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LLM-finetuning-subtask-1
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "87a2606d",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:42:09.092567Z",
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},
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"papermill": {
"duration": 1.291674,
"end_time": "2024-05-31T19:42:10.371377",
"exception": false,
"start_time": "2024-05-31T19:42:09.079703",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading hatespeech-data, 877272 bytes compressed\n",
"[==================================================] 877272 bytes downloadedFailed to load https://storage.googleapis.com/kaggle-data-sets/5075104/8503427/bundle/archive.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20240531%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20240531T064543Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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 to path /kaggle/input/hatespeech-data\n",
"Data source import complete.\n"
]
}
],
"source": [
"\n",
"# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES\n",
"# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n",
"# THEN FEEL FREE TO DELETE THIS CELL.\n",
"# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
"# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
"# NOTEBOOK.\n",
"\n",
"import os\n",
"import sys\n",
"from tempfile import NamedTemporaryFile\n",
"from urllib.request import urlopen\n",
"from urllib.parse import unquote, urlparse\n",
"from urllib.error import HTTPError\n",
"from zipfile import ZipFile\n",
"import tarfile\n",
"import shutil\n",
"\n",
"CHUNK_SIZE = 40960\n",
"DATA_SOURCE_MAPPING = 'hatespeech-data:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F5075104%2F8503427%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240531%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240531T064543Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'\n",
"\n",
"KAGGLE_INPUT_PATH='/kaggle/input'\n",
"KAGGLE_WORKING_PATH='/kaggle/working'\n",
"KAGGLE_SYMLINK='kaggle'\n",
"\n",
"!umount /kaggle/input/ 2> /dev/null\n",
"shutil.rmtree('/kaggle/input', ignore_errors=True)\n",
"os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n",
"os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n",
"\n",
"try:\n",
" os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n",
"except FileExistsError:\n",
" pass\n",
"try:\n",
" os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n",
"except FileExistsError:\n",
" pass\n",
"\n",
"for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n",
" directory, download_url_encoded = data_source_mapping.split(':')\n",
" download_url = unquote(download_url_encoded)\n",
" filename = urlparse(download_url).path\n",
" destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
" try:\n",
" with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n",
" total_length = fileres.headers['content-length']\n",
" print(f'Downloading {directory}, {total_length} bytes compressed')\n",
" dl = 0\n",
" data = fileres.read(CHUNK_SIZE)\n",
" while len(data) > 0:\n",
" dl += len(data)\n",
" tfile.write(data)\n",
" done = int(50 * dl / int(total_length))\n",
" sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
" sys.stdout.flush()\n",
" data = fileres.read(CHUNK_SIZE)\n",
" if filename.endswith('.zip'):\n",
" with ZipFile(tfile) as zfile:\n",
" zfile.extractall(destination_path)\n",
" else:\n",
" with tarfile.open(tfile.name) as tarfile:\n",
" tarfile.extractall(destination_path)\n",
" print(f'\\nDownloaded and uncompressed: {directory}')\n",
" except HTTPError as e:\n",
" print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
" continue\n",
" except OSError as e:\n",
" print(f'Failed to load {download_url} to path {destination_path}')\n",
" continue\n",
"\n",
"print('Data source import complete.')\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cef55350",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:42:10.399747Z",
"iopub.status.busy": "2024-05-31T19:42:10.399432Z",
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"shell.execute_reply": "2024-05-31T19:44:17.342417Z"
},
"id": "iVOrT-5vZcAM",
"outputId": "62b96a64-f162-45ab-a968-7c93f146d23c",
"papermill": {
"duration": 126.961091,
"end_time": "2024-05-31T19:44:17.346377",
"exception": false,
"start_time": "2024-05-31T19:42:10.385286",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#installing the dependencies\n",
"!pip install -q accelerate\n",
"!pip install -q transformers\n",
"!pip install -q peft\n",
"!pip install -q bitsandbytes\n",
"!pip install -q datasets\n",
"!pip install -q trl\n",
"!pip install huggingface_hub -q\n",
"!pip install datasets -q\n",
"!pip install nltk -q"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5a7b64a8",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:44:17.373099Z",
"iopub.status.busy": "2024-05-31T19:44:17.372231Z",
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"shell.execute_reply": "2024-05-31T19:44:19.553653Z"
},
"id": "oK7D7CN8ZcAR",
"outputId": "533d77a9-1a3a-4177-bd2c-7113b4a6974f",
"papermill": {
"duration": 2.198262,
"end_time": "2024-05-31T19:44:19.557066",
"exception": false,
"start_time": "2024-05-31T19:44:17.358804",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to /usr/share/nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n",
"[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n",
"[nltk_data] Downloading package wordnet to /usr/share/nltk_data...\n",
"[nltk_data] Package wordnet is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import nltk\n",
"nltk.download('punkt')\n",
"nltk.download('stopwords')\n",
"nltk.download('wordnet')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "df814569",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:44:19.584884Z",
"iopub.status.busy": "2024-05-31T19:44:19.584282Z",
"iopub.status.idle": "2024-05-31T19:44:19.589861Z",
"shell.execute_reply": "2024-05-31T19:44:19.588977Z"
},
"papermill": {
"duration": 0.021546,
"end_time": "2024-05-31T19:44:19.592092",
"exception": false,
"start_time": "2024-05-31T19:44:19.570546",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"['/root/nltk_data',\n",
" '/usr/share/nltk_data',\n",
" '/usr/local/share/nltk_data',\n",
" '/usr/lib/nltk_data',\n",
" '/usr/local/lib/nltk_data']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nltk.data.path"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3ee365ec",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:44:19.616946Z",
"iopub.status.busy": "2024-05-31T19:44:19.616685Z",
"iopub.status.idle": "2024-05-31T19:44:41.659289Z",
"shell.execute_reply": "2024-05-31T19:44:41.658507Z"
},
"id": "_va8igNpZcAT",
"papermill": {
"duration": 22.057709,
"end_time": "2024-05-31T19:44:41.661758",
"exception": false,
"start_time": "2024-05-31T19:44:19.604049",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-05-31 19:44:31.685315: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-05-31 19:44:31.685419: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-05-31 19:44:31.851406: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
]
}
],
"source": [
"from datasets import load_dataset\n",
"from transformers import (\n",
" AutoModelForSequenceClassification,\n",
" AutoTokenizer,\n",
" BitsAndBytesConfig,\n",
" HfArgumentParser,\n",
" AutoTokenizer,\n",
" TrainingArguments,\n",
" Trainer,\n",
" GenerationConfig,\n",
" DataCollatorWithPadding\n",
")\n",
"from tqdm import tqdm\n",
"from trl import SFTTrainer\n",
"import torch\n",
"import time\n",
"import pandas as pd\n",
"import numpy as np\n",
"import re\n",
"from nltk.corpus import stopwords\n",
"from nltk.tokenize import word_tokenize\n",
"from nltk.stem import WordNetLemmatizer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import classification_report"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "42ebd9bd",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:44:41.689339Z",
"iopub.status.busy": "2024-05-31T19:44:41.688586Z",
"iopub.status.idle": "2024-05-31T19:44:41.966972Z",
"shell.execute_reply": "2024-05-31T19:44:41.966112Z"
},
"id": "8H9CkZZGZcAV",
"outputId": "281a196e-a85e-434a-a9b9-b18da130e50a",
"papermill": {
"duration": 0.293784,
"end_time": "2024-05-31T19:44:41.969232",
"exception": false,
"start_time": "2024-05-31T19:44:41.675448",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Token has not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n",
"Token is valid (permission: write).\n",
"Your token has been saved to /root/.cache/huggingface/token\n",
"Login successful\n"
]
}
],
"source": [
"from kaggle_secrets import UserSecretsClient\n",
"user_secrets = UserSecretsClient()\n",
"hf_token = user_secrets.get_secret(\"HF_TOKEN\")\n",
"\n",
"\n",
"from huggingface_hub import login\n",
"login(token = hf_token)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "cf38afeb",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:44:41.995618Z",
"iopub.status.busy": "2024-05-31T19:44:41.994913Z",
"iopub.status.idle": "2024-05-31T19:44:41.999134Z",
"shell.execute_reply": "2024-05-31T19:44:41.998308Z"
},
"id": "t1XnFdehZcAZ",
"papermill": {
"duration": 0.019606,
"end_time": "2024-05-31T19:44:42.001161",
"exception": false,
"start_time": "2024-05-31T19:44:41.981555",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"torch.backends.cuda.enable_mem_efficient_sdp(False)\n",
"torch.backends.cuda.enable_flash_sdp(False)\n",
"#torch.set_default_tensor_type(torch.cuda.FloatTensor)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c162bbad",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:44:42.027202Z",
"iopub.status.busy": "2024-05-31T19:44:42.026877Z",
"iopub.status.idle": "2024-05-31T19:44:45.003304Z",
"shell.execute_reply": "2024-05-31T19:44:45.002391Z"
},
"id": "bW1NeTT3ZcAc",
"papermill": {
"duration": 2.992225,
"end_time": "2024-05-31T19:44:45.005423",
"exception": false,
"start_time": "2024-05-31T19:44:42.013198",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "c4cd12b8fc704c96bcc2a1bb245cc87f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading readme: 0%| | 0.00/5.92k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading data: 100%|██████████| 1.63M/1.63M [00:00<00:00, 6.47MB/s]\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f180c47dddd0414290edb69629eb9d2e",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Generating train split: 0%| | 0/24783 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = load_dataset(\"tdavidson/hate_speech_offensive\")\n",
"data = data['train']\n",
"data_df = data.to_pandas()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "86c31b0b",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:44:45.032950Z",
"iopub.status.busy": "2024-05-31T19:44:45.032657Z",
"iopub.status.idle": "2024-05-31T19:44:45.036539Z",
"shell.execute_reply": "2024-05-31T19:44:45.035730Z"
},
"id": "yhMcmX1chfUc",
"papermill": {
"duration": 0.019478,
"end_time": "2024-05-31T19:44:45.038362",
"exception": false,
"start_time": "2024-05-31T19:44:45.018884",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#data_df = data_df[:5000]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ccdcbc46",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:44:45.065771Z",
"iopub.status.busy": "2024-05-31T19:44:45.065212Z",
"iopub.status.idle": "2024-05-31T19:44:50.174536Z",
"shell.execute_reply": "2024-05-31T19:44:50.173385Z"
},
"id": "p0fWiiKMZcAf",
"papermill": {
"duration": 5.125737,
"end_time": "2024-05-31T19:44:50.177015",
"exception": false,
"start_time": "2024-05-31T19:44:45.051278",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Load the stop words\n",
"stop_words = set(stopwords.words('english'))\n",
"\n",
"def preprocess_text(text):\n",
" # Lowercase the text\n",
" text = text.lower()\n",
" # Remove special characters and punctuation\n",
" text = re.sub(r'[^a-zA-Z0-9\\s]', '', text)\n",
" # Tokenize the text\n",
" tokens = word_tokenize(text)\n",
" # Remove stop words and lemmatize the tokens\n",
" tokens = [word for word in tokens if word not in stop_words]\n",
" # Join the tokens back into a single string\n",
" text = ' '.join(tokens)\n",
" return text\n",
"\n",
"# Apply the preprocessing function to the 'text' column\n",
"data_df['tweet'] = data_df['tweet'].apply(preprocess_text)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "09146c8b",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:44:50.204878Z",
"iopub.status.busy": "2024-05-31T19:44:50.204564Z",
"iopub.status.idle": "2024-05-31T19:44:50.223877Z",
"shell.execute_reply": "2024-05-31T19:44:50.222912Z"
},
"papermill": {
"duration": 0.035651,
"end_time": "2024-05-31T19:44:50.226100",
"exception": false,
"start_time": "2024-05-31T19:44:50.190449",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"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>count</th>\n",
" <th>hate_speech_count</th>\n",
" <th>offensive_language_count</th>\n",
" <th>neither_count</th>\n",
" <th>class</th>\n",
" <th>tweet</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>rt mayasolovely woman shouldnt complain cleani...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>rt mleew17 boy dats coldtyga dwn bad cuffin da...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>rt urkindofbrand dawg rt 80sbaby4life ever fuc...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>rt cganderson vivabased look like tranny</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>rt shenikaroberts shit hear might true might f...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24778</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>yous muthafin lie 8220lifeasking 20pearls core...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24779</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>youve gone broke wrong heart baby drove rednec...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24780</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>young buck wan na eat dat nigguh like aint fuc...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24781</th>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>youu got wild bitches tellin lies</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24782</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>ruffled ntac eileen dahlia beautiful color com...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>24783 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" count hate_speech_count offensive_language_count neither_count \\\n",
"0 3 0 0 3 \n",
"1 3 0 3 0 \n",
"2 3 0 3 0 \n",
"3 3 0 2 1 \n",
"4 6 0 6 0 \n",
"... ... ... ... ... \n",
"24778 3 0 2 1 \n",
"24779 3 0 1 2 \n",
"24780 3 0 3 0 \n",
"24781 6 0 6 0 \n",
"24782 3 0 0 3 \n",
"\n",
" class tweet \n",
"0 2 rt mayasolovely woman shouldnt complain cleani... \n",
"1 1 rt mleew17 boy dats coldtyga dwn bad cuffin da... \n",
"2 1 rt urkindofbrand dawg rt 80sbaby4life ever fuc... \n",
"3 1 rt cganderson vivabased look like tranny \n",
"4 1 rt shenikaroberts shit hear might true might f... \n",
"... ... ... \n",
"24778 1 yous muthafin lie 8220lifeasking 20pearls core... \n",
"24779 2 youve gone broke wrong heart baby drove rednec... \n",
"24780 1 young buck wan na eat dat nigguh like aint fuc... \n",
"24781 1 youu got wild bitches tellin lies \n",
"24782 2 ruffled ntac eileen dahlia beautiful color com... \n",
"\n",
"[24783 rows x 6 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_df"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "fde2f138",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:44:50.253943Z",
"iopub.status.busy": "2024-05-31T19:44:50.253641Z",
"iopub.status.idle": "2024-05-31T19:44:50.264981Z",
"shell.execute_reply": "2024-05-31T19:44:50.264033Z"
},
"id": "rYvG3SxvZcAh",
"papermill": {
"duration": 0.027147,
"end_time": "2024-05-31T19:44:50.267010",
"exception": false,
"start_time": "2024-05-31T19:44:50.239863",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"x = list(data_df['tweet'])\n",
"y = list(data_df['class'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "0c59802b",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:44:50.296992Z",
"iopub.status.busy": "2024-05-31T19:44:50.296277Z",
"iopub.status.idle": "2024-05-31T19:44:50.302282Z",
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},
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"papermill": {
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"compute_dtype = getattr(torch, \"float16\")\n",
"bnb_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_quant_type='nf4',\n",
" bnb_4bit_compute_dtype=compute_dtype,\n",
" bnb_4bit_use_double_quant=False,\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "badd9470",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:44:50.333561Z",
"iopub.status.busy": "2024-05-31T19:44:50.333161Z",
"iopub.status.idle": "2024-05-31T19:45:26.972165Z",
"shell.execute_reply": "2024-05-31T19:45:26.971160Z"
},
"id": "oQlPslv5ZcAm",
"outputId": "b4e71c4e-4756-4029-dd51-e452410b4c38",
"papermill": {
"duration": 36.656344,
"end_time": "2024-05-31T19:45:26.974673",
"exception": false,
"start_time": "2024-05-31T19:44:50.318329",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py:468: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n",
" warnings.warn(\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "ca7f2a1a55d74d29a39957d0cc99772f",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"config.json: 0%| | 0.00/735 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"text/plain": [
"model.safetensors.index.json: 0%| | 0.00/35.7k [00:00<?, ?B/s]"
]
},
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},
{
"data": {
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"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "6794de7332e74ea0ae5c0fa54437cdfa",
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},
"text/plain": [
"model-00001-of-00002.safetensors: 0%| | 0.00/5.00G [00:00<?, ?B/s]"
]
},
"metadata": {},
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},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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},
"text/plain": [
"model-00002-of-00002.safetensors: 0%| | 0.00/564M [00:00<?, ?B/s]"
]
},
"metadata": {},
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},
{
"data": {
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"model_id": "1f0e2e133d224fa798b449b17c950d0d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of PhiForSequenceClassification were not initialized from the model checkpoint at microsoft/phi-2 and are newly initialized: ['score.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
}
],
"source": [
"model_name='microsoft/phi-2'\n",
"device_map = {\"\": 0}\n",
"model = AutoModelForSequenceClassification.from_pretrained(model_name,\n",
" num_labels = 3,\n",
" device_map=device_map,\n",
" quantization_config=bnb_config,\n",
" trust_remote_code=True,\n",
" use_auth_token=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "89c4077e",
"metadata": {
"execution": {
"iopub.execute_input": "2024-05-31T19:45:27.008353Z",
"iopub.status.busy": "2024-05-31T19:45:27.007419Z",
"iopub.status.idle": "2024-05-31T19:45:28.389322Z",
"shell.execute_reply": "2024-05-31T19:45:28.388105Z"
},
"id": "D6EQ3uiSZcAn",
"outputId": "e903dd08-6c47-4da6-97c6-358dd5e64e79",
"papermill": {
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"end_time": "2024-05-31T19:45:28.392679",
"exception": false,
"start_time": "2024-05-31T19:45:26.990591",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1ed81faf2a364a169e4873cc8ede4ec7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"tokenizer_config.json: 0%| | 0.00/7.34k [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"version_major": 2,
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},
"text/plain": [
"vocab.json: 0%| | 0.00/798k [00:00<?, ?B/s]"
]
},
"metadata": {},
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},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"text/plain": [
"merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s]"
]
},
"metadata": {},
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},
{
"data": {
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},
"text/plain": [
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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},
"text/plain": [
"special_tokens_map.json: 0%| | 0.00/99.0 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"version_minor": 0
},
"text/plain": [
"tokenizer.json: 0%| | 0.00/2.11M [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",