non parallel data model.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"non parallel data model.ipynb","provenance":[],"collapsed_sections":[],"machine_shape":"hm"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["This code is taken from [here](https://blog.paperspace.com/seq-to-seq-attention-mechanism-keras/) and is modified to solve our task!"],"metadata":{"id":"wIKPGZdbnq_E"}},{"cell_type":"code","source":["# Please note that this code just follows the provided video\n","# Mount data from drive\n","from google.colab import drive\n","drive.mount('/content/drive', force_remount=True)\n","\n","# Folder name\n","folderName = 'CS685 Project/Sanity Check'\n","assert folderName is not None, \"[Error] Please enter folder name.\"\n","\n","\n","# Load python files from our folder\n","import sys\n","sys.path.append('/content/drive/My Drive/{}'.format(folderName))\n","\n","%cd /content/drive/My\\ Drive/$folderName/"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Gh2hARImXhen","executionInfo":{"status":"ok","timestamp":1652389971442,"user_tz":240,"elapsed":13797,"user":{"displayName":"Jiachang Situ","userId":"10829431653516566851"}},"outputId":"fc8727f6-67eb-42f3-c6ae-621b071e56dc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","/content/drive/.shortcut-targets-by-id/176_V3hUNBvwdqw9WeYJjHSQNn8fY7Q2K/CS685 Project/Sanity Check\n"]}]},{"cell_type":"code","source":["!pip install sentencepiece\n","!pip install transformers\n","# !pip install torch\n","!pip install rich[jupyter]\n","\n","# Importing libraries\n","import os\n","import numpy as np\n","import pandas as pd\n","import torch\n","import torch.nn.functional as F\n","from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler\n","import os\n","\n","# Importing the T5 modules from huggingface/transformers\n","from transformers import T5Tokenizer, T5ForConditionalGeneration\n","\n","# rich: for a better display on terminal\n","from rich.table import Column, Table\n","from rich import box\n","from rich.console import Console"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"h4sqhklgxJVJ","executionInfo":{"status":"ok","timestamp":1652389997358,"user_tz":240,"elapsed":25923,"user":{"displayName":"Jiachang Situ","userId":"10829431653516566851"}},"outputId":"d9d739e3-d6e8-4659-86fa-94fba6264bd6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting sentencepiece\n","  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n","\u001b[?25l\r\u001b[K     |▎                               | 10 kB 23.8 MB/s eta 0:00:01\r\u001b[K     |▌                               | 20 kB 10.3 MB/s eta 0:00:01\r\u001b[K     |▉                               | 30 kB 8.9 MB/s eta 0:00:01\r\u001b[K     |█                     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nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0.0,>=7.5.1->rich[jupyter]) (5.0.0)\n","Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0.0,>=7.5.1->rich[jupyter]) (0.5.1)\n","Installing collected packages: commonmark, rich\n","Successfully installed commonmark-0.9.1 rich-12.4.1\n"]}]},{"cell_type":"markdown","source":["Store Paraphrases as X and Shakespeare sentences as Z\n","\n","Resulting dataset: (X, Z)\n","\n","Task for dataset: Given Paraphrase X generate original sentence Z\n"],"metadata":{"id":"_23S_vKvtf2V"}},{"cell_type":"markdown","source":["Fine-tune T5 for this task"],"metadata":{"id":"H8H-HMYLsiOh"}},{"cell_type":"code","source":["# define a rich console logger\n","console = Console(record=True)\n","\n","# to display dataframe in ASCII format\n","def display_df(df):\n","    \"\"\"display dataframe in ASCII format\"\"\"\n","\n","    console = Console()\n","    table = Table(\n","        Column(\"source_text\", justify=\"center\"),\n","        Column(\"target_text\", justify=\"center\"),\n","        title=\"Sample Data\",\n","        pad_edge=False,\n","        box=box.ASCII,\n","    )\n","\n","    for i, row in enumerate(df.values.tolist()):\n","        table.add_row(row[0], row[1])\n","\n","    console.print(table)\n","\n","# training logger to log training progress\n","training_logger = Table(\n","    Column(\"Epoch\", justify=\"center\"),\n","    Column(\"Steps\", justify=\"center\"),\n","    Column(\"Loss\", justify=\"center\"),\n","    title=\"Training Status\",\n","    pad_edge=False,\n","    box=box.ASCII,\n",")\n","\n","# Setting up the device for GPU usage\n","from torch import cuda\n","device = 'cuda' if cuda.is_available() else 'cpu'\n"],"metadata":{"id":"ljwGeYxHZMPm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class CustomDataSetClass(Dataset):\n","    \"\"\"\n","    Creating a custom dataset for reading the dataset and\n","    loading it into the dataloader to pass it to the\n","    neural network for finetuning the model\n","\n","    \"\"\"\n","\n","    def __init__(\n","        self, dataframe, tokenizer, source_len, target_len, source_text, target_text\n","    ):\n","        \"\"\"\n","        Initializes a Dataset class\n","\n","        Args:\n","            dataframe (pandas.DataFrame): Input dataframe\n","            tokenizer (transformers.tokenizer): Transformers tokenizer\n","            source_len (int): Max length of source text\n","            target_len (int): Max length of target text\n","            source_text (str): column name of source text\n","            target_text (str): column name of target text\n","        \"\"\"\n","        self.tokenizer = tokenizer\n","        self.data = dataframe\n","        self.source_len = source_len\n","        self.summ_len = target_len\n","        self.target_text = self.data[target_text]\n","        self.source_text = self.data[source_text]\n","\n","    def __len__(self):\n","        \"\"\"returns the length of dataframe\"\"\"\n","\n","        return len(self.target_text)\n","\n","    def __getitem__(self, index):\n","        \"\"\"return the input ids, attention masks and target ids\"\"\"\n","\n","        source_text = str(self.source_text[index])\n","        target_text = str(self.target_text[index])\n","\n","        # cleaning data so as to ensure data is in string type\n","        source_text = \" \".join(source_text.split())\n","        target_text = \" \".join(target_text.split())\n","\n","        source = self.tokenizer.batch_encode_plus(\n","            [source_text],\n","            max_length=self.source_len,\n","            pad_to_max_length=True,\n","            truncation=True,\n","            padding=\"max_length\",\n","            return_tensors=\"pt\",\n","        )\n","        target = self.tokenizer.batch_encode_plus(\n","            [target_text],\n","            max_length=self.summ_len,\n","            pad_to_max_length=True,\n","            truncation=True,\n","            padding=\"max_length\",\n","            return_tensors=\"pt\",\n","        )\n","\n","        source_ids = source[\"input_ids\"].squeeze()\n","        source_mask = source[\"attention_mask\"].squeeze()\n","        target_ids = target[\"input_ids\"].squeeze()\n","        target_mask = target[\"attention_mask\"].squeeze()\n","\n","        return {\n","            \"source_ids\": source_ids.to(dtype=torch.long),\n","            \"source_mask\": source_mask.to(dtype=torch.long),\n","            \"target_ids\": target_ids.to(dtype=torch.long),\n","            \"target_ids_y\": target_ids.to(dtype=torch.long),\n","        }"],"metadata":{"id":"Ea0dYMcNbgO8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def train(epoch, tokenizer, model, device, loader, optimizer):\n","\n","    \"\"\"\n","    Function to be called for training with the parameters passed from main function\n","\n","    \"\"\"\n","\n","    model.train()\n","    for _, data in enumerate(loader, 0):\n","        y = data[\"target_ids\"].to(device, dtype=torch.long)\n","        y_ids = y[:, :-1].contiguous()\n","        lm_labels = y[:, 1:].clone().detach()\n","        lm_labels[y[:, 1:] == tokenizer.pad_token_id] = -100\n","        ids = data[\"source_ids\"].to(device, dtype=torch.long)\n","        mask = data[\"source_mask\"].to(device, dtype=torch.long)\n","\n","        outputs = model(\n","            input_ids=ids,\n","            attention_mask=mask,\n","            decoder_input_ids=y_ids,\n","            labels=lm_labels,\n","        )\n","        loss = outputs[0]\n","\n","        if _ % 330 == 0:\n","            training_logger.add_row(str(epoch), str(_), str(loss))\n","            console.print(training_logger)\n","\n","        optimizer.zero_grad()\n","        loss.backward()\n","        optimizer.step()"],"metadata":{"id":"SVEEx2xhbkUq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def validate(epoch, tokenizer, model, device, loader):\n","\n","  \"\"\"\n","  Function to evaluate model for predictions\n","\n","  \"\"\"\n","  model.eval()\n","  predictions = []\n","  actuals = []\n","  with torch.no_grad():\n","      for _, data in enumerate(loader, 0):\n","          y = data['target_ids'].to(device, dtype = torch.long)\n","          ids = data['source_ids'].to(device, dtype = torch.long)\n","          mask = data['source_mask'].to(device, dtype = torch.long)\n","\n","          generated_ids = model.generate(\n","              input_ids = ids,\n","              attention_mask = mask, \n","              max_length=150, \n","              num_beams=2,\n","              repetition_penalty=2.5, \n","              length_penalty=1.0, \n","              early_stopping=True\n","              )\n","          preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\n","          target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True)for t in y]\n","          if _%10==0:\n","              console.print(f'Completed {_}')\n","\n","          predictions.extend(preds)\n","          actuals.extend(target)\n","  return predictions, actuals"],"metadata":{"id":"05VjQyLWborm"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def T5Trainer(\n","    dataframe, source_text, target_text, model_params, output_dir=\"./outputs/\"\n","):\n","\n","    \"\"\"\n","    T5 trainer\n","\n","    \"\"\"\n","\n","    # Set random seeds and deterministic pytorch for reproducibility\n","    torch.manual_seed(model_params[\"SEED\"])  # pytorch random seed\n","    np.random.seed(model_params[\"SEED\"])  # numpy random seed\n","    torch.backends.cudnn.deterministic = True\n","\n","    # logging\n","    console.log(f\"\"\"[Model]: Loading {model_params[\"MODEL\"]}...\\n\"\"\")\n","\n","    # tokenzier for encoding the text\n","    tokenizer = T5Tokenizer.from_pretrained(model_params[\"MODEL\"])\n","\n","    # Defining the model. We are using t5-base model and added a Language model layer on top for generation of Summary.\n","    # Further this model is sent to device (GPU/TPU) for using the hardware.\n","    model = T5ForConditionalGeneration.from_pretrained(model_params[\"MODEL\"])\n","    model = model.to(device)\n","\n","    # logging\n","    console.log(f\"[Data]: Reading data...\\n\")\n","\n","    # Importing the raw dataset\n","    dataframe = dataframe[[source_text, target_text]]\n","    display_df(dataframe.head(2))\n","\n","    # Creation of Dataset and Dataloader\n","    # Defining the train size. So 80% of the data will be used for training and the rest for validation.\n","    train_size = 0.8\n","    train_dataset = dataframe.sample(frac=train_size, random_state=model_params[\"SEED\"])\n","    val_dataset = dataframe.drop(train_dataset.index).reset_index(drop=True)\n","    train_dataset = train_dataset.reset_index(drop=True)\n","\n","    console.print(f\"FULL Dataset: {dataframe.shape}\")\n","    console.print(f\"TRAIN Dataset: {train_dataset.shape}\")\n","    console.print(f\"TEST Dataset: {val_dataset.shape}\\n\")\n","\n","    # Creating the Training and Validation dataset for further creation of Dataloader\n","    training_set = CustomDataSetClass(\n","        train_dataset,\n","        tokenizer,\n","        model_params[\"MAX_SOURCE_TEXT_LENGTH\"],\n","        model_params[\"MAX_TARGET_TEXT_LENGTH\"],\n","        source_text,\n","        target_text,\n","    )\n","    val_set = CustomDataSetClass(\n","        val_dataset,\n","        tokenizer,\n","        model_params[\"MAX_SOURCE_TEXT_LENGTH\"],\n","        model_params[\"MAX_TARGET_TEXT_LENGTH\"],\n","        source_text,\n","        target_text,\n","    )\n","\n","    # Defining the parameters for creation of dataloaders\n","    train_params = {\n","        \"batch_size\": model_params[\"TRAIN_BATCH_SIZE\"],\n","        \"shuffle\": True,\n","        \"num_workers\": 0,\n","    }\n","\n","    val_params = {\n","        \"batch_size\": model_params[\"VALID_BATCH_SIZE\"],\n","        \"shuffle\": False,\n","        \"num_workers\": 0,\n","    }\n","\n","    # Creation of Dataloaders for testing and validation. This will be used down for training and validation stage for the model.\n","    training_loader = DataLoader(training_set, **train_params)\n","    val_loader = DataLoader(val_set, **val_params)\n","\n","    # Defining the optimizer that will be used to tune the weights of the network in the training session.\n","    optimizer = torch.optim.Adam(\n","        params=model.parameters(), lr=model_params[\"LEARNING_RATE\"]\n","    )\n","\n","    # Training loop\n","    console.log(f\"[Initiating Fine Tuning]...\\n\")\n","\n","    for epoch in range(model_params[\"TRAIN_EPOCHS\"]):\n","        train(epoch, tokenizer, model, device, training_loader, optimizer)\n","\n","    console.log(f\"[Saving Model]...\\n\")\n","    # Saving the model after training\n","    path = os.path.join(output_dir, \"model_files\")\n","    model.save_pretrained(path)\n","    tokenizer.save_pretrained(path)\n","\n","    # evaluating test dataset\n","    console.log(f\"[Initiating Validation]...\\n\")\n","    for epoch in range(model_params[\"VAL_EPOCHS\"]):\n","        predictions, actuals = validate(epoch, tokenizer, model, device, val_loader)\n","        final_df = pd.DataFrame({\"Generated Text\": predictions, \"Actual Text\": actuals})\n","        final_df.to_csv(os.path.join(output_dir, \"predictions.csv\"))\n","\n","    console.save_text(os.path.join(output_dir, \"logs.txt\"))\n","\n","    console.log(f\"[Validation Completed.]\\n\")\n","    console.print(\n","        f\"\"\"[Model] Model saved @ {os.path.join(output_dir, \"model_files\")}\\n\"\"\"\n","    )\n","    console.print(\n","        f\"\"\"[Validation] Generation on Validation data saved @ {os.path.join(output_dir,'predictions.csv')}\\n\"\"\"\n","    )\n","    console.print(f\"\"\"[Logs] Logs saved @ {os.path.join(output_dir,'logs.txt')}\\n\"\"\")"],"metadata":{"id":"Od9ISd1gbtG7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# let's define model parameters specific to T5\n","model_params = {\n","    \"MODEL\": \"t5-base\",  # model_type: t5-base/t5-large\n","    \"TRAIN_BATCH_SIZE\": 8,  # training batch size\n","    \"VALID_BATCH_SIZE\": 8,  # validation batch size\n","    \"TRAIN_EPOCHS\": 5,  # number of training epochs\n","    \"VAL_EPOCHS\": 1,  # number of validation epochs\n","    \"LEARNING_RATE\": 3e-4,  # learning rate\n","    \"MAX_SOURCE_TEXT_LENGTH\": 255,  # max length of source text\n","    \"MAX_TARGET_TEXT_LENGTH\": 255,  # max length of target text\n","    \"SEED\": 42,  # set seed for reproducibility\n","}"],"metadata":{"id":"6NO_utmfb3ws"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["path = \"data/pseudo_parallel.csv\"\n","df = pd.read_csv(path)\n","training_df = df.sample(frac = 0.9)\n","testing_df = df.drop(training_df.index, axis=0)\n","testing_df.size #shape (1839, 3)\n"],"metadata":{"id":"Qp3j8aBkcWyc","executionInfo":{"status":"ok","timestamp":1652389997987,"user_tz":240,"elapsed":191,"user":{"displayName":"Jiachang Situ","userId":"10829431653516566851"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"c239fea9-c60e-4891-f71a-03d4a0355b2d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["5517"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["# T5 accepts prefix of the task to be performed:\n","# Since we are summarizing, let's add summarize to source text as a prefix\n","model_df = training_df.copy()\n","# model_df[\"input\"] = \"Shakespeare it: \" + model_df[\"input\"]\n","\n","\n","T5Trainer(\n","    dataframe=model_df,\n","    source_text=\"input\",\n","    target_text=\"target\",\n","    model_params=model_params,\n","    output_dir=\"pseudo_parallel_removed_shakespeare\",\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"_NnhUjQEb4fl","outputId":"4d12f995-89a4-49fa-c7b7-b34cc57565a5","executionInfo":{"status":"ok","timestamp":1652387362298,"user_tz":240,"elapsed":5062459,"user":{"displayName":"Jiachang Situ","userId":"10829431653516566851"}}},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[2;36m[19:04:58]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1m[\u001b[0mModel\u001b[1m]\u001b[0m: Loading t5-base\u001b[33m...\u001b[0m                      \u001b[2m<ipython-input-7-60af46234dab>\u001b[0m\u001b[2m:\u001b[0m\u001b[2m16\u001b[0m\n","\u001b[2;36m           \u001b[0m                                                 \u001b[2m                                 \u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[19:04:58] </span><span style=\"font-weight: bold\">[</span>Model<span style=\"font-weight: bold\">]</span>: Loading t5-base<span style=\"color: #808000; text-decoration-color: #808000\">...</span>                      <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">&lt;ipython-input-7-60af46234dab&gt;:16</span>\n","<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                 </span>\n","</pre>\n"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/transformers/models/t5/tokenization_t5.py:169: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n","For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n","- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n","- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n","- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n","  FutureWarning,\n"]},{"output_type":"display_data","data":{"text/plain":["\u001b[2;36m[19:05:12]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1m[\u001b[0mData\u001b[1m]\u001b[0m: Reading data\u001b[33m...\u001b[0m                          \u001b[2m<ipython-input-7-60af46234dab>\u001b[0m\u001b[2m:\u001b[0m\u001b[2m27\u001b[0m\n","\u001b[2;36m           \u001b[0m                                                 \u001b[2m                                 \u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[19:05:12] </span><span style=\"font-weight: bold\">[</span>Data<span style=\"font-weight: bold\">]</span>: Reading data<span style=\"color: #808000; text-decoration-color: #808000\">...</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">&lt;ipython-input-7-60af46234dab&gt;:27</span>\n","<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                 </span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                                      Sample Data                                      \u001b[0m\n","+-------------------------------------------------------------------------------------+\n","|\u001b[1m            source_text            \u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                  target_text                  \u001b[0m|\n","|------------------------------------+------------------------------------------------|\n","|How long shall her sweet life last? | But how long fairly shall her sweet life last ?|\n","|          There is peace.           |                     Peace !                    |\n","+-------------------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                                      Sample Data                                      </span>\n","+-------------------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">            source_text             </span>|<span style=\"font-weight: bold\">                   target_text                  </span>|\n","|------------------------------------+------------------------------------------------|\n","|How long shall her sweet life last? | But how long fairly shall her sweet life last ?|\n","|          There is peace.           |                     Peace !                    |\n","+-------------------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["FULL Dataset: \u001b[1m(\u001b[0m\u001b[1;36m16556\u001b[0m, \u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">FULL Dataset: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16556</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">)</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["TRAIN Dataset: \u001b[1m(\u001b[0m\u001b[1;36m13245\u001b[0m, \u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">TRAIN Dataset: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">13245</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">)</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["TEST Dataset: \u001b[1m(\u001b[0m\u001b[1;36m3311\u001b[0m, \u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m\n","\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">TEST Dataset: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3311</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">)</span>\n","\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1m[\u001b[0mInitiating Fine Tuning\u001b[1m]\u001b[0m\u001b[33m...\u001b[0m                      \u001b[2m<ipython-input-7-60af46234dab>\u001b[0m\u001b[2m:\u001b[0m\u001b[2m85\u001b[0m\n","\u001b[2;36m           \u001b[0m                                                 \u001b[2m                                 \u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"font-weight: bold\">[</span>Initiating Fine Tuning<span style=\"font-weight: bold\">]</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>                      <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">&lt;ipython-input-7-60af46234dab&gt;:85</span>\n","<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                 </span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |   0   | tensor(0.5353, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |   0   | tensor(0.5353, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |   0   | tensor(0.5353, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |  330  | tensor(0.5485, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |   0   | tensor(0.5353, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |  330  | tensor(0.5485, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |   0   | tensor(0.5353, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |  330  | tensor(0.5485, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |  660  | tensor(0.6786, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |   0   | tensor(0.5353, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |  330  | tensor(0.5485, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |  660  | tensor(0.6786, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |   0   | tensor(0.5353, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |  330  | tensor(0.5485, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |  660  | tensor(0.6786, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |  990  | tensor(0.2498, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |   0   | tensor(0.5353, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |  330  | tensor(0.5485, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |  660  | tensor(0.6786, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |  990  | tensor(0.2498, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |   0   | tensor(0.5353, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |  330  | tensor(0.5485, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |  660  | tensor(0.6786, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |  990  | tensor(0.2498, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   | 1320  | tensor(0.8233, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |   0   | tensor(0.5353, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |  330  | tensor(0.5485, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |  660  | tensor(0.6786, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |  990  | tensor(0.2498, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   | 1320  | tensor(0.8233, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                               Training Status                               \u001b[0m\n","+---------------------------------------------------------------------------+\n","|\u001b[1mEpoch\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1mSteps\u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                           Loss                            \u001b[0m|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |   0   | tensor(0.5353, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |  330  | tensor(0.5485, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |  660  | tensor(0.6786, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   |  990  | tensor(0.2498, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   | 1320  | tensor(0.8233, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","|  4   | 1650  | tensor(0.6279, device='cuda:0', grad_fn=<NllLossBackward0>)|\n","+---------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                               Training Status                               </span>\n","+---------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">Epoch </span>|<span style=\"font-weight: bold\"> Steps </span>|<span style=\"font-weight: bold\">                            Loss                            </span>|\n","|------+-------+------------------------------------------------------------|\n","|  0   |   0   | tensor(7.0470, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  330  | tensor(1.5200, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  660  | tensor(1.4367, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   |  990  | tensor(1.5751, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1320  | tensor(1.4400, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  0   | 1650  | tensor(1.1554, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |   0   | tensor(0.8746, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  330  | tensor(1.0480, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  660  | tensor(0.7338, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   |  990  | tensor(1.8478, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1320  | tensor(1.2358, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  1   | 1650  | tensor(1.1238, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |   0   | tensor(0.4572, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  330  | tensor(1.7261, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  660  | tensor(0.7730, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   |  990  | tensor(1.0654, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1320  | tensor(1.1041, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  2   | 1650  | tensor(0.7717, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |   0   | tensor(0.7136, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  330  | tensor(0.7750, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  660  | tensor(1.1131, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   |  990  | tensor(0.4417, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1320  | tensor(0.6633, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  3   | 1650  | tensor(1.4967, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |   0   | tensor(0.5353, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |  330  | tensor(0.5485, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |  660  | tensor(0.6786, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   |  990  | tensor(0.2498, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   | 1320  | tensor(0.8233, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","|  4   | 1650  | tensor(0.6279, device='cuda:0', grad_fn=&lt;NllLossBackward0&gt;)|\n","+---------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[2;36m[20:20:39]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1m[\u001b[0mSaving Model\u001b[1m]\u001b[0m\u001b[33m...\u001b[0m                                \u001b[2m<ipython-input-7-60af46234dab>\u001b[0m\u001b[2m:\u001b[0m\u001b[2m90\u001b[0m\n","\u001b[2;36m           \u001b[0m                                                 \u001b[2m                                 \u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:20:39] </span><span style=\"font-weight: bold\">[</span>Saving Model<span style=\"font-weight: bold\">]</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>                                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">&lt;ipython-input-7-60af46234dab&gt;:90</span>\n","<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                 </span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[2;36m[20:20:43]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1m[\u001b[0mInitiating Validation\u001b[1m]\u001b[0m\u001b[33m...\u001b[0m                       \u001b[2m<ipython-input-7-60af46234dab>\u001b[0m\u001b[2m:\u001b[0m\u001b[2m97\u001b[0m\n","\u001b[2;36m           \u001b[0m                                                 \u001b[2m                                 \u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:20:43] </span><span style=\"font-weight: bold\">[</span>Initiating Validation<span style=\"font-weight: bold\">]</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>                       <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">&lt;ipython-input-7-60af46234dab&gt;:97</span>\n","<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                 </span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m0\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m10\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier 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style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:29:21] </span><span style=\"font-weight: bold\">[</span>Validation Completed.<span style=\"font-weight: bold\">]</span>                         <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">&lt;ipython-input-7-60af46234dab&gt;:105</span>\n","<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                  </span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m[\u001b[0mModel\u001b[1m]\u001b[0m Model saved @ pseudo_parallel_removed_shakespeare/model_files\n","\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">[</span>Model<span style=\"font-weight: bold\">]</span> Model saved @ pseudo_parallel_removed_shakespeare/model_files\n","\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m[\u001b[0mValidation\u001b[1m]\u001b[0m Generation on Validation data saved @ \n","pseudo_parallel_removed_shakespeare/predictions.csv\n","\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">[</span>Validation<span style=\"font-weight: bold\">]</span> Generation on Validation data saved @ \n","pseudo_parallel_removed_shakespeare/predictions.csv\n","\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m[\u001b[0mLogs\u001b[1m]\u001b[0m Logs saved @ pseudo_parallel_removed_shakespeare/logs.txt\n","\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">[</span>Logs<span style=\"font-weight: bold\">]</span> Logs saved @ pseudo_parallel_removed_shakespeare/logs.txt\n","\n","</pre>\n"]},"metadata":{}}]},{"cell_type":"code","source":["def T5Generator(\n","    dataframe, source_text, target_text, model_params, input_dir=\"./outputs/\", output_dir=\"./outputs/\"\n","):\n","\n","    \"\"\"\n","    T5 trainer\n","\n","    \"\"\"\n","\n","    # Set random seeds and deterministic pytorch for reproducibility\n","    torch.manual_seed(model_params[\"SEED\"])  # pytorch random seed\n","    np.random.seed(model_params[\"SEED\"])  # numpy random seed\n","    torch.backends.cudnn.deterministic = True\n","\n","    # Loading the model\n","    path = os.path.join(input_dir, \"model_files\")\n","\n","    # logging\n","    console.log(f\"\"\"[Model]: Loading {model_params[\"MODEL\"]}...\\n\"\"\")\n","\n","    # tokenzier for encoding the text\n","    tokenizer = T5Tokenizer.from_pretrained(path)\n","\n","    # Defining the model. We are using t5-base model and added a Language model layer on top for generation of Summary.\n","    # Further this model is sent to device (GPU/TPU) for using the hardware.\n","    model = T5ForConditionalGeneration.from_pretrained(path)\n","    model = model.to(device)\n","\n","    # logging\n","    console.log(f\"[Data]: Reading data...\\n\")\n","\n","    # Importing the raw dataset\n","    dataframe = dataframe[[source_text, target_text]]\n","    display_df(dataframe.head(2))\n","\n","    # Creation of Dataset and Dataloader\n","    # Defining the train size. So 80% of the data will be used for training and the rest for validation.\n","    # train_size = 0.8\n","    # train_dataset = dataframe.sample(frac=train_size, random_state=model_params[\"SEED\"])\n","    gen_dataset = dataframe.reset_index(drop=True)\n","    # train_dataset = train_dataset.reset_index(drop=True)\n","\n","    console.print(f\"FULL Dataset: {dataframe.shape}\")\n","    console.print(f\"Gen Dataset: {gen_dataset.shape}\\n\")\n","\n","    gen_set = CustomDataSetClass(\n","        gen_dataset,\n","        tokenizer,\n","        model_params[\"MAX_SOURCE_TEXT_LENGTH\"],\n","        model_params[\"MAX_TARGET_TEXT_LENGTH\"],\n","        source_text,\n","        target_text,\n","    )\n","\n","    # Defining the parameters for creation of dataloaders\n","    train_params = {\n","        \"batch_size\": model_params[\"TRAIN_BATCH_SIZE\"],\n","        \"shuffle\": True,\n","        \"num_workers\": 0,\n","    }\n","\n","    gem_params = {\n","        \"batch_size\": model_params[\"VALID_BATCH_SIZE\"],\n","        \"shuffle\": False,\n","        \"num_workers\": 0,\n","    }\n","\n","    # Creation of Dataloaders for testing and validation. This will be used down for training and validation stage for the model.\n","    gen_loader = DataLoader(gen_set, **gem_params)\n","\n","    # evaluating test dataset\n","    console.log(f\"[Initiating Validation]...\\n\")\n","    for epoch in range(model_params[\"VAL_EPOCHS\"]):\n","        predictions, actuals = validate(epoch, tokenizer, model, device, gen_loader)\n","        final_df = pd.DataFrame({\"Generated Text\": predictions})\n","        final_df.to_csv(os.path.join(output_dir, \"gen_predictions.csv\"))\n","\n","    console.save_text(os.path.join(output_dir, \"gen_logs.txt\"))"],"metadata":{"id":"AKBj0XcyYjxE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# gen_df = testing_df.copy()\n","# gen_df[\"input\"] = \"Shakespear it: \" + gen_df[\"input\"]\n","# gen_df.head(1)"],"metadata":{"id":"Mh9jlwRaxCjL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# T5 accepts prefix of the task to be performed:\n","# Since we are summarizing, let's add summarize to source text as a prefix\n","gen_df = testing_df.copy()\n","# gen_df[\"input\"] = \"Shakespear it: \" + gen_df[\"input\"]\n","\n","T5Generator(\n","    dataframe=gen_df,\n","    source_text=\"input\",\n","    target_text=\"target\",\n","    model_params=model_params,\n","    input_dir=\"pseudo_parallel_removed_shakespeare\",\n","    output_dir=\"pseudo_parallel_removed_shakespeare\",\n",")"],"metadata":{"id":"HUZHEbIGa41u","colab":{"base_uri":"https://localhost:8080/","height":641},"executionInfo":{"status":"ok","timestamp":1652387645914,"user_tz":240,"elapsed":283647,"user":{"displayName":"Jiachang Situ","userId":"10829431653516566851"}},"outputId":"2adf4d4c-4702-4c23-ac4f-55e38d721996"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1m[\u001b[0mModel\u001b[1m]\u001b[0m: Loading t5-base\u001b[33m...\u001b[0m                     \u001b[2m<ipython-input-11-283f28e2abf0>\u001b[0m\u001b[2m:\u001b[0m\u001b[2m19\u001b[0m\n","\u001b[2;36m           \u001b[0m                                                \u001b[2m                                  \u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"font-weight: bold\">[</span>Model<span style=\"font-weight: bold\">]</span>: Loading t5-base<span style=\"color: #808000; text-decoration-color: #808000\">...</span>                     <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">&lt;ipython-input-11-283f28e2abf0&gt;:19</span>\n","<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                  </span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[2;36m[20:29:24]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1m[\u001b[0mData\u001b[1m]\u001b[0m: Reading data\u001b[33m...\u001b[0m                         \u001b[2m<ipython-input-11-283f28e2abf0>\u001b[0m\u001b[2m:\u001b[0m\u001b[2m30\u001b[0m\n","\u001b[2;36m           \u001b[0m                                                \u001b[2m                                  \u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[20:29:24] </span><span style=\"font-weight: bold\">[</span>Data<span style=\"font-weight: bold\">]</span>: Reading data<span style=\"color: #808000; text-decoration-color: #808000\">...</span>                         <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">&lt;ipython-input-11-283f28e2abf0&gt;:30</span>\n","<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                  </span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                                    Sample Data                                     \u001b[0m\n","+----------------------------------------------------------------------------------+\n","|\u001b[1m          source_text          \u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                  target_text                   \u001b[0m|\n","|--------------------------------+-------------------------------------------------|\n","|I have a plan to speak to thee. | I have a mind to strike thee ere thou speak'st .|\n","|      He is well, Madame.       |               Madam , he's well .               |\n","+----------------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                                    Sample Data                                     </span>\n","+----------------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">          source_text           </span>|<span style=\"font-weight: bold\">                   target_text                   </span>|\n","|--------------------------------+-------------------------------------------------|\n","|I have a plan to speak to thee. | I have a mind to strike thee ere thou speak'st .|\n","|      He is well, Madame.       |               Madam , he's well .               |\n","+----------------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["FULL Dataset: \u001b[1m(\u001b[0m\u001b[1;36m1839\u001b[0m, \u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">FULL Dataset: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1839</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">)</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Gen Dataset: \u001b[1m(\u001b[0m\u001b[1;36m1839\u001b[0m, \u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m\n","\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Gen Dataset: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1839</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">)</span>\n","\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1m[\u001b[0mInitiating Validation\u001b[1m]\u001b[0m\u001b[33m...\u001b[0m                      \u001b[2m<ipython-input-11-283f28e2abf0>\u001b[0m\u001b[2m:\u001b[0m\u001b[2m72\u001b[0m\n","\u001b[2;36m           \u001b[0m                                                \u001b[2m                                  \u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"font-weight: bold\">[</span>Initiating Validation<span style=\"font-weight: bold\">]</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>                      <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">&lt;ipython-input-11-283f28e2abf0&gt;:72</span>\n","<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                  </span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m0\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m10\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; 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text-decoration-color: #008080; font-weight: bold\">220</span>\n","</pre>\n"]},"metadata":{}}]},{"cell_type":"code","source":["path = \"data/dev_1.csv\"\n","testing_df = pd.read_csv(path)\n","testing_df.head(1)\n","\n","gen_df = testing_df.copy()\n","gen_df[\"Shakespearean\"] = gen_df[\"text\"]\n","gen_df[\"English\"] =  gen_df[\"text\"]\n","\n","T5Generator(\n","    dataframe=gen_df,\n","    source_text=\"English\",\n","    target_text=\"Shakespearean\",\n","    model_params=model_params,\n","    input_dir=\"pseudo_parallel_removed_shakespeare\",\n","    output_dir=\"T5_2-5-epochs-test1-outputs-removed-shakespeare/001\",\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":545},"id":"IP-gN1mH-STo","executionInfo":{"status":"ok","timestamp":1652390345034,"user_tz":240,"elapsed":237801,"user":{"displayName":"Jiachang Situ","userId":"10829431653516566851"}},"outputId":"5812040f-f1cf-464b-be99-46d549525c4e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[2;36m[21:15:06]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1m[\u001b[0mModel\u001b[1m]\u001b[0m: Loading t5-base\u001b[33m...\u001b[0m                      \u001b[2m<ipython-input-9-283f28e2abf0>\u001b[0m\u001b[2m:\u001b[0m\u001b[2m19\u001b[0m\n","\u001b[2;36m           \u001b[0m                                                 \u001b[2m                                 \u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[21:15:06] </span><span style=\"font-weight: bold\">[</span>Model<span style=\"font-weight: bold\">]</span>: Loading t5-base<span style=\"color: #808000; text-decoration-color: #808000\">...</span>                      <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">&lt;ipython-input-9-283f28e2abf0&gt;:19</span>\n","<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                 </span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[2;36m[21:15:10]\u001b[0m\u001b[2;36m \u001b[0m\u001b[1m[\u001b[0mData\u001b[1m]\u001b[0m: Reading data\u001b[33m...\u001b[0m                          \u001b[2m<ipython-input-9-283f28e2abf0>\u001b[0m\u001b[2m:\u001b[0m\u001b[2m30\u001b[0m\n","\u001b[2;36m           \u001b[0m                                                 \u001b[2m                                 \u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[21:15:10] </span><span style=\"font-weight: bold\">[</span>Data<span style=\"font-weight: bold\">]</span>: Reading data<span style=\"color: #808000; text-decoration-color: #808000\">...</span>                          <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">&lt;ipython-input-9-283f28e2abf0&gt;:30</span>\n","<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                 </span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[3m                                         Sample Data                                         \u001b[0m\n","+-------------------------------------------------------------------------------------------+\n","|\u001b[1m                source_text                 \u001b[0m\u001b[1m \u001b[0m|\u001b[1m \u001b[0m\u001b[1m                target_text                 \u001b[0m|\n","|---------------------------------------------+---------------------------------------------|\n","|     Now , you lie there on the path .       |      Now , you lie there on the path .      |\n","|She said if she were interested in someone , | She said if she were interested in someone ,|\n","|  it would be someone who looked like me .   |   it would be someone who looked like me .  |\n","+-------------------------------------------------------------------------------------------+\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">                                         Sample Data                                         </span>\n","+-------------------------------------------------------------------------------------------+\n","|<span style=\"font-weight: bold\">                source_text                  </span>|<span style=\"font-weight: bold\">                 target_text                 </span>|\n","|---------------------------------------------+---------------------------------------------|\n","|     Now , you lie there on the path .       |      Now , you lie there on the path .      |\n","|She said if she were interested in someone , | She said if she were interested in someone ,|\n","|  it would be someone who looked like me .   |   it would be someone who looked like me .  |\n","+-------------------------------------------------------------------------------------------+\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["FULL Dataset: \u001b[1m(\u001b[0m\u001b[1;36m1218\u001b[0m, \u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">FULL Dataset: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1218</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">)</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Gen Dataset: \u001b[1m(\u001b[0m\u001b[1;36m1218\u001b[0m, \u001b[1;36m2\u001b[0m\u001b[1m)\u001b[0m\n","\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Gen Dataset: <span style=\"font-weight: bold\">(</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1218</span>, <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span><span style=\"font-weight: bold\">)</span>\n","\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[1m[\u001b[0mInitiating Validation\u001b[1m]\u001b[0m\u001b[33m...\u001b[0m                       \u001b[2m<ipython-input-9-283f28e2abf0>\u001b[0m\u001b[2m:\u001b[0m\u001b[2m72\u001b[0m\n","\u001b[2;36m           \u001b[0m                                                 \u001b[2m                                 \u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"font-weight: bold\">[</span>Initiating Validation<span style=\"font-weight: bold\">]</span><span style=\"color: #808000; text-decoration-color: #808000\">...</span>                       <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">&lt;ipython-input-9-283f28e2abf0&gt;:72</span>\n","<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                 <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                                 </span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m0\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m10\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m20\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">20</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m30\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">30</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m40\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">40</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m50\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">50</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m60\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">60</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m70\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">70</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m80\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">80</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m90\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">90</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m100\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m110\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">110</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m120\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">120</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Completed \u001b[1;36m130\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Completed <span style=\"color: #008080; 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