Shahzad1/go_emotions_model_bert

Model Card for GoEmotions Model BERT

This model is a fine-tuned DistilBERT model for emotion detection using the simplified GoEmotions dataset provided by Google.

It is capable of classifying English text into multiple emotions from the GoEmotions label set.

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Model Details

Model Description

This is a fine-tuned distilbert-base-uncased model on the simplified version of the GoEmotions dataset for multi-label emotion classification.

• Developed by: Shahzad Sohail
• Funded by [optional]: Self-funded
• Shared by [optional]: Shahzad Sohail
• Model type: DistilBERT (fine-tuned for multi-label classification)
• Language(s) (NLP): English
• License: Apache 2.0
• Finetuned from model [optional]: distilbert-base-uncased

Model Sources

• Repository: [Optional: Add link if you have a GitHub repository]
• Dataset: https://huggingface.co/datasets/google-research-datasets/go_emotions
• Demo [optional]: [Add Gradio/Hugging Face Space if applicable]

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Uses

Direct Use

This model can be directly used for:

• Emotion classification in social media comments
• Sentiment analysis with fine-grained emotions
• Enhancing chatbot emotional understanding
• Social listening and brand monitoring

Downstream Use [optional]

The model can be further fine-tuned for specialized domains such as healthcare, education, or customer support.

Out-of-Scope Use

• Not suitable for clinical or psychological diagnosis.
• Not designed for non-English text.
• Should not be used in critical decision-making without human oversight.

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Bias, Risks, and Limitations

The model inherits biases from the GoEmotions dataset:

• May underperform on non-standard English, slang, or low-resource dialects.
• May misclassify ambiguous or sarcastic text.
• Model's emotion predictions should be verified in sensitive contexts.

Recommendations

• Use the model with awareness of its biases and limitations.
• Apply additional human review in high-stakes or sensitive applications.

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How to Get Started with the Model

You can load and use the model with the Hugging Face transformers pipeline:

from transformers import pipeline

classifier = pipeline("text-classification", model="Shahzad1/go_emotions_model_bert", top_k=None)

text = "I am feeling very happy today!"
results = classifier(text)
print(results)