1.fine-tune-llama-2-for-sentiment-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "20222eb5",
   "metadata": {
    "papermill": {
     "duration": 0.008487,
     "end_time": "2023-12-14T20:26:31.615362",
     "exception": false,
     "start_time": "2023-12-14T20:26:31.606875",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Fine-tune Llama 2 for Sentiment Analysis\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "11117c49",
   "metadata": {
    "papermill": {
     "duration": 0.015876,
     "end_time": "2023-12-14T20:27:11.883631",
     "exception": false,
     "start_time": "2023-12-14T20:27:11.867755",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "353dd789",
   "metadata": {
    "papermill": {
     "duration": 0.014843,
     "end_time": "2023-12-14T20:27:11.922749",
     "exception": false,
     "start_time": "2023-12-14T20:27:11.907906",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "92b8d07c-f73f-4fab-9cf2-24d0a752ddba",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ad11dbc",
   "metadata": {
    "papermill": {
     "duration": 0.008205,
     "end_time": "2023-12-14T20:27:11.939312",
     "exception": false,
     "start_time": "2023-12-14T20:27:11.931107",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "In the following cell there are all the other imports for running the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1091ee0",
   "metadata": {
    "papermill": {
     "duration": 16.157879,
     "end_time": "2023-12-14T20:27:28.105276",
     "exception": false,
     "start_time": "2023-12-14T20:27:11.947397",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-01-31 15:01:28,554] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import bitsandbytes as bnb\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import transformers\n",
    "from datasets import Dataset\n",
    "from peft import LoraConfig, PeftConfig\n",
    "from trl import SFTTrainer\n",
    "from transformers import (AutoModelForCausalLM, \n",
    "                          AutoTokenizer, \n",
    "                          BitsAndBytesConfig, \n",
    "                          TrainingArguments, \n",
    "                          pipeline, \n",
    "                          logging)\n",
    "from sklearn.metrics import (accuracy_score, \n",
    "                             classification_report, \n",
    "                             confusion_matrix)\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "424480a1",
   "metadata": {
    "papermill": {
     "duration": 0.01583,
     "end_time": "2023-12-14T20:27:28.130304",
     "exception": false,
     "start_time": "2023-12-14T20:27:28.114474",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pytorch version 2.1.2+cu121\n"
     ]
    }
   ],
   "source": [
    "print(f\"pytorch version {torch.__version__}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "461114ef",
   "metadata": {
    "papermill": {
     "duration": 0.531064,
     "end_time": "2023-12-14T20:27:28.685819",
     "exception": false,
     "start_time": "2023-12-14T20:27:28.154755",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "filename = \"dataset/fin_sentiment/all-data.csv\"\n",
    "\n",
    "df = pd.read_csv(filename, \n",
    "                 names=[\"sentiment\", \"text\"],\n",
    "                 encoding=\"utf-8\", encoding_errors=\"replace\")\n",
    "\n",
    "X_train = list()\n",
    "X_test = list()\n",
    "for sentiment in [\"positive\", \"neutral\", \"negative\"]:\n",
    "    train, test  = train_test_split(df[df.sentiment==sentiment], \n",
    "                                    train_size=300,\n",
    "                                    test_size=300, \n",
    "                                    random_state=42)\n",
    "    X_train.append(train)\n",
    "    X_test.append(test)\n",
    "\n",
    "X_train = pd.concat(X_train).sample(frac=1, random_state=10)\n",
    "X_test = pd.concat(X_test)\n",
    "\n",
    "eval_idx = [idx for idx in df.index if idx not in list(train.index) + list(test.index)]\n",
    "X_eval = df[df.index.isin(eval_idx)]\n",
    "X_eval = (X_eval\n",
    "          .groupby('sentiment', group_keys=False)\n",
    "          .apply(lambda x: x.sample(n=50, random_state=10, replace=True)))\n",
    "X_train = X_train.reset_index(drop=True)\n",
    "\n",
    "def generate_prompt(data_point):\n",
    "    return f\"\"\"\n",
    "            Analyze the sentiment of the news headline enclosed in square brackets, \n",
    "            determine if it is positive, neutral, or negative, and return the answer as \n",
    "            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\".\n",
    "\n",
    "            [{data_point[\"text\"]}] = {data_point[\"sentiment\"]}\n",
    "            \"\"\".strip()\n",
    "\n",
    "def generate_test_prompt(data_point):\n",
    "    return f\"\"\"\n",
    "            Analyze the sentiment of the news headline enclosed in square brackets, \n",
    "            determine if it is positive, neutral, or negative, and return the answer as \n",
    "            the corresponding sentiment label \"positive\" or \"neutral\" or \"negative\".\n",
    "\n",
    "            [{data_point[\"text\"]}] = \"\"\".strip()\n",
    "\n",
    "X_train = pd.DataFrame(X_train.apply(generate_prompt, axis=1), \n",
    "                       columns=[\"text\"])\n",
    "X_eval = pd.DataFrame(X_eval.apply(generate_prompt, axis=1), \n",
    "                      columns=[\"text\"])\n",
    "\n",
    "y_true = X_test.sentiment\n",
    "X_test = pd.DataFrame(X_test.apply(generate_test_prompt, axis=1), columns=[\"text\"])\n",
    "\n",
    "train_data = Dataset.from_pandas(X_train)\n",
    "eval_data = Dataset.from_pandas(X_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c3ba256",
   "metadata": {
    "papermill": {
     "duration": 0.019935,
     "end_time": "2023-12-14T20:27:28.730772",
     "exception": false,
     "start_time": "2023-12-14T20:27:28.710837",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def evaluate(y_true, y_pred):\n",
    "    labels = ['positive', 'neutral', 'negative']\n",
    "    mapping = {'positive': 2, 'neutral': 1, 'none':1, 'negative': 0}\n",
    "    def map_func(x):\n",
    "        return mapping.get(x, 1)\n",
    "    \n",
    "    y_true = np.vectorize(map_func)(y_true)\n",
    "    y_pred = np.vectorize(map_func)(y_pred)\n",
    "    \n",
    "    # Calculate accuracy\n",
    "    accuracy = accuracy_score(y_true=y_true, y_pred=y_pred)\n",
    "    print(f'Accuracy: {accuracy:.3f}')\n",
    "    \n",
    "    # Generate accuracy report\n",
    "    unique_labels = set(y_true)  # Get unique labels\n",
    "    \n",
    "    for label in unique_labels:\n",
    "        label_indices = [i for i in range(len(y_true)) \n",
    "                         if y_true[i] == label]\n",
    "        label_y_true = [y_true[i] for i in label_indices]\n",
    "        label_y_pred = [y_pred[i] for i in label_indices]\n",
    "        accuracy = accuracy_score(label_y_true, label_y_pred)\n",
    "        print(f'Accuracy for label {label}: {accuracy:.3f}')\n",
    "        \n",
    "    # Generate classification report\n",
    "    class_report = classification_report(y_true=y_true, y_pred=y_pred)\n",
    "    print('\\nClassification Report:')\n",
    "    print(class_report)\n",
    "    \n",
    "    # Generate confusion matrix\n",
    "    conf_matrix = confusion_matrix(y_true=y_true, y_pred=y_pred, labels=[0, 1, 2])\n",
    "    print('\\nConfusion Matrix:')\n",
    "    print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "05e083b1",
   "metadata": {
    "papermill": {
     "duration": 172.58075,
     "end_time": "2023-12-14T20:30:21.335994",
     "exception": false,
     "start_time": "2023-12-14T20:27:28.755244",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abd82f5452dc4ede90584c098ad9aba5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_name = \"huggingface_models/Llama-2-7b-hf\"\n",
    "\n",
    "compute_dtype = getattr(torch, \"float16\")\n",
    "\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=compute_dtype,\n",
    "    bnb_4bit_use_double_quant=False,\n",
    ")\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_name,\n",
    "    device_map=\"auto\",\n",
    "    quantization_config=bnb_config, \n",
    ")\n",
    "\n",
    "model.config.use_cache = False\n",
    "model.config.pretraining_tp = 1\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_name, \n",
    "                                          trust_remote_code=True,\n",
    "                                         )\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"right\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f27f53d4",
   "metadata": {
    "papermill": {
     "duration": 0.018725,
     "end_time": "2023-12-14T20:30:21.381454",
     "exception": false,
     "start_time": "2023-12-14T20:30:21.362729",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def predict(test, model, tokenizer):\n",
    "    y_pred = []\n",
    "    for i in tqdm(range(len(X_test))):\n",
    "        prompt = X_test.iloc[i][\"text\"]\n",
    "        pipe = pipeline(task=\"text-generation\", \n",
    "                        model=model, \n",
    "                        tokenizer=tokenizer, \n",
    "                        max_new_tokens = 1, \n",
    "                        temperature = 0.001,\n",
    "                       )\n",
    "        result = pipe(prompt)\n",
    "        answer = result[0]['generated_text'].split(\"=\")[-1]\n",
    "        if \"positive\" in answer:\n",
    "            y_pred.append(\"positive\")\n",
    "        elif \"negative\" in answer:\n",
    "            y_pred.append(\"negative\")\n",
    "        elif \"neutral\" in answer:\n",
    "            y_pred.append(\"neutral\")\n",
    "        else:\n",
    "            y_pred.append(\"none\")\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2cca3dd6",
   "metadata": {
    "papermill": {
     "duration": 332.339467,
     "end_time": "2023-12-14T20:35:53.746473",
     "exception": false,
     "start_time": "2023-12-14T20:30:21.407006",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 900/900 [00:51<00:00, 17.57it/s]\n"
     ]
    }
   ],
   "source": [
    "y_pred = predict(test, model, tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "582c4689",
   "metadata": {
    "papermill": {
     "duration": 0.102613,
     "end_time": "2023-12-14T20:35:54.078595",
     "exception": false,
     "start_time": "2023-12-14T20:35:53.975982",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.373\n",
      "Accuracy for label 0: 0.027\n",
      "Accuracy for label 1: 0.940\n",
      "Accuracy for label 2: 0.153\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.03      0.05       300\n",
      "           1       0.34      0.94      0.50       300\n",
      "           2       0.68      0.15      0.25       300\n",
      "\n",
      "    accuracy                           0.37       900\n",
      "   macro avg       0.64      0.37      0.27       900\n",
      "weighted avg       0.64      0.37      0.27       900\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      "[[  8 287   5]\n",
      " [  1 282  17]\n",
      " [  0 254  46]]\n"
     ]
    }
   ],
   "source": [
    "evaluate(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "346dd169",
   "metadata": {
    "papermill": {
     "duration": 10.04123,
     "end_time": "2023-12-14T20:36:04.354968",
     "exception": false,
     "start_time": "2023-12-14T20:35:54.313738",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21ab39d05df841a78ca4472f3550fdbf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/900 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b28b8b094891475ab147134e14199527",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "peft_config = LoraConfig(\n",
    "    lora_alpha=16,\n",
    "    lora_dropout=0.1,\n",
    "    r=64,\n",
    "    bias=\"none\",\n",
    "    task_type=\"CAUSAL_LM\",\n",
    ")\n",
    "\n",
    "training_arguments = TrainingArguments(\n",
    "    output_dir=\"logs\",\n",
    "    num_train_epochs=3,\n",
    "    per_device_train_batch_size=1,\n",
    "    gradient_accumulation_steps=8, # 4\n",
    "    optim=\"paged_adamw_32bit\",\n",
    "    save_steps=0,\n",
    "    logging_steps=25,\n",
    "    learning_rate=2e-4,\n",
    "    weight_decay=0.001,\n",
    "    fp16=True,\n",
    "    bf16=False,\n",
    "    max_grad_norm=0.3,\n",
    "    max_steps=-1,\n",
    "    warmup_ratio=0.03,\n",
    "    group_by_length=True,\n",
    "    lr_scheduler_type=\"cosine\",\n",
    "    report_to=\"tensorboard\",\n",
    "    evaluation_strategy=\"epoch\"\n",
    ")\n",
    "\n",
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    train_dataset=train_data,\n",
    "    eval_dataset=eval_data,\n",
    "    peft_config=peft_config,\n",
    "    dataset_text_field=\"text\",\n",
    "    tokenizer=tokenizer,\n",
    "    args=training_arguments,\n",
    "    packing=False,\n",
    "    max_seq_length=1024,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b1f8e31-ec2b-44e6-a05e-2df21a47584c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train model\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4cb372cc",
   "metadata": {
    "papermill": {
     "duration": 2283.381985,
     "end_time": "2023-12-14T21:14:07.964732",
     "exception": false,
     "start_time": "2023-12-14T20:36:04.582747",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Save trained model\n",
    "# trainer.model.save_pretrained(\"trained_models/sentiment_fintech\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "543c2dd5",
   "metadata": {
    "papermill": {
     "duration": 362.05384,
     "end_time": "2023-12-14T21:20:17.087186",
     "exception": false,
     "start_time": "2023-12-14T21:14:15.033346",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████| 900/900 [01:06<00:00, 13.59it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.824\n",
      "Accuracy for label 0: 0.917\n",
      "Accuracy for label 1: 0.750\n",
      "Accuracy for label 2: 0.807\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.92      0.92       300\n",
      "           1       0.75      0.75      0.75       300\n",
      "           2       0.80      0.81      0.80       300\n",
      "\n",
      "    accuracy                           0.82       900\n",
      "   macro avg       0.83      0.82      0.82       900\n",
      "weighted avg       0.83      0.82      0.82       900\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      "[[275  24   1]\n",
      " [ 14 225  61]\n",
      " [  6  52 242]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = predict(test, model, tokenizer)\n",
    "evaluate(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a7e09e71",
   "metadata": {
    "papermill": {
     "duration": 0.168802,
     "end_time": "2023-12-14T21:20:17.727677",
     "exception": false,
     "start_time": "2023-12-14T21:20:17.558875",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "evaluation = pd.DataFrame({'text': X_test[\"text\"], \n",
    "                           'y_true':y_true, \n",
    "                           'y_pred': y_pred},\n",
    "                         )\n",
    "evaluation.to_csv(\"results/fintech_sentiment/test_predictions.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2de104d-7f55-469b-a1fd-5aff1bf9aa40",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "gpu",
   "dataSources": [
    {
     "datasetId": 622510,
     "sourceId": 1192499,
     "sourceType": "datasetVersion"
    },
    {
     "modelInstanceId": 3090,
     "sourceId": 4295,
     "sourceType": "modelInstanceVersion"
    }
   ],
   "dockerImageVersionId": 30559,
   "isGpuEnabled": true,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "venv",
   "language": "python",
   "name": "venv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.15"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 3232.423321,
   "end_time": "2023-12-14T21:20:20.693995",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2023-12-14T20:26:28.270674",
   "version": "2.4.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "05b7f4c280d34937966ff3f319ccd394": {
      "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_5be1fc45aff04d029c2c007fc64fffcf",
        "IPY_MODEL_f821d0b10f7644d2a241ab389bd056b1",
        "IPY_MODEL_9e91347fade74b7ba614b6bcf20fe236"
       ],
       "layout": "IPY_MODEL_92a3d43d82e4493c89f1ac6f9ec33641"
      }
     },
     "0bddf5c53db047528f27ae8d22d50b83": {
      "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": ""
      }
     },
     "2b31ec9c5b084aed8587ea91aba55225": {
      "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": ""
      }
     },
     "2cb8222066ef48ffb0bfee1995a28e09": {
      "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": ""
      }
     },
     "2d5fad9e604a4706bba7e73adbe40dc4": {
      "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
      }
     },
     "30c2145d0d2e4ab4a04e22b2fb0f49a6": {
      "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_dda2e193d23d4965be203fa5d74de88c",
        "IPY_MODEL_802d42114bf843498aa3658e0ef1d868",
        "IPY_MODEL_9cbf59faf6984431b4d827fad953d9d0"
       ],
       "layout": "IPY_MODEL_3ea576530d6d46a2ae82154bdd3faed1"
      }
     },
     "37aa1f593009495491576606da530a17": {
      "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
      }
     },
     "3ea576530d6d46a2ae82154bdd3faed1": {
      "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
      }
     },
     "4651330cf15d4b238b81520e1737116f": {
      "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_37aa1f593009495491576606da530a17",
       "placeholder": "​",
       "style": "IPY_MODEL_0bddf5c53db047528f27ae8d22d50b83",
       "value": " 1/1 [00:00&lt;00:00,  2.99ba/s]"
      }
     },
     "4a4fec627ffe4355892fb80347ad3361": {
      "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_6b7caa0bd00d4bdfa8cb070671a6c7fc",
        "IPY_MODEL_bcab31429669413f92cf5776cadc0645",
        "IPY_MODEL_4651330cf15d4b238b81520e1737116f"
       ],
       "layout": "IPY_MODEL_887d77b1c5824645a3fae080c6bccc49"
      }
     },
     "4afdadb0c60c47af9d5ea847bcff2ccb": {
      "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": ""
      }
     },
     "57a5570c9bd44afcb08845ec8c4d432b": {
      "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": ""
      }
     },
     "588c0f69b84d4a54bc2631c3252f1a01": {
      "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
      }
     },
     "5be1fc45aff04d029c2c007fc64fffcf": {
      "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_64077cdfb25f4934860b0ec149f3c637",
       "placeholder": "​",
       "style": "IPY_MODEL_5f9517db097c48bf9d3b2e9460061425",
       "value": "Loading checkpoint shards: 100%"
      }
     },
     "5f9517db097c48bf9d3b2e9460061425": {
      "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": ""
      }
     },
     "61a9920b4a384ca2913db7fee764ca85": {
      "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
      }
     },
     "64077cdfb25f4934860b0ec149f3c637": {
      "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
      }
     },
     "6b7caa0bd00d4bdfa8cb070671a6c7fc": {
      "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_e88c53fbf770409a8b37d40c87882242",
       "placeholder": "​",
       "style": "IPY_MODEL_e1285d334e62462f83edc701da898cd0",
       "value": "100%"
      }
     },
     "70a9ae85b59146caa2c79d9613cd09cb": {
      "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": ""
      }
     },
     "802d42114bf843498aa3658e0ef1d868": {
      "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_e6f73ebdc8b54d2cbdf83aaa8c795971",
       "max": 1,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_2cb8222066ef48ffb0bfee1995a28e09",
       "value": 1
      }
     },
     "81708567bd80428783b55c467ed328db": {
      "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": ""
      }
     },
     "887d77b1c5824645a3fae080c6bccc49": {
      "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
      }
     },
     "92a3d43d82e4493c89f1ac6f9ec33641": {
      "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
      }
     },
     "9cbf59faf6984431b4d827fad953d9d0": {
      "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_cea55922e9dc473eac00816fb101c5b6",
       "placeholder": "​",
       "style": "IPY_MODEL_4afdadb0c60c47af9d5ea847bcff2ccb",
       "value": " 1/1 [00:00&lt;00:00, 19.81ba/s]"
      }
     },
     "9e91347fade74b7ba614b6bcf20fe236": {
      "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_588c0f69b84d4a54bc2631c3252f1a01",
       "placeholder": "​",
       "style": "IPY_MODEL_81708567bd80428783b55c467ed328db",
       "value": " 2/2 [02:46&lt;00:00, 76.31s/it]"
      }
     },
     "bcab31429669413f92cf5776cadc0645": {
      "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_d150a41a9a4745b793b5bba499cf462e",
       "max": 1,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_2b31ec9c5b084aed8587ea91aba55225",
       "value": 1
      }
     },
     "cea55922e9dc473eac00816fb101c5b6": {
      "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
      }
     },
     "d150a41a9a4745b793b5bba499cf462e": {
      "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
      }
     },
     "dda2e193d23d4965be203fa5d74de88c": {
      "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_2d5fad9e604a4706bba7e73adbe40dc4",
       "placeholder": "​",
       "style": "IPY_MODEL_70a9ae85b59146caa2c79d9613cd09cb",
       "value": "100%"
      }
     },
     "e1285d334e62462f83edc701da898cd0": {
      "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": ""
      }
     },
     "e6f73ebdc8b54d2cbdf83aaa8c795971": {
      "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
      }
     },
     "e88c53fbf770409a8b37d40c87882242": {
      "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
      }
     },
     "f821d0b10f7644d2a241ab389bd056b1": {
      "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_61a9920b4a384ca2913db7fee764ca85",
       "max": 2,
       "min": 0,
       "orientation": "horizontal",
       "style": "IPY_MODEL_57a5570c9bd44afcb08845ec8c4d432b",
       "value": 2
      }
     }
    },
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}