ModernBERT-GoEmotions

Model Summary

ModernBERT-GoEmotions is a fine-tuned Transformer-based model for multi-label emotion classification.
Given a short text input, the model predicts one or more emotions from a predefined set of 27 emotions + neutral, as defined in the GoEmotions dataset.

The model is designed for applications requiring fine-grained emotional understanding, such as affective chat systems, moderation pipelines, and sentiment-aware conversational agents.

Model Details

Model Description

Developed by: Pradeep Kr. Mahato
Model type: Encoder-only Transformer (BERT-style)
Language(s): English
License: Apache 2.0
Fine-tuned from: answerdotai/ModernBERT-base
Task: Multi-label text classification
Output: Independent emotion probabilities via sigmoid activation

Model Sources

Base Model: https://huggingface.co/answerdotai/ModernBERT-base
Dataset: https://huggingface.co/datasets/google-research-datasets/go_emotions

Uses

Direct Use

The model can be used directly to:

Detect multiple emotions in short English texts
Analyze emotional tone in social media posts or chats
Power emotion-aware chat or moderation systems

Downstream Use

The model may be integrated into:

Conversational AI pipelines
Mental health or well-being analytics (non-diagnostic)
Emotion-aware recommendation systems
Research on affective computing

Out-of-Scope Use

The model is not intended for:

Medical or psychological diagnosis
Legal or forensic decision-making
High-stakes autonomous systems
Real-time moderation without human oversight

Bias, Risks, and Limitations

The training data is derived from Reddit, which may contain demographic, cultural, and topical biases
Emotion labels may be subjective and context-dependent
The model does not explicitly model emotion intensity or temporal dynamics
Predictions should be interpreted as probabilistic signals, not ground truth

Recommendations

Use human-in-the-loop validation for sensitive applications
Calibrate thresholds per emotion for production use
Avoid over-reliance on single-label interpretations

How to Get Started with the Model

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="your-username/ModernBERT-GoEmotions",
    return_all_scores=True
)

classifier("I feel anxious but also hopeful about the future.")

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Safetensors

Model size

0.1B params

Tensor type

F32