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"\n",
"!pip install transformers\n",
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"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..."
],
"text/html": [
"\n",
" \n",
"
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"\n",
"
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" \n",
" \n",
" | \n",
" Unnamed: 0 | \n",
" sentiment | \n",
" clean_review | \n",
"
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"
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"
\n",
"
\n"
]
},
"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": [
"\n",
" \n",
"
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"\n",
"
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" \n",
" \n",
" | \n",
" sentiment | \n",
" clean_review | \n",
"
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" 0 | \n",
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" recently shown cable tv movie opens disclaimer... | \n",
"
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" i surprised film i touched lives paulie touche... | \n",
"
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" 2 | \n",
" -1 | \n",
" now im one watch movies got poor reviews say h... | \n",
"
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" 1 | \n",
" this film came twelve years years ago revelati... | \n",
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"
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"
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"
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"
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"
\n"
]
},
"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,
"referenced_widgets": [
"acceed5ed94246db90cea444efa0e3c3",
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},
"id": "e1FQsILf-Pr3",
"outputId": "98b544f7-07e3-43b2-feb4-783633449104"
},
"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,
"model_id": "4fe48bfb1af646ceb8d241717d3b0290"
}
},
"metadata": {}
}
]
},
{
"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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"9097772f3c7c49c5903415f45ac60b4c",
"26ae24806224467cae9f6aabaff96913",
"9944781f8a354836a346f10a016e4171",
"1ffc9f70aa5b49b68f1e5e5d6769115c",
"384ff47c81664f9e8489b35bdf00d2e7",
"9385d43dd9854a47ab60577a96f687a2",
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"c495d015b2c845128e24dd025a24750c",
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"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",
"
\n",
" \n",
" 2 | \n",
" 0.336900 | \n",
" 0.321795 | \n",
" 0.864400 | \n",
"
\n",
" \n",
" 3 | \n",
" 0.294900 | \n",
" 0.314254 | \n",
" 0.874400 | \n",
"
\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",
"
"
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},
"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
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},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "xqGQ_M0DKExs"
},
"execution_count": null,
"outputs": []
}
]
}