💡 This model is part of the collection Frontier BERT Workhorses.

UltraBERT Go Emotions 2026-10

Overview

This model was fine-tuned from UltraBERT-150M on the GoEmotions dataset for multi-label classification. It predicts emotional states in text, with a total of 28 possible labels. Each input text can have one or more associated labels, reflecting the multi-label nature of the task.

🎭 Try the live demo →

---

Intended Use

The model is designed for emotion classification in English-language text, particularly in domains such as:

• Social media sentiment analysis
• Customer feedback evaluation
• Behavioral or psychological research

---

Example Usage

Here’s how to use the model with Hugging Face Transformers:

# requires transformers>=5.2:  pip install -U "transformers>=5.2"
from transformers import pipeline
import torch

# Load the model
classifier = pipeline(
    "text-classification", 
    model="cirimus/ultrabert-150M-go-emotions",
    top_k=5,
    trust_remote_code=True
)

text = "I am so happy and excited about this opportunity!"
predictions = classifier(text)

# Print top 5 detected emotions
print("\nTop 5 emotions detected:")
for pred in predictions[0]:  # Note: predictions is [[...]] format
    print(f"\t{pred['label']:10s} : {pred['score']:.3f}")

# Example output
# Top 5 emotions detected:
#        excitement : 0.937
#        joy        : 0.640
#        admiration : 0.021
#        gratitude  : 0.017
#        approval   : 0.015

---

Model Details

• Base Model: UltraBERT-150M
• Fine-Tuning Dataset: GoEmotions
• Number of Labels: 28
• Problem Type: Multi-label classification
• Language: English
• License: Apache 2.0
• Fine-Tuning Framework: Hugging Face Transformers
• Release: October 2026
• Version: 2026-10 (Optimized)

Training Improvements

Overall Results (threshold 0.5)

---

Dataset

The GoEmotions dataset is a multi-label emotion classification dataset derived from Reddit comments. It contains 58,000 examples with 28 emotion labels (e.g., admiration, amusement, anger, etc.), and it is annotated for multi-label classification.

---

Evaluation Results

The model was evaluated on the test split of the GoEmotions dataset, using a threshold of 0.5 for binarizing predictions. The overall metrics were:

Standard Results:

Using the default threshold of 0.5.

Optimal Results:

Using the best threshold for each label based on the training set (tuned on F1).

Confusion matrix:

---

Limitations and Biases

• Data Bias: The dataset is based on Reddit comments, which may not generalize well to other domains or cultural contexts.
• Underrepresented Classes: Certain labels like "grief" and "relief" have very few examples, leading to lower performance for those classes.
• Ambiguity: Some training data contain annotation inconsistencies or ambiguities that may impact predictions.

---

---

Environmental Impact

• Hardware Used: NVIDIA DGX Spark (GB10)
• Training Time: <1 hour
• Carbon Emissions: ~0.01 kg CO2 (calculated via ML CO2 Impact Calculator).

---

Citation

If you use this model, please cite it as follows:

@misc{junque2025emotion,
  title        = {Emotion and Bias Classification with Modern Encoders},
  author       = {Junqu\'e de Fortuny, Enric},
  year         = {2025},
  howpublished = {\url{https://huggingface.co/spaces/cirimus/emotion-detection}}
}