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"grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "60cd5ff423714a329215ba006cb30588": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# Installing Packages" ], "metadata": { "id": "xWRhMX9RvBHW" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "9-sqwxyBus97", "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "outputId": "14d8a29f-8f4b-46b3-8b20-1d50f0d410b8" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting accelerate\n", " Downloading accelerate-0.32.1-py3-none-any.whl (314 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m314.1/314.1 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: numpy<2.0.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.25.2)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (24.1)\n", "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n", "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n", "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.3.0+cu121)\n", "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.23.4)\n", "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.3)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.15.4)\n", "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.12.2)\n", "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.13.0)\n", "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.3)\n", "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.4)\n", "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n", "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n", "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n", "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n", "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n", "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n", "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n", "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n", "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n", "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n", "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n", "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n", " Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n", "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.3.0)\n", "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate)\n", " Downloading nvidia_nvjitlink_cu12-12.5.82-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m73.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n", "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.4)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.7)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.7.4)\n", "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n", "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, accelerate\n", "Successfully installed accelerate-0.32.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105\n", "Collecting optuna\n", " Downloading optuna-3.6.1-py3-none-any.whl (380 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m380.1/380.1 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hCollecting alembic>=1.5.0 (from optuna)\n", " Downloading alembic-1.13.2-py3-none-any.whl (232 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m 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StratifiedKFold, GridSearchCV\n", "from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments, DataCollatorWithPadding, EarlyStoppingCallback\n", "import torch\n", "from torch.utils.data import Dataset, DataLoader\n", "import torch.nn.functional as F\n", "from sklearn.metrics import precision_score, recall_score, accuracy_score, f1_score, roc_auc_score, confusion_matrix\n", "from sklearn.utils.class_weight import compute_class_weight\n", "import optuna\n", "import numpy as np\n", "import random\n", "import accelerate\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from google.colab import drive\n", "from transformers import DataCollatorWithPadding\n", "\n" ], "metadata": { "id": "wtM1R-ULu7eZ" }, "execution_count": 2, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Importing and Cleaning Data" ], "metadata": { "id": "Fea1ipcsvFzJ" } }, { "cell_type": "code", "source": [ "# Read the data\n", "drive.mount('/content/drive')\n", "\n", "bias = pd.read_csv('/content/drive/MyDrive/hackathon/misdirection.csv')\n", "\n", "# Selecting out badly formatted columns\n", "clean_bias = bias.loc[:, 'conversation_id':'unique_id']\n", "\n", "# Filtering to just accepted vs. rejected\n", "clean_bias = clean_bias[clean_bias['submission_grade'].isin(['accepted', 'rejected'])]\n", "\n", "# Removing all NA under user (these do not help)\n", "clean_bias = clean_bias.dropna(subset=['user'])\n", "\n", "# Grouping by unique_id and joining each prompt into a single paragraph\n", "grouped = clean_bias.groupby('unique_id')['user'].apply(lambda x: ' '.join(x)).reset_index()\n", "\n", "# Selecting the predictor variable to be these paragraphs\n", "X = grouped[\"user\"].astype(str).tolist()\n", "\n", "# Creating the predicted variable to be rejected and accepted as binary\n", "y = clean_bias.groupby('unique_id')['submission_grade'].apply(lambda x: x.iloc[-1]).map({'rejected': 'non-violation','accepted': 'violation'}).tolist()\n", "\n", "# Split the data in such a way that y is stratified\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1, stratify=y)" ], "metadata": { "id": "lUNFVtsBu8Ni", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d621a144-f75f-447c-c2ec-26804b4ee337" }, "execution_count": 101, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ ":5: DtypeWarning: Columns (27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198) have mixed types. Specify dtype option on import or set low_memory=False.\n", " bias = pd.read_csv('/content/drive/MyDrive/hackathon/misdirection.csv')\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Tokenizing Data" ], "metadata": { "id": "XJO2X9MOvMEH" } }, { "cell_type": "code", "source": [ "# Load tokenizer and model\n", "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n", "model = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=2)\n", "\n", "# Tokenize the data\n", "train_encodings = tokenizer(X_train, truncation=True, padding=True, max_length=256)\n", "test_encodings = tokenizer(X_test, truncation=True, padding=True, max_length=256)\n", "\n", "# Creating a customdataset\n", "class CustomDataset(Dataset):\n", " def __init__(self, encodings, labels):\n", " self.encodings = encodings\n", " self.labels = labels\n", "\n", " def __getitem__(self, idx):\n", " item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n", " label = 0 if self.labels[idx] == 'non-violation' else 1\n", " item['labels'] = torch.tensor(label, dtype=torch.long)\n", " return item\n", "\n", " def __len__(self):\n", " return len(self.labels)\n", "\n", "# Create the dataset objects\n", "train_dataset = CustomDataset(train_encodings, [0 if label == 'non-violation' else 1 for label in y_train])\n", "test_dataset = CustomDataset(test_encodings, [0 if label == 'non-violation' else 1 for label in y_test])\n" ], "metadata": { "id": "M3QaTZesvJZd", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "51254ab7-ac1f-42e4-e27d-453bea75a86b" }, "execution_count": 102, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] } ] }, { "cell_type": "markdown", "source": [ "# Creating Model" ], "metadata": { "id": "ePToZE3OA4qc" } }, { "cell_type": "code", "source": [ "# Defining the metrics\n", "def compute_metrics(pred):\n", " labels = pred.label_ids\n", " preds = pred.predictions.argmax(-1)\n", " accuracy = accuracy_score(labels, preds)\n", " precision = precision_score(labels, preds, average='weighted')\n", " recall = recall_score(labels, preds, average='weighted')\n", " f1 = f1_score(labels, preds, average='weighted')\n", "\n", " return {\n", " \"accuracy\": accuracy,\n", " \"precision\": precision,\n", " \"recall\": recall,\n", " \"f1\": f1\n", " }\n", "\n", "# Objective function for Optuna\n", "def objective(trial):\n", " # Preventing overfitting and defining hyperparameters\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", " training_args = TrainingArguments(\n", " output_dir=\"./misdirection_classification\",\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", " per_device_train_batch_size=trial.suggest_categorical('batch_size', [8, 16, 32]),\n", " gradient_accumulation_steps=2,\n", " num_train_epochs=trial.suggest_int('num_train_epochs', 3, 10),\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", " save_strategy=\"epoch\",\n", " evaluation_strategy=\"epoch\",\n", " logging_dir=\"./logs\",\n", " logging_steps=10,\n", " load_best_model_at_end=True,\n", " metric_for_best_model=\"f1\",\n", " push_to_hub=False,\n", " )\n", "\n", " # Tokenizing the data\n", " train_encodings_fold = tokenizer(X_train, truncation=True, padding=True, max_length=256)\n", " val_encodings_fold = tokenizer(X_test, truncation=True, padding=True, max_length=256)\n", "\n", " # Creating dataset objects\n", " train_dataset_fold = CustomDataset(train_encodings_fold, y_train)\n", " val_dataset_fold = CustomDataset(val_encodings_fold, y_test)\n", "\n", " # Initializing a new model\n", " model_fold = model_init(dropout_rate)\n", "\n", " # Defining the trainer\n", " trainer = Trainer(\n", " model=model_fold,\n", " args=training_args,\n", " train_dataset=train_dataset_fold,\n", " eval_dataset=val_dataset_fold,\n", " tokenizer=tokenizer,\n", " compute_metrics=compute_metrics,\n", " )\n", "\n", " # Training the model\n", " trainer.train()\n", "\n", " eval_result = trainer.evaluate(eval_dataset=val_dataset_fold)\n", " accuracy = eval_result['eval_accuracy']\n", " precision = eval_result['eval_precision']\n", " recall = eval_result['eval_recall']\n", " f1 = eval_result['eval_f1']\n", "\n", " # Calculate the composite score using average metrics (f1 yielded best results in end)\n", " composite_score = (\n", " 0.25 * accuracy +\n", " 0.25 * precision +\n", " 0.25 * recall +\n", " 0.25 * f1\n", " )\n", "\n", " return f1\n", "\n", "# Model initialization function\n", "def model_init(dropout_rate):\n", " model = AutoModelForSequenceClassification.from_pretrained(\"distilbert-base-uncased\", num_labels=2)\n", " model.classifier.dropout = torch.nn.Dropout(p=dropout_rate)\n", "\n", " return model\n", "\n", "# Run Optuna optimization\n", "study = optuna.create_study(direction='maximize')\n", "study.optimize(objective, n_trials=15)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "vJB5bX3Dr5uD", "outputId": "4cde144a-faa5-47fc-e65d-96d5ab848c04", "collapsed": true }, "execution_count": 103, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:10:40,264] A new study created in memory with name: no-name-5cb80da5-d0bf-4b2e-8a50-1a6437ae1326\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [252/252 00:44, Epoch 9/9]\n", "
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EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6790000.6624670.6126130.7090140.6126130.515776
20.6031000.6698530.6486490.6764490.6486490.605478
30.4202000.7077830.6486490.6454140.6486490.645701
40.2354000.8649830.6666670.6650380.6666670.656150
50.0922001.0551450.6936940.6915870.6936940.688914
60.1522001.2188250.6756760.6733290.6756760.673615
70.0869001.4634940.6486490.6462860.6486490.634805
80.0536001.2550540.7027030.7035030.7027030.703046
90.0440001.2788570.7117120.7117120.7117120.711712

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

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\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:11:26,200] Trial 0 finished with value: 0.7117117117117117 and parameters: {'dropout_rate': 0.10385627724223441, 'learning_rate': 4.81278007062444e-05, 'batch_size': 8, 'num_train_epochs': 9, 'weight_decay': 0.0034752897209702643}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [35/35 00:22, Epoch 5/5]\n", "
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EpochTraining LossValidation LossAccuracyPrecisionRecallF1
1No log0.6809310.5675680.3221330.5675680.410997
20.6854000.6757880.5675680.3221330.5675680.410997
30.6721000.6717920.5765770.7574940.5765770.431023
40.6721000.6682680.6036040.7666080.6036040.487196
50.6649000.6668470.5945950.6779190.5945950.481713

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

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\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:11:50,463] Trial 1 finished with value: 0.487195793078146 and parameters: {'dropout_rate': 0.17068673489931369, 'learning_rate': 1.1286528878992392e-05, 'batch_size': 32, 'num_train_epochs': 5, 'weight_decay': 0.002323647746273169}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [126/126 00:44, Epoch 9/9]\n", "
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EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6896000.6619950.5765770.6141140.5765770.445486
20.6463000.6395640.6306310.6268640.6306310.612844
30.5536000.6440090.6396400.6768140.6396400.586042
40.5144000.6607710.6216220.6181330.6216220.597751
50.4043000.6932560.6486490.6462860.6486490.634805
60.3417000.7486490.6306310.6282280.6306310.628968
70.2933000.7923080.6486490.6501600.6486490.628320
80.2156000.8288980.6126130.6157920.6126130.613758
90.2187000.8162340.6396400.6351730.6396400.634017

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:12:36,246] Trial 2 finished with value: 0.6348050177837412 and parameters: {'dropout_rate': 0.28121765507438046, 'learning_rate': 2.9241433152657863e-05, 'batch_size': 16, 'num_train_epochs': 9, 'weight_decay': 0.0001405319811092116}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [63/63 00:40, Epoch 9/9]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
1No log0.6742490.5675680.3221330.5675680.410997
20.6806000.6588230.6216220.6887340.6216220.541507
30.6454000.6392420.6396400.6662790.6396400.592390
40.6454000.6297360.6576580.6779950.6576580.623359
50.5939000.6283090.6396400.6470950.6396400.608408
60.5445000.6343350.6486490.6620020.6486490.615876
70.5445000.6403410.6306310.6252740.6306310.621559
80.5012000.6421470.6486490.6501600.6486490.628320
90.4709000.6444520.6666670.6759260.6666670.643810

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:13:17,790] Trial 3 finished with value: 0.6438095238095238 and parameters: {'dropout_rate': 0.18544344792139109, 'learning_rate': 2.5089800016424374e-05, 'batch_size': 32, 'num_train_epochs': 9, 'weight_decay': 0.0014704792938239244}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [84/84 00:29, Epoch 6/6]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6869000.6700040.5765770.7574940.5765770.431023
20.6634000.6448840.6486490.6683400.6486490.610908
30.6053000.6324760.6486490.7006760.6486490.593089
40.5772000.6302100.6486490.6531530.6486490.624556
50.5490000.6307770.6486490.6620020.6486490.615876
60.5117000.6337920.6576580.6779950.6576580.623359

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:13:50,733] Trial 4 finished with value: 0.6245559845559847 and parameters: {'dropout_rate': 0.12426035705506583, 'learning_rate': 2.1090702325374656e-05, 'batch_size': 16, 'num_train_epochs': 6, 'weight_decay': 0.028336717591528258}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [280/280 00:48, Epoch 10/10]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6895000.6741430.5675680.3221330.5675680.410997
20.6376000.6401180.6396400.6519610.6396400.603536
30.5453000.6430130.6216220.6259280.6216220.583713
40.4825000.6549440.6846850.6870470.6846850.672261
50.3752000.6971630.6666670.6666670.6666670.653533
60.3447000.7594790.6036040.5975640.6036040.597418
70.2447000.7978050.6396400.6366750.6396400.623906
80.2408000.8501790.6126130.6074490.6126130.607573
90.1826000.8701040.6126130.6059170.6126130.603098
100.1736000.8858650.6036040.5987350.6036040.599399

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:14:40,878] Trial 5 finished with value: 0.6722609133956652 and parameters: {'dropout_rate': 0.28622418773712177, 'learning_rate': 1.7492165352167913e-05, 'batch_size': 8, 'num_train_epochs': 10, 'weight_decay': 0.0043284059814853865}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [98/98 00:35, Epoch 7/7]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6849000.6660220.5945950.7635140.5945950.469082
20.6564000.6457270.6396400.6519610.6396400.603536
30.5983000.6342360.6486490.6869230.6486490.599552
40.5735000.6367780.5945950.5846610.5945950.571138
50.5384000.6405140.6216220.6225540.6216220.588829
60.5016000.6474620.6126130.6076080.6126130.586049
70.5024000.6481640.6126130.6058200.6126130.593958

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:15:17,758] Trial 6 finished with value: 0.6035358666937614 and parameters: {'dropout_rate': 0.3857309932545293, 'learning_rate': 1.754805613368691e-05, 'batch_size': 16, 'num_train_epochs': 7, 'weight_decay': 0.00022149023899491855}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [70/70 00:23, Epoch 5/5]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6921000.6790930.5675680.3221330.5675680.410997
20.6712000.6684370.5675680.3221330.5675680.410997
30.6370000.6568990.6306310.7000250.6306310.556734
40.6175000.6508190.6576580.7098150.6576580.606740
50.5939000.6486140.6666670.7183980.6666670.620088

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n", "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:15:42,512] Trial 7 finished with value: 0.6200881920627144 and parameters: {'dropout_rate': 0.22275716979667287, 'learning_rate': 1.8559762469144838e-05, 'batch_size': 16, 'num_train_epochs': 5, 'weight_decay': 0.00013692300541561439}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [252/252 00:46, Epoch 9/9]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6818000.6620470.6126130.7090140.6126130.515776
20.6129000.6378750.6486490.7006760.6486490.593089
30.4581000.6798490.6126130.6059170.6126130.603098
40.3183000.7496870.6576580.6557310.6576580.645550
50.1942000.8851840.6396400.6380190.6396400.638621
60.1792000.9627350.6396400.6357790.6396400.635818
70.1425001.0772500.6666670.6666670.6666670.653533
80.0901001.1351060.6396400.6396400.6396400.639640
90.0724001.1421090.6576580.6543000.6576580.654027

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:16:29,929] Trial 8 finished with value: 0.654026754026754 and parameters: {'dropout_rate': 0.11524013563643445, 'learning_rate': 3.201697528287598e-05, 'batch_size': 8, 'num_train_epochs': 9, 'weight_decay': 0.0261128193231869}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [84/84 00:15, Epoch 3/3]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6831000.6667340.6036040.6954950.6036040.498999
20.6370000.6438470.6576580.6779950.6576580.623359
30.5846000.6373770.6576580.6860710.6576580.618297

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:16:46,879] Trial 9 finished with value: 0.6233590733590735 and parameters: {'dropout_rate': 0.2525876251383933, 'learning_rate': 2.0178432113594854e-05, 'batch_size': 8, 'num_train_epochs': 3, 'weight_decay': 0.00018893632379971434}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [196/196 00:34, Epoch 7/7]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6769000.6500300.6486490.7191860.6486490.586041
20.5851000.6880440.6306310.6475200.6306310.585246
30.3699000.7746440.6216220.6172570.6216220.617609
40.2400000.9327060.6126130.6086070.6126130.609363
50.0896001.1539120.6396400.6351730.6396400.634017
60.1205001.2666310.6306310.6270100.6306310.627532
70.0961001.2795110.6396400.6380190.6396400.638621

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:17:22,326] Trial 10 finished with value: 0.6386213341000576 and parameters: {'dropout_rate': 0.49442102479647, 'learning_rate': 4.938833942332147e-05, 'batch_size': 8, 'num_train_epochs': 7, 'weight_decay': 0.006346793446160044}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [280/280 00:52, Epoch 10/10]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6812000.6751160.5675680.3221330.5675680.410997
20.6596000.6629470.6306310.7000250.6306310.556734
30.5951000.6528900.6396400.7100740.6396400.571572
40.5547000.6439920.6396400.6404150.6396400.616906
50.4866000.6553070.6306310.6303800.6306310.605302
60.4793000.6663500.6126130.6065490.6126130.605488
70.3934000.7142630.6396400.6519610.6396400.603536
80.3913000.7034330.6396400.6351730.6396400.634017
90.3515000.7251530.6576580.6540850.6576580.650340
100.3499000.7308340.6576580.6540850.6576580.650340

" ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, msg_start, len(result))\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:18:16,580] Trial 11 finished with value: 0.6503401486301623 and parameters: {'dropout_rate': 0.3555925378234452, 'learning_rate': 1.3026823273482803e-05, 'batch_size': 8, 'num_train_epochs': 10, 'weight_decay': 0.0099373404485024}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [280/280 00:50, Epoch 10/10]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6788000.6489640.6666670.7354170.6666670.613956
20.5920000.6845900.6306310.6475200.6306310.585246
30.3661000.7841540.6306310.6255200.6306310.623838
40.1867000.9175080.6576580.6540850.6576580.650340
50.0709001.2243170.6306310.6261040.6306310.625825
60.1187001.4038260.6306310.6252740.6306310.621559
70.0863001.5650570.6216220.6159770.6216220.605101
80.0772001.4636740.6576580.6543000.6576580.654027
90.0690001.5305960.6666670.6634150.6666670.662330
100.0741001.5206720.6576580.6543000.6576580.654027

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:19:08,132] Trial 12 finished with value: 0.6623299822590183 and parameters: {'dropout_rate': 0.34512035668190677, 'learning_rate': 4.891257926913519e-05, 'batch_size': 8, 'num_train_epochs': 10, 'weight_decay': 0.08637044984974761}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [224/224 00:41, Epoch 8/8]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6759000.6479470.6576580.7275800.6576580.600163
20.6028000.6546250.6306310.6554480.6306310.579017
30.4139000.7287570.6126130.6055680.6126130.600390
40.2811000.7838600.6306310.6255200.6306310.623838
50.1395000.9821950.6306310.6297530.6306310.630141
60.1753001.0124960.6396400.6367300.6396400.637350
70.1264001.1878810.6306310.6259060.6306310.616077
80.0930001.1537360.6396400.6357790.6396400.635818

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:19:51,798] Trial 13 finished with value: 0.6373497243062459 and parameters: {'dropout_rate': 0.427562047440541, 'learning_rate': 3.7063781644991714e-05, 'batch_size': 8, 'num_train_epochs': 8, 'weight_decay': 0.0007509175073472913}. Best is trial 0 with value: 0.7117117117117117.\n", ":18: FutureWarning: suggest_uniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float instead.\n", " dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)\n", ":21: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " learning_rate=trial.suggest_loguniform('learning_rate', 1e-5, 5e-5),\n", ":25: FutureWarning: suggest_loguniform has been deprecated in v3.0.0. This feature will be removed in v6.0.0. See https://github.com/optuna/optuna/releases/tag/v3.0.0. Use suggest_float(..., log=True) instead.\n", " weight_decay=trial.suggest_loguniform('weight_decay', 1e-4, 1e-1),\n", "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n", "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [280/280 00:50, Epoch 10/10]\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6839000.6696240.5765770.7574940.5765770.431023
20.6478000.6507470.6486490.6620020.6486490.615876
30.5543000.6418410.6486490.6683400.6486490.610908
40.5175000.6528590.6126130.6055680.6126130.600390
50.4373000.6791090.6306310.6253910.6306310.618977
60.4116000.7146980.6216220.6198860.6216220.620552
70.3153000.7518600.6036040.5966980.6036040.574142
80.3252000.7595000.6126130.6074490.6126130.607573
90.2596000.7760520.5945950.5875780.5945950.587139
100.2423000.7815410.6126130.6074490.6126130.607573

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "[I 2024-07-16 03:20:43,756] Trial 14 finished with value: 0.6205524008050604 and parameters: {'dropout_rate': 0.3157675657890665, 'learning_rate': 1.4572481345426317e-05, 'batch_size': 8, 'num_train_epochs': 10, 'weight_decay': 0.0006477493771776529}. Best is trial 0 with value: 0.7117117117117117.\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Final Model" ], "metadata": { "id": "1OiFIHjlvkBn" } }, { "cell_type": "code", "source": [ "# Retrieve the best parameters from the Optuna study\n", "best_params = study.best_params\n", "\n", "# Define training arguments using the best parameters\n", "training_args = TrainingArguments(\n", " output_dir=\"predicting_misdirection\",\n", " learning_rate=best_params['learning_rate'],\n", " per_device_train_batch_size=best_params['batch_size'],\n", " gradient_accumulation_steps=2,\n", " num_train_epochs=best_params['num_train_epochs'],\n", " weight_decay=best_params['weight_decay'],\n", " save_strategy=\"epoch\",\n", " evaluation_strategy=\"epoch\",\n", " logging_dir=\"logs\",\n", " logging_steps=10,\n", " load_best_model_at_end=True,\n", " metric_for_best_model=\"f1\",\n", " push_to_hub=False,\n", ")\n", "\n", "# Define a data collator\n", "data_collator = DataCollatorWithPadding(tokenizer)\n", "\n", "# Initialize the trainer with the specified arguments\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=train_dataset,\n", " eval_dataset=test_dataset,\n", " tokenizer=tokenizer,\n", " data_collator=data_collator,\n", " compute_metrics=compute_metrics,\n", ")\n" ], "metadata": { "id": "xfA4-qBZvi3Q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "4506ec33-7c4d-442a-92c2-4425333e1b96" }, "execution_count": 105, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n", " warnings.warn(\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Training Final Model" ], "metadata": { "id": "gsRP-iWxvpkj" } }, { "cell_type": "code", "source": [ "# Training the final model on hyperparameters\n", "trainer.train()" ], "metadata": { "id": "RviNqVVevr6-", "colab": { "base_uri": "https://localhost:8080/", "height": 411 }, "outputId": "3a65c010-7b27-49bd-9607-4933e4af5194" }, "execution_count": 106, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [252/252 00:37, Epoch 9/9]\n", "
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EpochTraining LossValidation LossAccuracyPrecisionRecallF1
10.6794000.6453020.6396400.6908410.6396400.579119
20.5817000.6867400.6396400.6662790.6396400.592390
30.3639000.7679760.6216220.6184310.6216220.619217
40.2073000.8973610.6756760.6732400.6756760.668743
50.0729001.0735670.6936940.6916280.6936940.691747
60.1303001.1722080.6666670.6638170.6666670.663870
70.0675001.4547010.6576580.6596600.6576580.639553
80.0682001.3581680.6486490.6516690.6486490.649687
90.0678001.3401210.6486490.6495650.6486490.649054

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=252, training_loss=0.26621294730446404, metrics={'train_runtime': 37.8583, 'train_samples_per_second': 104.838, 'train_steps_per_second': 6.656, 'total_flos': 262881552632832.0, 'train_loss': 0.26621294730446404, 'epoch': 9.0})" ] }, "metadata": {}, "execution_count": 106 } ] }, { "cell_type": "markdown", "source": [ "# Evaluating Final Mode" ], "metadata": { "id": "zeadNEh5v3vw" } }, { "cell_type": "code", "source": [ "# Getting evaluation results\n", "eval_result = trainer.evaluate(eval_dataset=test_dataset)\n", "for key, value in eval_result.items():\n", " print(f\"{key}: {value}\")" ], "metadata": { "id": "da_nsL-0v5w3", "colab": { "base_uri": "https://localhost:8080/", "height": 198 }, "outputId": "5c7bb14f-49b9-4084-b65d-959cc01930c3" }, "execution_count": 107, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [14/14 00:00]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "eval_loss: 1.0735671520233154\n", "eval_accuracy: 0.6936936936936937\n", "eval_precision: 0.6916278558069603\n", "eval_recall: 0.6936936936936937\n", "eval_f1: 0.6917472656603092\n", "eval_runtime: 0.2672\n", "eval_samples_per_second: 415.362\n", "eval_steps_per_second: 52.388\n", "epoch: 9.0\n" ] } ] }, { "cell_type": "code", "source": [ "# Getting confusion matrix\n", "predictions = trainer.predict(test_dataset)\n", "predicted_labels = np.argmax(predictions.predictions, axis=1)\n", "\n", "true_labels = [item['labels'].item() for item in test_dataset]\n", "\n", "cm = confusion_matrix(true_labels, predicted_labels)\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "plt.figure(figsize=(10, 7))\n", "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", "plt.xlabel('Predicted Labels')\n", "plt.ylabel('True Labels')\n", "plt.title('Confusion Matrix')\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 641 }, "id": "eibX34uIIdvV", "outputId": "dc0d594e-24a7-4457-f551-e36d6772ee2d" }, "execution_count": 108, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "notebook_login()\n", "trainer.push_to_hub(\"/Eappelson/new\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 315, "referenced_widgets": [ "967d7138f5634e3aad71c24a0d61b960", "34eb20293e2a4a84baef6e13eb0e1bda", "5bc757394af34fba9946c79a3fd58bd2", "d830f9fa71d84ffaa2f34c91d7cf7095", "879cf673fd6d465793bcda625010394a", "cc19864b7dc24b6cac02d972176fbcc2", "142ad55d04f24d65801045b2e0d4cee5", "23718cec0d6c43ba9ba1a9f3a98a2796", "e38cb7120c9a47578b24f243ea619830", "6f26d15a472248ef860bdcb05fa20511", "5a9d9e58fb3e4a6383595dd7b4b2b019", "f900d1aead944387835d39d5447e2a31", "910613b720ad4521ab8239cc20aaf631", "87452d2739f54cf69431929baeda61f9", "27b9038dac7b42eabc267531523c0757", "745a27f290b14bcdaf744982975bf991", "22c1dbf3a3d74777b1d4bccfcb0b72b0", "0361d67395104c868b59b605342e55e8", "c832d4ca0b3547b383c87713e80ccf90", "98930ba730804a649bf0e161fa7d6f3c", "a0a7c273d93c490a9d32c26b83be9e1c", "2f3e9ecadb724d988a3fb696326b3fea", "5b1067f466224ad597f8df8ee31979da", "0c8a25e1a20c436a97a5176fea6d2a68", "312f6060c38543c3a396afb440c280ca", "1fa2a0a699cc47f0bc9dc50d829859f8", "442cde46c27348d493b25f2eb1970b50", "7c88c88a22c448f9bfff6caeddc17383", "e5be536c2566451f92b679732323f004", "ebfbaa7eb66540debd9a12e860b81c37", "89070995253f429aa443778b342b5638", "008235cd9f90455ab579712149cb9dc5", "a3c76838a37b4db8bf0d0dfff733e457", "3b75d3c5be684415a341334ed52fdc26", "367ed3754c41483d8ef3c5da6a37c3f4", "491022c6427d467bbfc36a8db77c2a1d", "fcbc100556244fe4b1a4d08bc05290d8", "0c7b6a8ffd254b9cb0d7ac697fb37b1e", "ddbb6b95aa18486888033f8ba8d3351a", "afb874092c92484988a42e27c46963fa", "ce673636c8924febbdc49b62d0064cb0", "42e892c6618145759befa870ae7dad34", "fead4b216a864bc0b9d5210c54981dbf", "c76f3bff7a5b4824b9fa5559e80d7430", "84ae40bc1cee43c79458b79e1ef707b0", "1ba2d037918a4139a236a36d19306626", "02e94884ec1a4906b5a3a6b1ce565894", "80fcda9092974975af20c79b65098be1", "b4fa1e877c1f4e74a35c37d5f09c9a95", "0a7d1eb4cec041838b449158c7e0553d", "fc682eb580864f5cae4eb02e99f0a3dc", "1ff653660a724788953f4e6f65c59388", "d518949beefe4cc7981fbe9ee84b7645", "73a94300a2d7450d9481265a5a620b45", "1ab5f0009308484aaeaf8791024c385d", "974dfdc49a9b458ab9d0e4b72b00f040", "a0cc22b7321a4d0d862ff1b18f620f2f", "331ef6b4da894920a687c6613f97c5f2", "025626b14c6e407da4aeef1cd77c9da2", "00edb680db88411585dba93bb25c691d", "c482e8d60c674700b474dd2ba2f75499", "364861042d5244afb3f4cab0645a49e7", "3c6a874584b847ee8d3a781e75a2cd2e", "4f85f74c72c94a84911efc66932feac2", "60cd5ff423714a329215ba006cb30588" ] }, "id": "nSGPW4oFkZRZ", "outputId": "59fc1549-eca1-45b1-9a57-cf863e478991" }, "execution_count": 109, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='