{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "45ryTg-ZLHcI" }, "source": [ "# wav2vec2 implementation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "40e143e654424e8f96a8acc62749b56c", "8a2b1e5cd1ed41208a04ce10bd37505d", "12ef45e353234cd187b89bd3b4adc885", "4934e2a2e84948d993219a887e6f932a", "08d5f1ac3e9e43d28bf0f2a1ca3780a9", "25ce37ab6aaf4bf6b0dcb920e2d3cb3e", "5e10377b33264c4a9ac9727d8b9e8e44", "8de1ae82920f4af996346673195773f2", "ffb9e0b266b04194bb777818b49b7b1e", "fbdc89bc7bca4e7b9af17480f1b51770", "9051309263bf4dcab2a84a14085e010f", "b63361a7078549919d77d03dbe797aa9", "99248a3e649b4b4fb4a6693fc781a3ae", "f77ea67f2d644e949477239def77cce3", "d5b9e59652574ae39463a62e91acbc6e", "ef93f9eb83cb461f90c8b5cb85fab024", "82dc818a6f8049b08e9482b672cf3418", "e27a9962ac30459c8dae3d495c40ab8c", "1622a1fc464742bdaeeec936fb81fd2c", "f4f3e6103c844b89b42c71709ee180ba", "abc8a5b72f034606b3fe3b9e5d0ef8bb", "1b77a27420aa4d7d90af158073b07e0c", "dbfe5751c7a843cbac2f760232c268b2", "7bcc87bdc1de4542be9816c23ef918d8", "789e1107230e4431a3b8fc36ccc0f370", "3719dfed7c0840e6bc152595a3c85487", "5d5dc262dd2a4ec282a1125b9dbaf63a", "ddf902c6d15240f294215ca9ebcec5b9", "3c66cc92b6dc443daa79dd82e564f407", "aea70cfcdeb94841bc1bc616cbbd1530", "678984ff6763443b9e57ad16e65e0c14", "5534f8d2d13b4f47b0dfacb3be042b68", "099820472ef94622bc930c7dcc27392a", "4a05f692345540a7ab688eae2ff4c635", "3a4ece5788cb4e4fa02ed2e22c6c7416", "2d1043343d8d4d1f8eb0044e1fb0e48c", "a7a6f6ff7cac4c1b8dce48b4038af21f", "d90a3d5595744e57b456a64dce5a6662", "880aab255d3c44798ec400644408b089", "f8f99acab78d454b8505aa41c5b2fdb9", "ba121eca7b8f47559d54e7a3e02cc32c", "8ab02e1b70cf46b6b2c2a38699d64de5", "c85639bd3d5240278f96912779017988", "4ce71846fdcc4524ae55b33671370e81" ] }, "id": "TQJi6vt_uXQM", "outputId": "550c4872-0a6d-4be8-ca9b-5f6e44d12327" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Using device: cuda\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "40e143e654424e8f96a8acc62749b56c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "preprocessor_config.json: 0%| | 0.00/159 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# =============================================================================\n", "# STEP 0: SETUP, LIBRARIES & DATA PREPARATION\n", "# =============================================================================\n", "# Install required libraries\n", "!pip install transformers[torch] accelerate -q\n", "\n", "import os\n", "import numpy as np\n", "import pandas as pd\n", "import librosa\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from tqdm import tqdm\n", "import kagglehub\n", "\n", "import torch\n", "import torch.nn as nn\n", "from torch.utils.data import DataLoader, Dataset\n", "from transformers import AutoFeatureExtractor, AutoModelForAudioClassification, get_scheduler\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import LabelEncoder\n", "from sklearn.metrics import classification_report, confusion_matrix\n", "from sklearn.utils.class_weight import compute_class_weight\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "print(f\"\\nUsing device: {device}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "1854c1a5" }, "source": [ "## Data Loading and Preparation\n", "\n", "This section downloads the RAVDESS and CREMA-D datasets using `kagglehub`. It then processes the metadata to create a DataFrame with file paths and sentiment labels, using a sentiment mapping to group related emotions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "4ce1824e" }, "outputs": [], "source": [ "# --- Download & Process Datasets ---\n", "RAVDESS_PATH = kagglehub.dataset_download(\"uwrfkaggler/ravdess-emotional-speech-audio\")\n", "CREMA_D_PATH = kagglehub.dataset_download(\"ejlok1/cremad\")\n", "\n", "sentiment_map = {'happy': 'positive', 'surprised': 'positive', 'sad': 'negative', 'angry': 'negative', 'fearful': 'negative', 'disgust': 'negative', 'neutral': 'neutral', 'calm': 'neutral'}\n", "ravdess_emotion_map = {'01': 'neutral', '02': 'calm', '03': 'happy', '04': 'sad', '05': 'angry', '06': 'fearful', '07': 'disgust', '08': 'surprised'}\n", "ravdess_data = []\n", "for dirpath, _, filenames in os.walk(RAVDESS_PATH):\n", " for filename in filenames:\n", " if filename.endswith('.wav'):\n", " emotion_code = filename.split('-')[2]; emotion = ravdess_emotion_map.get(emotion_code); sentiment = sentiment_map.get(emotion)\n", " if sentiment: ravdess_data.append({\"filepath\": os.path.join(dirpath, filename), \"sentiment\": sentiment})\n", "ravdess_df = pd.DataFrame(ravdess_data)\n", "\n", "crema_emotion_map = {'HAP': 'happy', 'SAD': 'sad', 'ANG': 'angry', 'FEA': 'fearful', 'DIS': 'disgust', 'NEU': 'neutral'}\n", "crema_data = []\n", "crema_audio_path = os.path.join(CREMA_D_PATH, \"AudioWAV\")\n", "for filename in os.listdir(crema_audio_path):\n", " if filename.endswith('.wav'):\n", " emotion_code = filename.split('_')[2]; emotion = crema_emotion_map.get(emotion_code); sentiment = sentiment_map.get(emotion)\n", " if sentiment: crema_data.append({\"filepath\": os.path.join(crema_audio_path, filename), \"sentiment\": sentiment})\n", "crema_df = pd.DataFrame(crema_data)\n", "\n", "combined_df = pd.concat([ravdess_df, crema_df], ignore_index=True).sample(frac=1, random_state=42).reset_index(drop=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "86de5576" }, "source": [ "## Wav2Vec2 Preparation and Dataset Class\n", "\n", "This section loads the pre-trained Wav2Vec 2.0 feature extractor and defines the `AudioDataset` class. This class handles loading the audio files, resampling them to the target sampling rate, and processing them using the Wav2Vec 2.0 feature extractor. It also includes a `collate_fn` to handle potential errors during audio processing." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7222e5b9" }, "outputs": [], "source": [ "# =============================================================================\n", "# STEP 1: WAV2VEC2 PREPARATION\n", "# =============================================================================\n", "MODEL_CHECKPOINT = \"facebook/wav2vec2-base\"\n", "TARGET_SAMPLING_RATE = 16000\n", "feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_CHECKPOINT)\n", "\n", "class AudioDataset(Dataset):\n", " def __init__(self, df, feature_extractor, max_duration_s=5.0):\n", " self.filepaths = df['filepath'].tolist(); self.labels = df['label'].tolist()\n", " self.feature_extractor = feature_extractor; self.max_length = int(max_duration_s * TARGET_SAMPLING_RATE)\n", " def __len__(self): return len(self.filepaths)\n", " def __getitem__(self, idx):\n", " filepath = self.filepaths[idx]; label = self.labels[idx]\n", " try:\n", " audio, sr = librosa.load(filepath, sr=None)\n", " if sr != TARGET_SAMPLING_RATE: audio = librosa.resample(y=audio, orig_sr=sr, target_sr=TARGET_SAMPLING_RATE)\n", " inputs = self.feature_extractor(audio, sampling_rate=TARGET_SAMPLING_RATE, max_length=self.max_length, truncation=True, padding='max_length', return_tensors=\"pt\")\n", " input_values = inputs.input_values.squeeze(0)\n", " except Exception as e: print(f\"Error processing {filepath}: {e}\"); return None, None\n", " return input_values, label\n", "\n", "le = LabelEncoder(); combined_df['label'] = le.fit_transform(combined_df['sentiment'])\n", "X_train_df, X_temp_df = train_test_split(combined_df, test_size=0.3, random_state=42, stratify=combined_df['label'])\n", "X_val_df, X_test_df = train_test_split(X_temp_df, test_size=0.5, random_state=42, stratify=X_temp_df['label'])\n", "train_dataset = AudioDataset(X_train_df, feature_extractor); val_dataset = AudioDataset(X_val_df, feature_extractor); test_dataset = AudioDataset(X_test_df, feature_extractor)\n", "def collate_fn(batch):\n", " batch = [b for b in batch if b[0] is not None];\n", " if not batch: return None, None\n", " return torch.utils.data.dataloader.default_collate(batch)\n", "train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True, collate_fn=collate_fn)\n", "val_loader = DataLoader(val_dataset, batch_size=16, shuffle=False, collate_fn=collate_fn)\n", "test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False, collate_fn=collate_fn)" ] }, { "cell_type": "markdown", "metadata": { "id": "31cb70d3" }, "source": [ "## Model and Training Setup\n", "\n", "This section loads the pre-trained Wav2Vec 2.0 model for audio classification and sets up the loss function and optimizer. It also calculates class weights to handle the imbalanced dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "326b7435" }, "outputs": [], "source": [ "# =============================================================================\n", "# STEP 2: MODEL & TRAINING SETUP\n", "# =============================================================================\n", "NUM_CLASSES = len(le.classes_)\n", "model = AutoModelForAudioClassification.from_pretrained(MODEL_CHECKPOINT, num_labels=NUM_CLASSES).to(device)\n", "class_weights_np = compute_class_weight('balanced', classes=np.unique(X_train_df['label']), y=X_train_df['label'])\n", "class_weights = torch.tensor(class_weights_np, dtype=torch.float32).to(device)\n", "criterion = nn.CrossEntropyLoss(weight=class_weights)" ] }, { "cell_type": "markdown", "metadata": { "id": "8aaf7e60" }, "source": [ "## Two-Stage Fine-Tuning\n", "\n", "This section implements a two-stage fine-tuning process for the Wav2Vec 2.0 model. In Stage 1, only the classification head is trained with the base model frozen. In Stage 2, all layers are unfrozen and the entire model is fine-tuned with a lower learning rate and a learning rate scheduler." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6bbccd3c" }, "outputs": [], "source": [ "# =============================================================================\n", "# STEP 3: TWO-STAGE FINE-TUNING\n", "# =============================================================================\n", "\n", "# --- STAGE 1: Train only the classification head ---\n", "print(\"\\n--- STAGE 1: Freezing base model and training classifier head ---\")\n", "# Freeze the parameters of the base Wav2Vec2 model\n", "for param in model.wav2vec2.parameters():\n", " param.requires_grad = False\n", "\n", "# The optimizer will only update the weights of the unfrozen classifier head\n", "optimizer = torch.optim.AdamW(model.classifier.parameters(), lr=1e-3)\n", "STAGE1_EPOCHS = 3\n", "\n", "for epoch in range(STAGE1_EPOCHS):\n", " model.train(); train_loss = 0.0\n", " for inputs, labels in tqdm(train_loader, desc=f\"Stage 1 - Epoch {epoch+1}/{STAGE1_EPOCHS}\"):\n", " if inputs is None: continue\n", " inputs, labels = inputs.to(device), labels.to(device)\n", " optimizer.zero_grad(); outputs = model(inputs)\n", " loss = criterion(outputs.logits, labels)\n", " loss.backward(); optimizer.step(); train_loss += loss.item()\n", "\n", " model.eval(); val_loss, val_correct, val_total = 0.0, 0, 0\n", " with torch.no_grad():\n", " for inputs, labels in val_loader:\n", " if inputs is None: continue\n", " inputs, labels = inputs.to(device), labels.to(device); outputs = model(inputs)\n", " loss = criterion(outputs.logits, labels); val_loss += loss.item()\n", " _, predicted = torch.max(outputs.logits, 1); val_total += labels.size(0); val_correct += (predicted == labels).sum().item()\n", "\n", " avg_train_loss = train_loss/len(train_loader); avg_val_loss = val_loss/len(val_loader); val_acc = val_correct/val_total\n", " print(f\"Stage 1 - Epoch {epoch+1} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | Val Acc: {val_acc:.4f}\")\n", "\n", "# --- STAGE 2: Unfreeze and fine-tune the entire model ---\n", "print(\"\\n--- STAGE 2: Unfreezing all layers and fine-tuning the entire model ---\")\n", "# Unfreeze all parameters\n", "for param in model.parameters():\n", " param.requires_grad = True\n", "\n", "# Create a new optimizer for the whole model with a lower learning rate\n", "optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)\n", "STAGE2_EPOCHS = 6 # Total epochs will be STAGE1 + STAGE2\n", "num_training_steps = STAGE2_EPOCHS * len(train_loader)\n", "num_warmup_steps = int(0.1 * num_training_steps)\n", "lr_scheduler = get_scheduler(\"linear\", optimizer, num_warmup_steps, num_training_steps)\n", "best_val_loss = float('inf')\n", "\n", "for epoch in range(STAGE2_EPOCHS):\n", " model.train(); train_loss = 0.0\n", " for inputs, labels in tqdm(train_loader, desc=f\"Stage 2 - Epoch {epoch+1}/{STAGE2_EPOCHS}\"):\n", " if inputs is None: continue\n", " inputs, labels = inputs.to(device), labels.to(device)\n", " optimizer.zero_grad(); outputs = model(inputs)\n", " loss = criterion(outputs.logits, labels)\n", " loss.backward(); optimizer.step(); lr_scheduler.step(); train_loss += loss.item()\n", "\n", " model.eval(); val_loss, val_correct, val_total = 0.0, 0, 0\n", " with torch.no_grad():\n", " for inputs, labels in val_loader:\n", " if inputs is None: continue\n", " inputs, labels = inputs.to(device), labels.to(device); outputs = model(inputs)\n", " loss = criterion(outputs.logits, labels); val_loss += loss.item()\n", " _, predicted = torch.max(outputs.logits, 1); val_total += labels.size(0); val_correct += (predicted == labels).sum().item()\n", "\n", " avg_train_loss = train_loss/len(train_loader); avg_val_loss = val_loss/len(val_loader); val_acc = val_correct/val_total\n", " print(f\"Stage 2 - Epoch {epoch+1} | Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | Val Acc: {val_acc:.4f}\")\n", "\n", " if avg_val_loss < best_val_loss:\n", " best_val_loss = avg_val_loss\n", " torch.save(model.state_dict(), 'best_wav2vec2_model_2stage.pth')" ] }, { "cell_type": "markdown", "metadata": { "id": "74a029a7" }, "source": [ "## Model Evaluation\n", "\n", "This section evaluates the final fine-tuned Wav2Vec 2.0 model on the hold-out test set. It loads the best model state dictionary, performs inference, and then displays the classification report and confusion matrix to assess the model's performance." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2a156c15" }, "outputs": [], "source": [ "# =============================================================================\n", "# STEP 4: EVALUATE THE FINAL MODEL\n", "# =============================================================================\n", "print(\"\\n--- Evaluating the final fine-tuned Wav2Vec2 model ---\")\n", "model.load_state_dict(torch.load('best_wav2vec2_model_2stage.pth'))\n", "model.eval()\n", "\n", "all_preds, all_labels = [], []\n", "with torch.no_grad():\n", " for inputs, labels in tqdm(test_loader, desc=\"Evaluating on Test Set\"):\n", " if inputs is None: continue\n", " inputs = inputs.to(device)\n", " outputs = model(inputs)\n", " _, predicted = torch.max(outputs.logits, 1)\n", " all_preds.extend(predicted.cpu().numpy())\n", " all_labels.extend(labels.numpy())\n", "\n", "print(\"\\n--- Final Wav2Vec2 Classification Report (2-Stage Training) ---\")\n", "print(classification_report(all_labels, all_preds, target_names=le.classes_))\n", "cm = confusion_matrix(all_labels, all_preds)\n", "plt.figure(figsize=(8, 6))\n", "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=le.classes_, yticklabels=le.classes_)\n", "plt.title('Final Wav2Vec2 Confusion Matrix (2-Stage)'); plt.ylabel('True Label'); plt.xlabel('Predicted Label')\n", "plt.show()" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "08d5f1ac3e9e43d28bf0f2a1ca3780a9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "099820472ef94622bc930c7dcc27392a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "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": "" } }, "12ef45e353234cd187b89bd3b4adc885": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_8de1ae82920f4af996346673195773f2", "max": 159, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_ffb9e0b266b04194bb777818b49b7b1e", "value": 159 } }, "1622a1fc464742bdaeeec936fb81fd2c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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": "20px" } }, "1b77a27420aa4d7d90af158073b07e0c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "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": "" } }, "25ce37ab6aaf4bf6b0dcb920e2d3cb3e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "2d1043343d8d4d1f8eb0044e1fb0e48c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_ba121eca7b8f47559d54e7a3e02cc32c", "max": 380204696, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_8ab02e1b70cf46b6b2c2a38699d64de5", "value": 380204696 } }, "3719dfed7c0840e6bc152595a3c85487": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_5534f8d2d13b4f47b0dfacb3be042b68", "placeholder": "​", "style": "IPY_MODEL_099820472ef94622bc930c7dcc27392a", "value": " 380M/380M [00:06<00:00, 34.9MB/s]" } }, "3a4ece5788cb4e4fa02ed2e22c6c7416": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_880aab255d3c44798ec400644408b089", "placeholder": "​", "style": "IPY_MODEL_f8f99acab78d454b8505aa41c5b2fdb9", "value": "model.safetensors: 100%" } }, "3c66cc92b6dc443daa79dd82e564f407": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "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": "" } }, "40e143e654424e8f96a8acc62749b56c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_8a2b1e5cd1ed41208a04ce10bd37505d", "IPY_MODEL_12ef45e353234cd187b89bd3b4adc885", "IPY_MODEL_4934e2a2e84948d993219a887e6f932a" ], "layout": "IPY_MODEL_08d5f1ac3e9e43d28bf0f2a1ca3780a9" } }, "4934e2a2e84948d993219a887e6f932a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_fbdc89bc7bca4e7b9af17480f1b51770", "placeholder": "​", "style": "IPY_MODEL_9051309263bf4dcab2a84a14085e010f", "value": " 159/159 [00:00<00:00, 4.26kB/s]" } }, "4a05f692345540a7ab688eae2ff4c635": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_3a4ece5788cb4e4fa02ed2e22c6c7416", "IPY_MODEL_2d1043343d8d4d1f8eb0044e1fb0e48c", "IPY_MODEL_a7a6f6ff7cac4c1b8dce48b4038af21f" ], "layout": "IPY_MODEL_d90a3d5595744e57b456a64dce5a6662" } }, "4ce71846fdcc4524ae55b33671370e81": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "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": "" } }, "5534f8d2d13b4f47b0dfacb3be042b68": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "5d5dc262dd2a4ec282a1125b9dbaf63a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "5e10377b33264c4a9ac9727d8b9e8e44": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "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": "" } }, "678984ff6763443b9e57ad16e65e0c14": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "789e1107230e4431a3b8fc36ccc0f370": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_aea70cfcdeb94841bc1bc616cbbd1530", "max": 380267417, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_678984ff6763443b9e57ad16e65e0c14", "value": 380267417 } }, "7bcc87bdc1de4542be9816c23ef918d8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_ddf902c6d15240f294215ca9ebcec5b9", "placeholder": "​", "style": "IPY_MODEL_3c66cc92b6dc443daa79dd82e564f407", "value": "pytorch_model.bin: 100%" } }, "82dc818a6f8049b08e9482b672cf3418": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "880aab255d3c44798ec400644408b089": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "8a2b1e5cd1ed41208a04ce10bd37505d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_25ce37ab6aaf4bf6b0dcb920e2d3cb3e", "placeholder": "​", "style": "IPY_MODEL_5e10377b33264c4a9ac9727d8b9e8e44", "value": "preprocessor_config.json: 100%" } }, "8ab02e1b70cf46b6b2c2a38699d64de5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "8de1ae82920f4af996346673195773f2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "9051309263bf4dcab2a84a14085e010f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "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": "" } }, "99248a3e649b4b4fb4a6693fc781a3ae": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_82dc818a6f8049b08e9482b672cf3418", "placeholder": "​", "style": "IPY_MODEL_e27a9962ac30459c8dae3d495c40ab8c", "value": "config.json: " } }, "a7a6f6ff7cac4c1b8dce48b4038af21f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c85639bd3d5240278f96912779017988", "placeholder": "​", "style": "IPY_MODEL_4ce71846fdcc4524ae55b33671370e81", "value": " 380M/380M [00:02<00:00, 167MB/s]" } }, "abc8a5b72f034606b3fe3b9e5d0ef8bb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "aea70cfcdeb94841bc1bc616cbbd1530": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "b63361a7078549919d77d03dbe797aa9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_99248a3e649b4b4fb4a6693fc781a3ae", "IPY_MODEL_f77ea67f2d644e949477239def77cce3", "IPY_MODEL_d5b9e59652574ae39463a62e91acbc6e" ], "layout": "IPY_MODEL_ef93f9eb83cb461f90c8b5cb85fab024" } }, "ba121eca7b8f47559d54e7a3e02cc32c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "c85639bd3d5240278f96912779017988": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "d5b9e59652574ae39463a62e91acbc6e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_abc8a5b72f034606b3fe3b9e5d0ef8bb", "placeholder": "​", "style": "IPY_MODEL_1b77a27420aa4d7d90af158073b07e0c", "value": " 1.84k/? [00:00<00:00, 33.1kB/s]" } }, "d90a3d5595744e57b456a64dce5a6662": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "dbfe5751c7a843cbac2f760232c268b2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_7bcc87bdc1de4542be9816c23ef918d8", "IPY_MODEL_789e1107230e4431a3b8fc36ccc0f370", "IPY_MODEL_3719dfed7c0840e6bc152595a3c85487" ], "layout": "IPY_MODEL_5d5dc262dd2a4ec282a1125b9dbaf63a" } }, "ddf902c6d15240f294215ca9ebcec5b9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "e27a9962ac30459c8dae3d495c40ab8c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "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": "" } }, "ef93f9eb83cb461f90c8b5cb85fab024": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "f4f3e6103c844b89b42c71709ee180ba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "f77ea67f2d644e949477239def77cce3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1622a1fc464742bdaeeec936fb81fd2c", "max": 1, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_f4f3e6103c844b89b42c71709ee180ba", "value": 1 } }, "f8f99acab78d454b8505aa41c5b2fdb9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "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": "" } }, "fbdc89bc7bca4e7b9af17480f1b51770": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "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 } }, "ffb9e0b266b04194bb777818b49b7b1e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } } } } }, "nbformat": 4, "nbformat_minor": 0 }