Model Card for Model ID
The "Ishagupta2010/sentiment-ai" model is a sentiment classification model based on RoBERTa fine-tuned on the GoEmotions dataset. It classifies text into 27 emotion labels (e.g., happiness, sadness, anger, etc.) along with a neutral category.
Key Highlights:
Task: Text classification for emotion detection.
Input: A piece of text (e.g., "I am feeling very happy today!").
Output: Predicted emotion(s) and confidence scores (e.g., {'label': 'joy', 'score': 0.95}).
This model is ideal for understanding emotional tone in user text, such as social media comments, reviews, or chatbot responses.
Model Details
Architecture: RoBERTa (a variant of BERT)
Dataset: GoEmotions dataset (27 emotion categories)
Task: Text classification for emotion detection
Input: Raw text (e.g., sentences or phrases)
Output: Emotion label(s) with confidence scores
Model Description
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Isha Gupta
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- Model type: RoBERTa-based transformer model
- Language(s) (NLP): English
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- Finetuned from model [optional]: RoBERTa (pretrained model)
Model Sources [optional]
- Repository: https://huggingface.co/Ishagupta2010/sentiment-ai
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Uses
The RoBERTa-based model fine-tuned on the GoEmotions dataset is primarily used for sentiment analysis, specifically for:
- Emotion Classification: The model is capable of identifying emotions in text, such as joy, sadness, anger, fear, etc., making it useful for analyzing customer feedback, reviews, or social media content.
- Text Sentiment Analysis: It can classify the sentiment expressed in a text (positive, negative, neutral), useful for sentiment-driven applications in marketing, product development, or customer service.
- Opinion Mining: It helps in understanding public opinion from textual data, which can be beneficial in various sectors like politics, entertainment, and media.
- Mental Health Monitoring: It can be applied to detect emotional states in conversational data, which may be used in mental health applications to assess emotional well-being.
This model is highly useful for any application requiring emotional tone analysis or sentiment detection in text.
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