syedkhalid076/RoBERTa-Sentimental-Analysis-v2

RoBERTa Sentiment Analysis Model v2

This repository hosts a fine-tuned RoBERTa 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 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.

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Model Details

• Base Model: 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
• Library: Transformers
• File Format: Safetensors

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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.

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Installation

To use this model, ensure you have the transformers library installed:

pip install transformers

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Usage

Here’s how you can load the model and tokenizer and perform inference:

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]}")

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Dataset

This model is fine-tuned on the syedkhalid076/Sentiment-Analysis-Over-sampled dataset. The dataset has been carefully preprocessed and oversampled to ensure balanced label representation and improve model performance.

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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.

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Inference Endpoints

You can use the Hugging Face Inference API to deploy and test this model in production environments.

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Author

Syed Khalid Hussain
UX Designer & Developer
Specializing in crafting user-centric digital experiences.