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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",
+ "nltk.download('punkt')\n",
+ "from transformers import Trainer\n",
+ "from transformers import TrainingArguments\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": "EkJ2l84Bb6LX"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount('/content/drive')"
+ ],
+ "metadata": {
+ "id": "srRtgXOxb_c6"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Importing Dataset"
+ ],
+ "metadata": {
+ "id": "Uh05EYjVd9Yj"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "data_path= '/content/drive/MyDrive/deep-learning/capstone_data.csv'"
+ ],
+ "metadata": {
+ "id": "e_WtrwtYeOYd"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "data= pd.read_csv(data_path)"
+ ],
+ "metadata": {
+ "id": "Dc_49AsceVOy"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "data.head()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ },
+ "id": "zA2KfvgFeYTi",
+ "outputId": "4406ba05-ea2e-43fa-8fbc-464d0929c658"
+ },
+ "execution_count": null,
+ "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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+ " clean_review | \n",
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+ " 1 | \n",
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+ " 1 | \n",
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+ " 2 | \n",
+ " -1 | \n",
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+ " 3 | \n",
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+ ]
+ },
+ "metadata": {},
+ "execution_count": 50
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "##checking for missing values\n",
+ "\n",
+ "data.isna().sum()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "2ruTQeoOea5b",
+ "outputId": "f81aa336-d3ea-477f-c115-d9c279e75494"
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+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "Unnamed: 0 0\n",
+ "sentiment 0\n",
+ "clean_review 0\n",
+ "dtype: int64"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 51
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "##dropping the unneccessary column\n",
+ "\n",
+ "data= data.drop(labels= \"Unnamed: 0\", axis=1)"
+ ],
+ "metadata": {
+ "id": "PljqGK2oefHk"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "data.head()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 206
+ },
+ "id": "pOe9KgGgerGG",
+ "outputId": "6efacf00-3363-4380-d651-5cf61084a6e3"
+ },
+ "execution_count": null,
+ "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..."
+ ],
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+ " clean_review | \n",
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+ " \n",
+ " \n",
+ " \n",
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+ " 4 | \n",
+ " 1 | \n",
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+ "
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+ " \n",
+ "
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+ "
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+ "
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+ "
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+ ]
+ },
+ "metadata": {},
+ "execution_count": 53
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "data= data.rename(columns={'sentiment': 'label'})"
+ ],
+ "metadata": {
+ "id": "sux_mf6pijRJ"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Data Splitting"
+ ],
+ "metadata": {
+ "id": "CEpwk7d4esHz"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train, eval = train_test_split(data, test_size= 0.2, random_state= 50)"
+ ],
+ "metadata": {
+ "id": "3RA15K4oe3qc"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train.shape"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "JsqDlSY_fCIv",
+ "outputId": "af4aa45f-d433-49ec-aac7-488d700b50ee"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(20000, 2)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 56
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "eval.shape"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "OhPJdntKfGXg",
+ "outputId": "d03befe4-5391-4a5e-c1a7-246f41cb679c"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(5000, 2)"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 57
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ " # iii. Loading Datasets using Load_dataset"
+ ],
+ "metadata": {
+ "id": "iDpgwhI7fJH9"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train.to_csv(\"/content/train_set.csv\")\n",
+ "eval.to_csv(\"/content/eval_set.csv\")"
+ ],
+ "metadata": {
+ "id": "UwmRasGEfOAe"
+ },
+ "execution_count": null,
+ "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,
+ "referenced_widgets": [
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+ "execution_count": null,
+ "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": "308c75875bfc42ec99a4094f3e25c39d"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Extracting data files: 0%| | 0/2 [00:00, ?it/s]"
+ ],
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+ }
+ },
+ "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": "a17e412593ea41d89784935ccea1f4ef"
+ }
+ },
+ "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": "ddd49a19eb004c5bb4517c49fc81e49f"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zTe1KcYIfd59",
+ "outputId": "d71186ae-2d4e-4e5a-8207-7e427d872ca7"
+ },
+ "execution_count": null,
+ "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": 60
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "###. Tokenization"
+ ],
+ "metadata": {
+ "id": "V_JQiOqSfhjp"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "tokenizer= AutoTokenizer.from_pretrained(\"roberta-base\")"
+ ],
+ "metadata": {
+ "id": "es-TPJNzfma1"
+ },
+ "execution_count": null,
+ "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= \"max_length\", truncation=True, return_tensors= \"pt\")"
+ ],
+ "metadata": {
+ "id": "bBxFMFfSfoBf"
+ },
+ "execution_count": null,
+ "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": [
+ "91ac2882718c42bfb79bd699de76f7a4",
+ "841849547def4bdcbdda5374300b087b",
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+ "8acf45cbc6c84e2481881ee09af4ce98",
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+ "2629f57540f54fcb8be31980aa435b5b",
+ "3ffbbab6b11247318bd2387fc07686a5"
+ ]
+ },
+ "id": "JKFeQudihC--",
+ "outputId": "ff07517c-0fde-4d8e-9c14-7273c685e982"
+ },
+ "execution_count": null,
+ "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": "91ac2882718c42bfb79bd699de76f7a4"
+ }
+ },
+ "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": "a1036f3be196454f98a2d5ba75653010"
+ }
+ },
+ "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": "de372a98d21845baa5fcf92183bc28e1"
+ }
+ },
+ "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": "d0de0be9861040a8a6a1cda8026c58a6"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "dataset"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "t-K5O1uAhGJk",
+ "outputId": "3a30e63b-92a4-43ed-d5b4-8b363651033a"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "DatasetDict({\n",
+ " train_set: Dataset({\n",
+ " features: ['input_ids', 'attention_mask', 'labels'],\n",
+ " num_rows: 20000\n",
+ " })\n",
+ " eval_set: Dataset({\n",
+ " features: ['input_ids', 'attention_mask', 'labels'],\n",
+ " num_rows: 5000\n",
+ " })\n",
+ "})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 64
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "_H7tFkzPyTVT"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## ii.Modelling"
+ ],
+ "metadata": {
+ "id": "8U9Q8XTjjUC2"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "\n",
+ "model= AutoModelForSequenceClassification.from_pretrained(\"roberta-base\", num_labels= 2)"
+ ],
+ "metadata": {
+ "id": "Qp4ZWPh8jXS_"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "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": "RnjgNjEEjaAu"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "batch_size= 16"
+ ],
+ "metadata": {
+ "id": "Wu8bwHthjq6s"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "\n",
+ "\n",
+ "training_args = TrainingArguments(\n",
+ " output_dir=\"Roberta-capstone\",\n",
+ " num_train_epochs=5,\n",
+ " load_best_model_at_end=True,\n",
+ " evaluation_strategy=\"epoch\",\n",
+ " save_strategy=\"epoch\",\n",
+ " push_to_hub=True,\n",
+ " weight_decay=0.02, # Adding weight decay to handle overfitting\n",
+ "\n",
+ ")"
+ ],
+ "metadata": {
+ "id": "EzkYo9zgjtVP"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "train_dataset= dataset['train_set'].shuffle(seed=10)\n",
+ "eval_dataset= dataset['eval_set'].shuffle(seed=10)"
+ ],
+ "metadata": {
+ "id": "FRIGcO85j2Ho"
+ },
+ "execution_count": null,
+ "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": [
+ "62801d4504a8429a9dcb4ed03a5c4da5",
+ "50cb06e1c9884b7592527c5658794676",
+ "be4c2a6309ac4504bd5257ce2524b396",
+ "8b9263c955734ff3bb2c0a78e64b4c62",
+ "be8f4578252a4eb2aa72c1ee98e33706",
+ "d11fa170f0434f6cabe654d2c1c6f373",
+ "0300d67c420c45b88feb26530a6aedc7",
+ "9c8e61c111394e8db2b4faea5fc0878e",
+ "b042bbc4a9a243cd9f7a03731008324f",
+ "922ad4da95004e4da67582c7a46b7a06",
+ "cd2c9d771c2649f1b17e3104476bfc07",
+ "a1e867d560394183b1129298f74bd2fc",
+ "269b094a6492407dad746d80e9e75864",
+ "7fe2c2c84b8e442793016372ad3c693a",
+ "e55ab02c14884a509c13aec2e2a3efaa",
+ "2374e6665922448d80cc13b8f38b8acf",
+ "83ce3101b43042398514a67fceef9820",
+ "ad12edb9496a4d9d880b3027cab2e0a5",
+ "6fb0284301a94dce972cddeb554ec763",
+ "4488e5db260d45e5abe78295f263b59e",
+ "be2d210472f9491f8546b80664537d7c",
+ "7d63c3d38e6c44718422031fafc13a11",
+ "9152c4fc664344a59b9f25f4ad863600",
+ "24976caaaa7242dcb81855d2be42d299",
+ "755da9355a124a60a18adce2201542e0",
+ "893d4ca90764483f9baa96b66ebe0f4d",
+ "0048228aa3464542847989c4391db387",
+ "dde09ee4afc24b58891297eb8e10554d",
+ "92014993f8a74fdda44afff1a4e9dcdd",
+ "5c66c8808d9b453b9fc07d044604422a",
+ "4d867d3081b64d6e91a412614a62e388",
+ "a67f8a6224494afaaafe2f6b80b86d86"
+ ]
+ },
+ "id": "AYYZV-cBj38V",
+ "outputId": "1c23e448-3ed8-4858-a366-d3212d3a879e"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "VBox(children=(HTML(value=' "
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [3125/3125 2:49:45, Epoch 5/5]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " Epoch | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ " Accuracy | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 1 | \n",
+ " 0.316400 | \n",
+ " 0.239256 | \n",
+ " 0.908600 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 0.211100 | \n",
+ " 0.215543 | \n",
+ " 0.916000 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 0.156800 | \n",
+ " 0.349332 | \n",
+ " 0.913600 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 0.071400 | \n",
+ " 0.366855 | \n",
+ " 0.919200 | \n",
+ "
\n",
+ " \n",
+ " 5 | \n",
+ " 0.039800 | \n",
+ " 0.401343 | \n",
+ " 0.920600 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "TrainOutput(global_step=3125, training_loss=0.14534735946655272, metrics={'train_runtime': 10188.082, 'train_samples_per_second': 9.815, 'train_steps_per_second': 0.307, 'total_flos': 2.6311105536e+16, 'train_loss': 0.14534735946655272, 'epoch': 5.0})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 74
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "trainer.push_to_hub()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
+ },
+ "id": "_PNeF2DykJfF",
+ "outputId": "a9881696-a8ba-4f76-ba6e-1a3356e8d737"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'https://huggingface.co./gArthur98/Roberta-capstone_2/tree/main/'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 75
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [],
+ "metadata": {
+ "id": "rriv3rRYZUxa"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file