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instructions

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+# Project Description
+## The sentiment analysis project utilizes the distilbert-base-uncased model from Hugging Face to perform sentiment classification on text inputs. It demonstrates how to leverage pre-trained models for natural language processing tasks using the Transformers library.
+
+# Installation Instructions
+## To set up the project on your GitHub repository and enable others to run your sentiment analysis script, follow these steps:
+
+## Clone Your Repository
+## If you haven't already cloned your GitHub repository to your local machine, do so using Git commands. Open your Gitpod terminal and navigate to the directory where you want to clone your repository:
+
+git clone <your-github-repository-url>
+cd <repository-name>
+
+## Install Dependencies
+Ensure you have Python installed. You'll also need the transformers library from Hugging Face for this project. Install it and other dependencies using pip:
+
+pip install transformers
+
+## Download the Script
+Download or create your text_analysis.py script and place it in the root directory of your repository.
+
+## Set Up Your Environment
+Set up your Python environment and ensure all necessary packages are installed:
+
+python -m venv venv  # Create a virtual environment (optional but recommended)
+source venv/bin/activate  # Activate the virtual environment (Linux/Mac)
+# or
+venv\Scripts\activate  # Activate the virtual environment (Windows)
+pip install -r requirements.txt  # Install required packages (if you have a requirements file)
+
+# Usage Examples
+Here are examples of how to use the sentiment analysis script (text_analysis.py) with different text inputs:
+
+# Import necessary libraries
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
+
+# Create a pipeline for sentiment analysis
+nlp_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+
+# Example texts for analysis
+texts = [
+    "I love using Hugging Face models!",
+    "This movie was terrible.",
+    "The weather today is nice.",
+    "I am feeling neutral about this.",
+    "The product exceeded my expectations."
+]
+
+# Perform sentiment analysis for each text
+for text in texts:
+    print(f"Text: {text}")
+    result = nlp_pipeline(text)
+    print(f"Sentiment: {result}\n")
+
+# Credits
+Include acknowledgments for libraries used and any data sources:
+
+Transformers Library: Used for loading and utilizing the distilbert-base-uncased model.
+Hugging Face Model Hub: Source of the pre-trained model checkpoint.
+Python: Programming language used for scripting.
+GitHub: Hosting platform for version control and project collaboration.

requirements.txt

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+transformers
+torch

text_analysis.py

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+# Import necessary libraries
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
+model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
+
+# Create a pipeline for sentiment analysis
+nlp_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
+
+# Example texts for analysis
+texts = [
+    "I love using Hugging Face models!",
+    "This movie was terrible.",
+    "The weather today is nice.",
+    "I am feeling neutral about this.",
+    "The product exceeded my expectations."
+    "I love my life"
+]
+
+# Perform sentiment analysis for each text
+for text in texts:
+    print(f"Text: {text}")
+    result = nlp_pipeline(text)
+    print(f"Sentiment: {result}\n")