A fine-tuned DistilBERT model for emotion classification of English text. The model classifies text into 6 emotion categories: sadness, joy, love, anger, fear, and surprise. It was fine-tuned on the dair-ai/emotion dataset with random oversampling to handle class imbalance.

Developed by: Omar Emran Taha Al Maqousi
Model type: Transformer (DistilBERT) — Sequence Classification
Language(s): English
License: Apache 2.0
Fine-tuned from: distilbert-base-uncased

Model Sources

Repository: OmarMaqousi/distilbert-emotion-model

Uses

Direct Use

This model can be used directly for classifying the emotion expressed in a piece of English text. Suitable use cases include:

Analyzing customer feedback or reviews
Social media sentiment/emotion monitoring
Chatbot emotion detection
Mental health text screening (with caution)

Out-of-Scope Use

Non-English text: The model was trained only on English data.
Long documents: DistilBERT has a 512-token limit; longer texts will be truncated.
Clinical diagnosis: This model should NOT be used as a substitute for professional mental health assessment.
Detecting sarcasm or nuanced emotions: The model may not reliably detect sarcasm, irony, or complex mixed emotions.

Bias, Risks, and Limitations

The training data consists of English Twitter-like text, so the model may perform poorly on formal or domain-specific text.
Class distribution in the original dataset is imbalanced (e.g., surprise is underrepresented). Random oversampling was applied to mitigate this, but some bias may remain.
The model may reflect biases present in the training data.

Recommendations

Users should be aware that the model's predictions are probabilistic and may not always be accurate, especially on edge cases or ambiguous text. Always validate model outputs before using them in production or sensitive contexts.

How to Get Started with the Model

python

from transformers import pipeline

classifier = pipeline("text-classification", model="OmarMaqousi/distilbert-emotion-model")

result = classifier("I am so happy today!")
print(result)
# [{'label': 'joy', 'score': 0.98}]

Or load the model and tokenizer manually:

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model = AutoModelForSequenceClassification.from_pretrained("OmarMaqousi/distilbert-emotion-model")
tokenizer = AutoTokenizer.from_pretrained("OmarMaqousi/distilbert-emotion-model")

text = "I feel really scared about the exam"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    predicted_class = torch.argmax(outputs.logits, dim=-1).item()

labels = ["sadness", "joy", "love", "anger", "fear", "surprise"]
print(f"Predicted emotion: {labels[predicted_class]}")

Training Details

Training Data

The model was fine-tuned on the dair-ai/emotion dataset, which contains ~20,000 English text samples labeled with 6 emotions:

Label	Emotion
0	sadness
1	joy
2	love
3	anger
4	fear
5	surprise

Data balancing: Random oversampling was applied to the training set to address class imbalance, ensuring the model sees a balanced distribution of all emotion classes during training.

Training Procedure

Preprocessing

Tokenization using distilbert-base-uncased tokenizer
Max sequence length: 512 tokens
Truncation and padding applied

Training Hyperparameters

Hyperparameter	Value
Learning rate	5e-5
Batch size (train)	64
Batch size (eval)	64
Max epochs	50
Actual epochs (early stop)	9
Weight decay	0.02
Optimizer	AdamW
LR Scheduler	Cosine
Warmup ratio	0.1
Early stopping patience	10 (based on F1)
Best model selection	Best weighted F1 on validation
Training regime	fp32

Training & Validation Results

Epoch	Training Loss	Validation Loss	Accuracy	F1
1	0.936842	0.226138	0.9210	0.921069
2	0.189488	0.176853	0.9345	0.933206
3	0.124524	0.138500	0.9360	0.936486
4	0.088845	0.144183	0.9395	0.939676
5	0.066814	0.153446	0.9390	0.938947
6	0.049265	0.192850	0.9410	0.940797
7	0.029422	0.208450	0.9485	0.948557
8	0.015454	0.225589	0.9450	0.945127
9	0.010990	0.227848	0.9460	0.946137

Evaluation

Testing Data

The evaluation was performed on the test split of the dair-ai/emotion dataset (2,000 samples).

Metrics

Metric	Validation (Best)	Test
Accuracy	0.9485	0.9270
F1 Score	0.9486	0.9274

Technical Specifications

Model Architecture and Objective

Architecture: DistilBERT (6 layers, 768 hidden size, 12 attention heads, 66M parameters)
Objective: Multi-class classification (6 emotion categories) with cross-entropy loss

Compute Infrastructure

Hardware: Google Colab (GPU — T4 or similar)
Software: Hugging Face Transformers, PyTorch, Datasets

Citation

If you use this model, please cite:

bibtex

@misc{maqousi2026distilbert-emotion,
  author = {Omar Al Maqousi},
  title = {DistilBERT Emotion Classification Model},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/OmarMaqousi/distilbert-emotion-model}
}

Model Card Authors

Omar Emran Taha Al Maqousi

Model Card Contact

Hugging Face: @OmarMaqousi