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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setup and data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\ESenn\\Miniconda3\\envs\\llm_class\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import BertTokenizer, BertForSequenceClassification\n",
+    "from transformers import pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load your data here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transformers Library for Sentiment Classification using BERT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Task:** Review classification\n",
+    "\n",
+    "**Model:** A pretrained BERT model including a sentiment classification head (which is not trained on our task).\n",
+    "\n",
+    "We use the hidden states of BERT for Sentiment Classification.\n",
+    "The classifier is not trained. See notebooks for chapter 2 for training, and chapter 5 for a step-by-stey description of how to train it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\ESenn\\Miniconda3\\envs\\llm_class\\Lib\\site-packages\\transformers\\tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Text: This movie was fantastic! I loved it.\n",
+      "Sentiment: 5 stars\n",
+      "Text: The food was terrible, I will never come back.\n",
+      "Sentiment: 1 star\n",
+      "Text: The service was just okay, nothing special.\n",
+      "Sentiment: 3 stars\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Load pre-trained BERT tokenizer and model for sentiment classification\n",
+    "tokenizer = BertTokenizer.from_pretrained(\n",
+    "    \"nlptown/bert-base-multilingual-uncased-sentiment\"\n",
+    ")\n",
+    "model = BertForSequenceClassification.from_pretrained(\n",
+    "    \"nlptown/bert-base-multilingual-uncased-sentiment\"\n",
+    ")\n",
+    "\n",
+    "# Load the classification pipeline\n",
+    "classifier = pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer)\n",
+    "\n",
+    "# Example texts for classification\n",
+    "texts = [\n",
+    "    \"This movie was fantastic! I loved it.\",\n",
+    "    \"The food was terrible, I will never come back.\",\n",
+    "    \"The service was just okay, nothing special.\",\n",
+    "]\n",
+    "\n",
+    "# Perform classification\n",
+    "results = classifier(texts)\n",
+    "\n",
+    "# Display the results\n",
+    "for text, result in zip(texts, results):\n",
+    "    print(f\"Text: {text}\")\n",
+    "    print(f\"Sentiment: {result['label']}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "llm_class",
+   "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}