Update README.md

README.md

@@ -7,4 +7,135 @@ metrics:
 - accuracy: 0.91789
 ---
 
-The model outputs 1 & 0. Where, 1 is "Positive" & 2 in "Negative".
+
+# Fine-Tuned RoBERTa Model for Sentiment Analysis
+
+## Overview
+
+This is a fine-tuned [RoBERTa](https://huggingface.co/docs/transformers/model_doc/robertal) model for sentiment analysis, trained on the [SST-2 dataset](https://huggingface.co/datasets/stanfordnlp/sst2). It classifies text into two sentiment categories:
+- **0**: Negative
+- **1**: Positive
+
+The model achieves an accuracy of **91.789%** on the SST-2 test set, making it a robust choice for sentiment classification tasks.
+
+---
+
+## Model Details
+
+- **Model architecture**: RoBERTa
+- **Dataset**: `stanfordnlp/sst2`
+- **Language**: English
+- **Model size**: 125 million parameters
+- **Precision**: FP32
+- **File format**: [SafeTensor](https://github.com/huggingface/safetensors)
+- **Tags**: Text Classification, Transformers, SafeTensors, SST-2, English, RoBERTa, Inference Endpoints
+
+---
+
+## Usage
+
+### Installation
+
+Ensure you have the necessary libraries installed:
+
+```bash
+pip install transformers torch safetensors
+```
+
+### Loading the Model
+
+The model can be loaded from Hugging Face's `transformers` library as follows:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the tokenizer and model
+model_name = "syedkhalid076/RoBERTa-Sentimental-Analysis-Model"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Example text
+text = "This is an amazing product!"
+
+# Tokenize input
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+
+# Perform inference
+outputs = model(**inputs)
+logits = outputs.logits
+predicted_class = logits.argmax().item()
+
+# Map the prediction to sentiment
+sentiments = {0: "Negative", 1: "Positive"}
+print(f"Sentiment: {sentiments[predicted_class]}")
+```
+
+---
+
+## Performance
+
+### Dataset
+
+The model was trained and evaluated on the **SST-2** dataset, which is widely used for sentiment analysis tasks.
+
+### Metrics
+
+| Metric   | Value    |
+|----------|----------|
+| Accuracy | 91.789%  |
+
+---
+
+## Deployment
+
+The model is hosted on Hugging Face and can be used directly via their [Inference Endpoints](https://huggingface.co/inference-endpoints).
+
+---
+
+## Applications
+
+This model can be used in a variety of applications, such as:
+- Customer feedback analysis
+- Social media sentiment monitoring
+- Product review classification
+- Opinion mining for research purposes
+
+---
+
+## Limitations
+
+While the model performs well on the SST-2 dataset, consider these limitations:
+1. It may not generalize well to domains with language or sentiment nuances different from the training data.
+2. It supports only binary sentiment classification (positive/negative).
+
+For fine-tuning on custom datasets or additional labels, refer to the [Hugging Face documentation](https://huggingface.co/docs/transformers/training).
+
+---
+
+## Model Card
+
+| **Feature**        | **Details**                                                                 |
+|---------------------|-----------------------------------------------------------------------------|
+| **Language**        | English                                                                    |
+| **Model size**      | 125M parameters                                                           |
+| **File format**     | SafeTensor                                                                 |
+| **Precision**       | FP32                                                                       |
+| **Dataset**         | stanfordnlp/sst2                                                          |
+| **Accuracy**         | 91.789%                                                         |
+
+---
+
+## Contributing
+
+Contributions to improve the model or extend its capabilities are welcome. Fork this repository, make your changes, and submit a pull request.
+
+---
+
+## Acknowledgments
+
+- The [Hugging Face Transformers library](https://github.com/huggingface/transformers) for model implementation and fine-tuning utilities.
+- The [Stanford Sentiment Treebank 2 (SST-2)](https://huggingface.co/datasets/stanfordnlp/sst2) dataset for providing high-quality sentiment analysis data.
+
+---
+
+**Author**: Syed Khalid Hussain