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{
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{
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"text": [
"Using custom data configuration imsoumyaneel--sentiment-analysis-llama2-406c8d12ee6e98f7\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading and preparing dataset csv/imsoumyaneel--sentiment-analysis-llama2 to /root/.cache/huggingface/datasets/imsoumyaneel___csv/imsoumyaneel--sentiment-analysis-llama2-406c8d12ee6e98f7/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a...\n"
]
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{
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"model_id": "1b57dcefac4c41bd8afdc69d582d21ae",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"Downloading data: 0%| | 0.00/173M [00:00<?, ?B/s]"
]
},
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{
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"Extracting data files: 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
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},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "176a9bfaff1b4ff0ae8e6a701054e277",
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"version_minor": 0
},
"text/plain": [
"0 tables [00:00, ? tables/s]"
]
},
"metadata": {},
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.9/dist-packages/datasets/download/streaming_download_manager.py:695: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\n",
" return pd.read_csv(xopen(filepath_or_buffer, \"rb\", use_auth_token=use_auth_token), **kwargs)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/imsoumyaneel___csv/imsoumyaneel--sentiment-analysis-llama2-406c8d12ee6e98f7/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a. Subsequent calls will reuse this data.\n"
]
},
{
"data": {
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"model_id": "a74c347ebc614136b0a2ad549ef01e3d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/1 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['sentence', 'label', 'text'],\n",
" num_rows: 598298\n",
" })\n",
"})"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datasets import load_dataset\n",
"\n",
"# dataset = load_dataset(\"dair-ai/emotion\")\n",
"dataset = load_dataset(\"imsoumyaneel/sentiment-analysis-llama2\")\n",
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "cbdbcb02-bcc5-4928-8eff-e238437f004b",
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{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 598298 entries, 0 to 598297\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 sentence 598298 non-null object\n",
" 1 label 598298 non-null object\n",
" 2 text 598298 non-null object\n",
" 3 new_label 598298 non-null object\n",
"dtypes: object(4)\n",
"memory usage: 18.3+ MB\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 478638 entries, 352227 to 559736\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 sentence 478638 non-null object\n",
" 1 label 478638 non-null object\n",
" 2 text 478638 non-null object\n",
" 3 new_label 478638 non-null object\n",
"dtypes: object(4)\n",
"memory usage: 18.3+ MB\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 119660 entries, 0 to 598297\n",
"Data columns (total 4 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 sentence 119660 non-null object\n",
" 1 label 119660 non-null object\n",
" 2 text 119660 non-null object\n",
" 3 new_label 119660 non-null object\n",
"dtypes: object(4)\n",
"memory usage: 4.6+ MB\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>sentence</th>\n",
" <th>label</th>\n",
" <th>text</th>\n",
" <th>new_label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>I'll throw out the garbage .</td>\n",
" <td>neutral</td>\n",
" <td>###Human:\\nyou are a sentiment analist. guess ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>So Dick , how about getting some coffee for to...</td>\n",
" <td>joy</td>\n",
" <td>###Human:\\nyou are a sentiment analist. guess ...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Come on , you can at least try a little , besi...</td>\n",
" <td>neutral</td>\n",
" <td>###Human:\\nyou are a sentiment analist. guess ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>What ’ s wrong with that ? Cigarette is the th...</td>\n",
" <td>anger</td>\n",
" <td>###Human:\\nyou are a sentiment analist. guess ...</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Not for me , Dick .</td>\n",
" <td>neutral</td>\n",
" <td>###Human:\\nyou are a sentiment analist. guess ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" sentence label \\\n",
"0 I'll throw out the garbage . neutral \n",
"1 So Dick , how about getting some coffee for to... joy \n",
"2 Come on , you can at least try a little , besi... neutral \n",
"3 What ’ s wrong with that ? Cigarette is the th... anger \n",
"4 Not for me , Dick . neutral \n",
"\n",
" text new_label \n",
"0 ###Human:\\nyou are a sentiment analist. guess ... 1 \n",
"1 ###Human:\\nyou are a sentiment analist. guess ... 0 \n",
"2 ###Human:\\nyou are a sentiment analist. guess ... 1 \n",
"3 ###Human:\\nyou are a sentiment analist. guess ... 3 \n",
"4 ###Human:\\nyou are a sentiment analist. guess ... 1 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"complete_dataset = dataset['train'].to_pandas()\n",
"complete_dataset['new_label'] = complete_dataset['label'].map({'joy': '0', 'neutral': '1', 'sadness': '2', 'anger': '3', 'fear': '4', 'love': '5', 'surprise': '6'}).values\n",
"\n",
"train_dataset = complete_dataset.sample(frac=0.8,random_state=200)\n",
"test_dataset = complete_dataset.drop(train_dataset.index)\n",
"\n",
"complete_dataset.info()\n",
"train_dataset.info()\n",
"test_dataset.info()\n",
"\n",
"complete_dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0cfc926c-70df-4089-9b2e-f201faa223df",
"metadata": {
"execution": {
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"outputs": [],
"source": [
"# imports for model creation\n",
"import tensorflow as tf\n",
"from keras import layers\n",
"from keras import losses\n",
"import keras\n",
"from tensorflow.keras.preprocessing.text import Tokenizer\n",
"from tensorflow.keras.preprocessing.sequence import pad_sequences"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "eb4bab6b-ae99-4fae-bf0b-ca91be630db3",
"metadata": {
"execution": {
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}
},
"outputs": [],
"source": [
"tokenizer = Tokenizer()\n",
"tokenizer.fit_on_texts(complete_dataset['sentence'])\n",
"\n",
"vocab_size = len(tokenizer.word_index) + 1\n",
"max_length = 200 # max words in a sentence\n",
"embedding_dim = 50 # TODO: need to adjust accordinglys\n",
"\n",
"X_train = tokenizer.texts_to_sequences(train_dataset['sentence'])\n",
"X_train = pad_sequences(X_train, maxlen=max_length, padding='post')\n",
"\n",
"X_test = tokenizer.texts_to_sequences(test_dataset['sentence'])\n",
"X_test = pad_sequences(X_test, maxlen=max_length, padding='post')\n",
"\n",
"y_train = train_dataset['new_label']\n",
"y_test = test_dataset['new_label']\n",
"\n",
"from keras.utils import to_categorical\n",
"\n",
"num_classes = 7 # Assuming you have 3 classes\n",
"y_train_encoded = to_categorical(y_train, num_classes=num_classes)\n",
"y_test_encoded = to_categorical(y_test, num_classes=num_classes)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "d7202d74-95c7-4bb2-aea5-54481dfcafd6",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(478638, 200)\n",
"(119660, 200)\n",
"(478638,)\n",
"(119660,)\n"
]
}
],
"source": [
"labels = complete_dataset['label']\n",
"\n",
"print(X_train.shape)\n",
"print(X_test.shape)\n",
"print(y_train.shape)\n",
"print(y_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "738e3137-7ea4-4e71-9395-773e537083cf",
"metadata": {
"execution": {
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},
"outputs": [],
"source": [
"# Build the model\n",
"model = keras.Sequential([\n",
" keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_shape=(max_length,)),\n",
" keras.layers.GlobalAveragePooling1D(),\n",
" keras.layers.Dense(32, activation='relu'),\n",
" keras.layers.Dense(7, activation='sigmoid')\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "cd072f39-99e6-44f0-8c7f-106a0055c43b",
"metadata": {
"execution": {
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"shell.execute_reply.started": "2024-03-31T16:39:02.150090Z"
}
},
"outputs": [],
"source": [
"# Compile the model\n",
"model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "72ad6548-5d1c-4221-88c7-014dcbaea0ee",
"metadata": {
"execution": {
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},
"outputs": [],
"source": [
"# split the dataset into train and test\n",
"# from sklearn.model_selection import train_test_split\n",
"\n",
"# X_train, X_test, y_train, y_test = train_test_split(, labels, test_size=0.3, random_state=42, shuffle=True)\n",
"# X_train"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "9267da90-7a84-49d1-94d0-04a2cd3062e0",
"metadata": {
"execution": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"14958/14958 [==============================] - 55s 4ms/step - loss: 0.4894 - accuracy: 0.8447 - val_loss: 0.4174 - val_accuracy: 0.8586\n",
"Epoch 2/10\n",
"14958/14958 [==============================] - 54s 4ms/step - loss: 0.3798 - accuracy: 0.8692 - val_loss: 0.3835 - val_accuracy: 0.8651\n",
"Epoch 3/10\n",
"14958/14958 [==============================] - 54s 4ms/step - loss: 0.3453 - accuracy: 0.8761 - val_loss: 0.3638 - val_accuracy: 0.8655\n",
"Epoch 4/10\n",
"14958/14958 [==============================] - 54s 4ms/step - loss: 0.3166 - accuracy: 0.8810 - val_loss: 0.3513 - val_accuracy: 0.8645\n",
"Epoch 5/10\n",
"14958/14958 [==============================] - 55s 4ms/step - loss: 0.2941 - accuracy: 0.8848 - val_loss: 0.3548 - val_accuracy: 0.8669\n",
"Epoch 6/10\n",
"14958/14958 [==============================] - 54s 4ms/step - loss: 0.2789 - accuracy: 0.8881 - val_loss: 0.3423 - val_accuracy: 0.8654\n",
"Epoch 7/10\n",
"14958/14958 [==============================] - 55s 4ms/step - loss: 0.2675 - accuracy: 0.8909 - val_loss: 0.3447 - val_accuracy: 0.8646\n",
"Epoch 8/10\n",
"14958/14958 [==============================] - 55s 4ms/step - loss: 0.2590 - accuracy: 0.8937 - val_loss: 0.3418 - val_accuracy: 0.8658\n",
"Epoch 9/10\n",
"14958/14958 [==============================] - 55s 4ms/step - loss: 0.2511 - accuracy: 0.8963 - val_loss: 0.3417 - val_accuracy: 0.8636\n",
"Epoch 10/10\n",
"14958/14958 [==============================] - 54s 4ms/step - loss: 0.2446 - accuracy: 0.8981 - val_loss: 0.3639 - val_accuracy: 0.8604\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7f8c901c6280>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# train the model\n",
"model.fit(X_train, y_train_encoded, epochs=10, batch_size=32, validation_data=(X_test, y_test_encoded))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "24e17bec-2fbe-400f-9273-a5abe823f193",
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3740/3740 [==============================] - 6s 2ms/step - loss: 0.3639 - accuracy: 0.8604\n"
]
},
{
"data": {
"text/plain": [
"0.8604295253753662"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Evaluate the model\n",
"loss, accuracy = model.evaluate(X_test, y_test_encoded)\n",
"accuracy"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "15fbbb09-ffdf-41d3-ba11-8877aa2c078e",
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-31T17:01:34.387114Z",
"iopub.status.busy": "2024-03-31T17:01:34.386216Z",
"iopub.status.idle": "2024-03-31T17:01:34.528910Z",
"shell.execute_reply": "2024-03-31T17:01:34.528157Z",
"shell.execute_reply.started": "2024-03-31T17:01:34.387078Z"
}
},
"outputs": [],
"source": [
"# save the model\n",
"import os\n",
"try:\n",
" model.save(\"../models/sentimental-analysis-llama2.keras\")\n",
"except FileNotFoundError:\n",
" os.mkdir(\"../models\")\n",
" model.save(\"../models/sentimental-analysis-llama2.keras\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "792f0680-5a32-4c46-b5b4-eb6795b51aeb",
"metadata": {
"execution": {
"iopub.execute_input": "2024-03-31T17:04:41.901658Z",
"iopub.status.busy": "2024-03-31T17:04:41.901124Z",
"iopub.status.idle": "2024-03-31T17:04:41.948670Z",
"shell.execute_reply": "2024-03-31T17:04:41.948177Z",
"shell.execute_reply.started": "2024-03-31T17:04:41.901637Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1/1 [==============================] - 0s 17ms/step\n"
]
},
{
"data": {
"text/plain": [
"array([[0.809063 , 0.78246254, 0.02547726, 0.03657908, 0.00648503,\n",
" 0.02069169, 0.07264358]], dtype=float32)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def preprocess_text(text):\n",
" # Tokenize the text\n",
" tokenized_text = tokenizer.texts_to_sequences([text])\n",
" # Pad sequences to the same length as training data\n",
" padded_text = pad_sequences(tokenized_text, maxlen=max_length, padding='post')\n",
" return padded_text\n",
"\n",
"# Preprocess the custom input text\n",
"preprocessed_text = preprocess_text(\"this is good\")\n",
"\n",
"# Make predictions\n",
"predictions = model.predict(preprocessed_text)\n",
"\n",
"predictions"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|