Ishagupta2010/sentiment-ai

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
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: RoBERTa-based transformer model
• Language(s) (NLP): English
• License: [More Information Needed]
• Finetuned from model [optional]: RoBERTa (pretrained model)

Model Sources [optional]

• Repository: https://huggingface.co/Ishagupta2010/sentiment-ai
• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

The RoBERTa-based model fine-tuned on the GoEmotions dataset is primarily used for sentiment analysis, specifically for:

1. 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.
2. 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.
3. Opinion Mining: It helps in understanding public opinion from textual data, which can be beneficial in various sectors like politics, entertainment, and media.
4. 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.

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

• Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: [More Information Needed]
• Hours used: [More Information Needed]
• Cloud Provider: [More Information Needed]
• Compute Region: [More Information Needed]
• Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]