RoBERTa-Sentimental-Analysis-v1

Fine-Tuned RoBERTa Model for Sentiment Analysis

Overview

This is a fine-tuned RoBERTa model for sentiment analysis, trained on the SST-2 dataset. 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.

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Model Details

• Model architecture: RoBERTa
• Dataset: stanfordnlp/sst2
• Language: English
• Model size: 125 million parameters
• Precision: FP32
• File format: SafeTensor
• Tags: Text Classification, Transformers, SafeTensors, SST-2, English, RoBERTa, Inference Endpoints

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Usage

Installation

Ensure you have the necessary libraries installed:

pip install transformers torch safetensors

Loading the Model

The model can be loaded from Hugging Face's transformers library as follows:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load the tokenizer and model
model_name = "syedkhalid076/RoBERTa-Sentimental-Analysis-v1"
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]}")

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Performance

Dataset

The model was trained and evaluated on the SST-2 dataset, which is widely used for sentiment analysis tasks.

Metrics

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Deployment

The model is hosted on Hugging Face and can be used directly via their Inference Endpoints.

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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

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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.

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Model Card

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Contributing

Contributions to improve the model or extend its capabilities are welcome. Fork this repository, make your changes, and submit a pull request.

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Acknowledgments

• The Hugging Face Transformers library for model implementation and fine-tuning utilities.
• The Stanford Sentiment Treebank 2 (SST-2) dataset for providing high-quality sentiment analysis data.

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Author: Syed Khalid Hussain