For fine-tuning the pre-trained model, we'll create a [`Trainer`](https://huggingface.co/transformers/main_classes/trainer.html) object, providing it with the pre-trained model, settings, training and development (validation) data, and the evaluation metric created above."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uwIGX6Y_fP8"
      },
      "source": [
        "trainer = Trainer(\n",
        "      model,\n",
        "      train_args,\n",
        "      train_dataset=encoded_dataset['train'],\n",
        "      eval_dataset=encoded_dataset['validation'],\n",
        "      tokenizer=tokenizer,\n",
        "      compute_metrics=compute_accuracy\n",
        ")"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VsEpkLgOAQ9c"
      },
      "source": [
        "Training is then performed simply by calling the `train` function of the `Trainer` object."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "Zk0pspNEAeWr",
        "outputId": "ac97c0e8-6c9d-47fc-9d7b-93b4a08d6dd1"
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "        </style>\n",
              "      \n",
              "      <progress value='106' max='106' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [106/106 00:34, Epoch 1/1]\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>Accuracy</th>\n",
              "      <th>Runtime</th>\n",
              "      <th>Samples Per Second</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.531700</td>\n",
              "      <td>0.348523</td>\n",
              "      <td>0.866972</td>\n",
              "      <td>1.562700</td>\n",
              "      <td>558.025000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=106, training_loss=0.5316962835923681, metrics={'train_runtime': 35.1877, 'train_samples_per_second': 3.012, 'total_flos': 209359146903300.0, 'epoch': 1.0, 'init_mem_cpu_alloc_delta': 351248, 'init_mem_gpu_alloc_delta': 433776128, 'init_mem_cpu_peaked_delta': 18306, 'init_mem_gpu_peaked_delta': 0, 'train_mem_cpu_alloc_delta': 528753, 'train_mem_gpu_alloc_delta': 1330412544, 'train_mem_cpu_peaked_delta': 430964, 'train_mem_gpu_peaked_delta': 3357101056})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ERPvLNK9EcN8"
      },
      "source": [
        "## Evaluation and predictions for user input\n",
        "\n",
        "We can use `trainer.evaluate()` to evaluate the trained model on the `eval_dataset` given to the trainer:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "id": "-f-XXjvkE0aL",
        "outputId": "ef06e632-6698-4413-89db-a29ee6196a47"
      },
      "source": [
        "results = trainer.evaluate()\n",
        "print(f'Accuracy: {results[\"eval_accuracy\"]}')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "        <style>\n",
              "            /* Turns off some styling */\n",
              "            progress {\n",
              "                /* gets rid of default border in Firefox and Opera. */\n",
              "                border: none;\n",
              "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "                background-size: auto;\n",
              "            }\n",
              "        </style>\n",
              "      \n",
              "      <progress value='109' max='109' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [109/109 00:01]\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.8669724770642202\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AGAlzsCKf7vc"
      },
      "source": [
        "The trained model is available as `trainer.model`, and we can use it like any other model. Here we define a function to predict the sentiment a user-given string."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ocPeVRnPWF_m"
      },
      "source": [
        "model = trainer.model\n",
        "model.to('cpu')    # simplifies input placement\n",
        "\n",
        "sentiment_label = [\n",
        "    'negative',\n",
        "    'positive'\n",
        "]\n",
        "\n",
        "def predict_sentiment(string):\n",
        "    tokenized = tokenizer(string, return_tensors='pt')\n",
        "    pred = model(**tokenized)\n",
        "    pred_idx = pred.logits.detach().numpy().argmax()\n",
        "    return sentiment_label[pred_idx]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-up4Ua7iMMO"
      },
      "source": [
        "try that out"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_QcEfttZgn9",
        "outputId": "5dd2269f-abb3-47b4-f349-6483f6de4d06"
      },
      "source": [
        "example_sentences = [\n",
        "    'This is great!',\n",
        "    'This is OK, I guess.',\n",
        "    'This is not great.',\n",
        "    'This is terrible!',\n",
        "]\n",
        "\n",
        "for e in example_sentences:\n",
        "    print(e, '->', predict_sentiment(e))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "This is great! -> positive\n",
            "This is OK, I guess. -> positive\n",
            "This is not great. -> negative\n",
            "This is terrible! -> negative\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I2oGRF0ssbYh"
      },
      "source": [
        "Can you change the model, the hyperparameters, or some other aspect of the fine-tuning process to make the result better?"
      ]
    }
  ]
}