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+ "source": [
+ "%%capture\n",
+ "\n",
+ "!pip install transformers\n",
+ "!pip install accelerate -U\n",
+ "!pip install datasets\n",
+ "!pip install huggingface_hub"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "%%capture\n",
+ "\n",
+ "import torch\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from transformers import pipeline\n",
+ "from datasets import load_dataset\n",
+ "import nltk\n",
+ "from transformers import Trainer\n",
+ "from transformers import TrainingArguments\n",
+ "nltk.download('punkt')\n",
+ "##others\n",
+ "import warnings\n",
+ "warnings.filterwarnings(\"ignore\")\n",
+ "import os\n",
+ "os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
+ "pd.set_option(\"display.max_rows\", 50)\n",
+ "pd.set_option(\"display.max_columns\", 50)"
+ ],
+ "metadata": {
+ "id": "Wj7QLw5x9oM4"
+ },
+ "execution_count": 2,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "o9apSfHD9sgd",
+ "outputId": "521cee40-1694-494e-9a7e-0d9dfcff2c67"
+ },
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Mounted at /content/drive\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "data_path= '/content/drive/MyDrive/deep-learning/capstone_data.csv'"
+ ],
+ "metadata": {
+ "id": "UCZGwUxA9wnG"
+ },
+ "execution_count": 4,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "data= pd.read_csv(data_path)"
+ ],
+ "metadata": {
+ "id": "Dg-GL0gw9ytd"
+ },
+ "execution_count": 5,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "data.head()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ },
+ "id": "4n7xoa9691w9",
+ "outputId": "fe504389-71e1-4743-dc49-5a8d9511154d"
+ },
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " Unnamed: 0 sentiment clean_review\n",
+ "0 0 1 recently shown cable tv movie opens disclaimer...\n",
+ "1 1 1 i surprised film i touched lives paulie touche...\n",
+ "2 2 -1 now im one watch movies got poor reviews say h...\n",
+ "3 3 1 this film came twelve years years ago revelati...\n",
+ "4 4 1 when orphanage manager goes vacation father ta..."
+ ],
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+ " \n",
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+ " sentiment | \n",
+ " clean_review | \n",
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+ " \n",
+ " \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
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+ " 1 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " i surprised film i touched lives paulie touche... | \n",
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+ " \n",
+ " 2 | \n",
+ " 2 | \n",
+ " -1 | \n",
+ " now im one watch movies got poor reviews say h... | \n",
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+ " 3 | \n",
+ " 3 | \n",
+ " 1 | \n",
+ " this film came twelve years years ago revelati... | \n",
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+ ]
+ },
+ "metadata": {},
+ "execution_count": 6
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "##checking for missing values\n",
+ "\n",
+ "data.isna().sum()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "EpzmuaGo-M7I",
+ "outputId": "7683db96-818c-4dfb-b3d1-6fbb1f262fe2"
+ },
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "Unnamed: 0 0\n",
+ "sentiment 0\n",
+ "clean_review 0\n",
+ "dtype: int64"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 7
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "##dropping the unneccessary column\n",
+ "\n",
+ "data= data.drop(labels= \"Unnamed: 0\", axis=1)"
+ ],
+ "metadata": {
+ "id": "wh1guTUc-NtS"
+ },
+ "execution_count": 8,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "data.head()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ },
+ "id": "qAUKfBIy-N8_",
+ "outputId": "789aaf22-8ce9-48e1-c9eb-55c07ae35e46"
+ },
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " sentiment clean_review\n",
+ "0 1 recently shown cable tv movie opens disclaimer...\n",
+ "1 1 i surprised film i touched lives paulie touche...\n",
+ "2 -1 now im one watch movies got poor reviews say h...\n",
+ "3 1 this film came twelve years years ago revelati...\n",
+ "4 1 when orphanage manager goes vacation father ta..."
+ ],
+ "text/html": [
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+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 1 | \n",
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+ " \n",
+ " 3 | \n",
+ " 1 | \n",
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+ " \n",
+ " 4 | \n",
+ " 1 | \n",
+ " when orphanage manager goes vacation father ta... | \n",
+ "
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+ " \n",
+ "
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+ "
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+ "
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+ "
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+ ]
+ },
+ "metadata": {},
+ "execution_count": 9
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "data= data.rename(columns={'sentiment': 'label'})"
+ ],
+ "metadata": {
+ "id": "ZgAPsa03-OR0"
+ },
+ "execution_count": 10,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Data Splitting"
+ ],
+ "metadata": {
+ "id": "mivsCBAB-OhG"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train, eval = train_test_split(data, test_size= 0.2, random_state= 50)"
+ ],
+ "metadata": {
+ "id": "IaTmsiYm-Ot5"
+ },
+ "execution_count": 11,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train.shape"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "GkuI7dtr-O7U",
+ "outputId": "e17ab0c8-972a-4bca-92f0-6d3ba3dc2554"
+ },
+ "execution_count": 12,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(20000, 2)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 12
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "eval.shape"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "yXsWwjLy-PH1",
+ "outputId": "d4f85996-61fe-45f0-92c4-3605e77beb13"
+ },
+ "execution_count": 13,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(5000, 2)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 13
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Loading Data With Load Dataset"
+ ],
+ "metadata": {
+ "id": "2sCQVFFa-PTf"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train.to_csv(\"/content/train_set.csv\")\n",
+ "eval.to_csv(\"/content/eval_set.csv\")"
+ ],
+ "metadata": {
+ "id": "nff9XEVl-Pep"
+ },
+ "execution_count": 14,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset= load_dataset(\"csv\", data_files={\"train_set\":\"train_set.csv\", \"eval_set\":\"eval_set.csv\" }, encoding= \"ISO-8859-1\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 145,
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+ },
+ "execution_count": 15,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading data files: 0%| | 0/2 [00:00, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "acceed5ed94246db90cea444efa0e3c3"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Extracting data files: 0%| | 0/2 [00:00, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "bc20e5e1321944568fb26deda865720e"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train_set split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1c5bc6d881d14a30a6a3c6662fd41c0b"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating eval_set split: 0 examples [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "c1566bb118a04ce091899fef6313b1f7"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "-tANTDWF-P2m",
+ "outputId": "73478f7c-5325-41c1-e432-5a9bd48a0179"
+ },
+ "execution_count": 16,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "DatasetDict({\n",
+ " train_set: Dataset({\n",
+ " features: ['Unnamed: 0', 'label', 'clean_review'],\n",
+ " num_rows: 20000\n",
+ " })\n",
+ " eval_set: Dataset({\n",
+ " features: ['Unnamed: 0', 'label', 'clean_review'],\n",
+ " num_rows: 5000\n",
+ " })\n",
+ "})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 16
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "##getting my tokenizer\n",
+ "\n",
+ "tokenizer= AutoTokenizer.from_pretrained(\"huawei-noah/TinyBERT_General_4L_312D\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 81,
+ "referenced_widgets": [
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+ "id": "gE5gBg-W-QAP",
+ "outputId": "a713fbdf-cbec-418d-998d-92b52461bb90"
+ },
+ "execution_count": 17,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)lve/main/config.json: 0%| | 0.00/409 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "ed8de3662d734d0d9381ee040e379355"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Downloading (…)solve/main/vocab.txt: 0%| | 0.00/232k [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
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+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "XCazxDZUERzL"
+ },
+ "execution_count": 17,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "## our labels are-1, and 1 and we will like to transform them into 0,1, respectively\n",
+ "\n",
+ "def transform_labels(input):\n",
+ " label= input[\"label\"]\n",
+ " num =0\n",
+ "\n",
+ " if label== -1:\n",
+ " num= 0 ##for negative sentiment\n",
+ " elif label== 1:\n",
+ " num =1 ##for positive sentiment\n",
+ " return {\"labels\": num}\n",
+ "\n",
+ "def tokenize(example):\n",
+ " return tokenizer(example[\"clean_review\"], padding= True, truncation=True, max_length=512, return_tensors= \"pt\")"
+ ],
+ "metadata": {
+ "id": "OU5nvbH4ESUY"
+ },
+ "execution_count": 18,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset= dataset.map(tokenize, batched= True)\n",
+ "remove_columns= ['Unnamed: 0', 'label', 'clean_review']\n",
+ "dataset = dataset.map(transform_labels, remove_columns=remove_columns)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 145,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "EIpBOMrrEVwP",
+ "outputId": "17aaa2e4-533b-4462-e880-575222580330"
+ },
+ "execution_count": 19,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/20000 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1a5eaf943fc9492bb1f42917aec0845d"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/5000 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d769b7dd78b64fa68ed322a9ae7e6ffd"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/20000 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "f4d7e69f13154c368c01f77845d3fe85"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Map: 0%| | 0/5000 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "96453e5f3a114cb59705779bf8a39191"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6FKUNIexEb4h",
+ "outputId": "31d92ce2-7962-4d2f-a26e-8d8b7f2a5b62"
+ },
+ "execution_count": 20,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "DatasetDict({\n",
+ " train_set: Dataset({\n",
+ " features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
+ " num_rows: 20000\n",
+ " })\n",
+ " eval_set: Dataset({\n",
+ " features: ['input_ids', 'token_type_ids', 'attention_mask', 'labels'],\n",
+ " num_rows: 5000\n",
+ " })\n",
+ "})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 20
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "###defining my compute metric for the modelling\n",
+ "\n",
+ "def compute_metrics(pred):\n",
+ " labels = pred.label_ids\n",
+ " preds = pred.predictions.argmax(-1)\n",
+ " accuracy = accuracy_score(labels, preds)\n",
+ " return {\"accuracy\": accuracy}"
+ ],
+ "metadata": {
+ "id": "baczYFH8-QKN"
+ },
+ "execution_count": 39,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "##determining my batch size for the training\n",
+ "\n",
+ "batch_size= 64"
+ ],
+ "metadata": {
+ "id": "g9DWrlMR-QTR"
+ },
+ "execution_count": 58,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "##instantiating my training metrics.\n",
+ "\n",
+ "logging_step= len(dataset['train_set'])// batch_size\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir= \"Capstone_TinyBert\",\n",
+ " num_train_epochs=5,\n",
+ " load_best_model_at_end=True,\n",
+ " learning_rate = 2e-5,\n",
+ " evaluation_strategy=\"epoch\",\n",
+ " per_device_train_batch_size= batch_size,\n",
+ " per_device_eval_batch_size= batch_size,\n",
+ " save_strategy=\"epoch\",\n",
+ " push_to_hub=True,\n",
+ " logging_steps = logging_step,\n",
+ " weight_decay=0.01 # Adding weight decay to handle overfitting\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "9eUgahQ4-QaY"
+ },
+ "execution_count": 59,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "\n",
+ "model= AutoModelForSequenceClassification.from_pretrained(\"huawei-noah/TinyBERT_General_4L_312D\", num_labels= 2)"
+ ],
+ "metadata": {
+ "id": "Ozab8KqDDaSV"
+ },
+ "execution_count": 60,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "##getting our training and evaluation set\n",
+ "\n",
+ "train_dataset= dataset['train_set'].shuffle(seed=10)\n",
+ "eval_dataset= dataset['eval_set'].shuffle(seed=10)"
+ ],
+ "metadata": {
+ "id": "axD-cLfJCvOH"
+ },
+ "execution_count": 61,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from huggingface_hub import notebook_login\n",
+ "\n",
+ "notebook_login()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 145,
+ "referenced_widgets": [
+ "5d0e32a55cf34b669606d82c7878b3d2",
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+ "8594ec08cdf4430cbec357df6b0bf3ec",
+ "78119f5aa1d94ba2b8f8503a0b4c3b84"
+ ]
+ },
+ "id": "n01FUopkC8pb",
+ "outputId": "e53bb34c-6a02-42fb-dcf1-5505ab0b454a"
+ },
+ "execution_count": 62,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value='
"
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [1565/1565 20:36, Epoch 5/5]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Epoch | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ " Accuracy | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 1 | \n",
+ " 0.462800 | \n",
+ " 0.361662 | \n",
+ " 0.852000 | \n",
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\n",
+ " \n",
+ " 2 | \n",
+ " 0.336900 | \n",
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+ " 0.864400 | \n",
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\n",
+ " \n",
+ " 3 | \n",
+ " 0.294900 | \n",
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\n",
+ " \n",
+ " 4 | \n",
+ " 0.269900 | \n",
+ " 0.319210 | \n",
+ " 0.871800 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " 0.248100 | \n",
+ " 0.317210 | \n",
+ " 0.877200 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=1565, training_loss=0.32232705960258506, metrics={'train_runtime': 1236.7702, 'train_samples_per_second': 80.856, 'train_steps_per_second': 1.265, 'total_flos': 1433899622400000.0, 'train_loss': 0.32232705960258506, 'epoch': 5.0})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 64
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "trainer.push_to_hub()"
+ ],
+ "metadata": {
+ "id": "H50kPXBNC-JW",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "outputId": "7bb224dd-e7f0-4cca-ba52-7fac37df81e6"
+ },
+ "execution_count": 65,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'https://huggingface.co/gArthur98/Capstone_TinyBert/tree/main/'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 65
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "xqGQ_M0DKExs"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file