Create README.md

README.md

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+---
+datasets:
+- syedkhalid076/Sentiment-Analysis-Over-sampled
+language:
+- en
+metrics:
+- accuracy: 0.9019906657776932
+- accuracy
+model_name: RoBERTa Sentiment Analysis Model v2
+base_model: roberta-base
+library_name: transformers
+tags:
+- Text Classification
+- Transformers
+- Safetensors
+- English
+- roberta
+- Inference Endpoints
+pipeline_tag: text-classification
+---
+
+
+# RoBERTa Sentiment Analysis Model v2
+
+This repository hosts a fine-tuned [RoBERTa](https://huggingface.co/roberta-base) model for sentiment analysis. The model classifies text into three categories: **Negative (0)**, **Neutral (1)**, and **Positive (2)**. It has been fine-tuned on the [syedkhalid076/Sentiment-Analysis-Over-sampled](https://huggingface.co/datasets/syedkhalid076/Sentiment-Analysis-Over-sampled) dataset and achieves high accuracy.
+The Model is Trained specifically for Feedback Sentiment Analysis for UX Research, but it does perform well on other Sentiment Analysis tasks.
+
+---
+
+## Model Details
+
+- **Base Model**: [RoBERTa-base](https://huggingface.co/roberta-base)
+- **Number of Labels**: 3 (0:Negative, 1:Neutral, 2:Positive)
+- **Model Size**: 125M parameters
+- **Language**: English (`en`)
+- **Metrics**: Accuracy: **90.20%**
+- **Tensor Type**: FP32
+- **Dataset**: [syedkhalid076/Sentiment-Analysis-Over-sampled](https://huggingface.co/datasets/syedkhalid076/Sentiment-Analysis-Over-sampled)
+- **Library**: [Transformers](https://github.com/huggingface/transformers)
+- **File Format**: [Safetensors](https://github.com/huggingface/safetensors)
+
+---
+
+## Features
+
+- **Text Classification**: Identify the sentiment of input text as Negative, Neutral, or Positive.
+- **High Accuracy**: Achieves 90.20% accuracy on the evaluation dataset.
+- **Hosted on Hugging Face**: Ready-to-use inference endpoints for quick deployment.
+- **Efficient Inference**: Lightweight and efficient, supporting FP32 tensors.
+
+---
+
+## Installation
+
+To use this model, ensure you have the `transformers` library installed:
+
+```bash
+pip install transformers
+```
+
+---
+
+## Usage
+
+Here’s how you can load the model and tokenizer and perform inference:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("syedkhalid076/RoBERTa-Sentimental-Analysis-Model")
+model = AutoModelForSequenceClassification.from_pretrained("syedkhalid076/RoBERTa-Sentimental-Analysis-Model")
+
+# Define input text
+text = "I absolutely love this product! It's fantastic."
+
+# Tokenize input
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+# Perform inference
+outputs = model(**inputs)
+logits = outputs.logits
+predicted_class = torch.argmax(logits, dim=-1).item()
+
+# Print results
+sentiment_labels = {0: "Negative", 1: "Neutral", 2: "Positive"}
+print(f"Predicted sentiment: {sentiment_labels[predicted_class]}")
+```
+
+---
+
+## Dataset
+
+This model is fine-tuned on the [syedkhalid076/Sentiment-Analysis-Over-sampled](https://huggingface.co/datasets/syedkhalid076/Sentiment-Analysis-Over-sampled) dataset. The dataset has been carefully preprocessed and oversampled to ensure balanced label representation and improve model performance.
+
+---
+
+## Performance
+
+The model was evaluated on a test set and achieved the following metrics:
+
+- **Accuracy**: 90.20% (0.9019906657776932)
+
+The evaluation strategy includes validation after each epoch and logging metrics for tracking training progress.
+
+---
+
+## Inference Endpoints
+
+You can use the Hugging Face Inference API to deploy and test this model in production environments.
+
+---
+
+
+## Author
+
+**Syed Khalid Hussain**  
+UX Designer & Developer  
+Specializing in crafting user-centric digital experiences.