Varnikasiva/sentiment-classification-bert-mini

bert-mini-sentiment-analysis

Model Details

Model Description

This model is a fine-tuned BERT Mini model for sentiment analysis,using the Prajjwal BERT Mini architecture as the base. It classifies text into various emotional labels such as sadness, happiness, anger, and others, capturing a wide range of human sentiments. The model is designed to provide nuanced insights into emotional expressions across diverse contexts.

• Developed by: Varnika S
• Model type: Transformer
• Language(s) (NLP): English(en)
• License: MIT License
• Finetuned from model: Prajjwal's BERT Mini

Uses

This model is designed for sentiment analysis tasks, allowing users to classify text into various emotional labels such as sadness, happiness, anger, and others. It is suitable for applications in customer feedback analysis, social media monitoring, and mental health assessments, providing valuable insights into emotional responses.

Foreseeable Users:

Developers:
Those looking to integrate sentiment analysis into applications or services.

Researchers:
Academics studying emotional expressions in text data or working on NLP projects.

Businesses:
Companies wanting to analyze customer feedback or social media sentiment to improve products and services.

Mental Health Professionals:
Practitioners who can use the model to gauge emotional states based on textual data.

Direct Use

This model can be used directly for sentiment analysis tasks by leveraging the Hugging Face Transformers library. Users can input text data and receive sentiment classifications without the need for additional fine-tuning.

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Example Usage:

from transformers import pipeline

# Load the sentiment analysis pipeline
sentiment_analysis = pipeline("sentiment-analysis", model="your_huggingface_username/bert-mini-sentiment-analysis")

# Analyze sentiment
result = sentiment_analysis("I feel great today!")
print(result)  # Output: [{'label': 'happy'}]