E-motionAssistant/English_Text_Emotion_Recogniton_Model

English Text Emotion Recognition Model

Fine-tuned RoBERTa-style model for multi-class emotion classification in English text.

This model was trained for 6 epochs on an English emotion dataset and achieves modest validation performance (~90% accuracy). It is suitable as a starting point for English emotion detection tasks, but would benefit from longer training, more data, or a better-suited base model.

Model Details

Model Description

• —Developed by: Bimsara Serasinghe
• —Shared by: Bimsara Serasinghe
• —Model type: Text Classification (fine-tuned transformer for multi-class emotion detection)
• —Language(s) (NLP): English
• —License: Apache-2.0
• —Finetuned from model: j-hartmann/emotion-english-distilroberta-base

Model Sources

• —Repository: https://huggingface.co/ShanukaB/EnglishTextEmotionRecognitonModel

Uses

Direct Use

Use the model directly with Hugging Face pipeline to classify English sentences into emotion categories.

Downstream Use

• —Building emotion-aware English chatbots
• —Social media emotion/sentiment monitoring (Twitter/X, Reddit, comments)
• —Mental health & wellbeing tools with emotion detection
• —Customer support & feedback analysis
• —Academic/research experiments in English affective computing

Out-of-Scope Use

• —High-stakes automated decisions (mental health diagnosis, hiring, legal)
• —Safety-critical real-time systems without thorough validation
• —Non-English languages (poor generalization expected)

Recommendations

• —Use model outputs only as a signal — combine with human judgment in sensitive contexts
• —Fine-tune further (more epochs, larger/cleaner dataset, or emotion-specialized base like roberta-base-go_emotions)
• —Evaluate on your specific use-case domain before production

How to Get Started with the Model

from transformers import pipeline
import joblib

# Load the fine-tuned model
classifier = pipeline(
    "text-classification",
    model="YOUR_USERNAME/YOUR_MODEL_NAME",
    tokenizer="YOUR_USERNAME/YOUR_MODEL_NAME"
)

# Optional: load saved label encoder (if uploaded to repo)
# label_encoder = joblib.load("label_encoder.pkl")

texts = [
    "I'm so happy today! 🎉",
    "This is really making me angry...",
    "I feel so scared right now 😨",
    "This is disgusting, I can't believe it."
]

for text in texts:
    result = classifier(text)[0]
    # If labels are saved as "LABEL_0", "LABEL_1", etc.
    # num_label = int(result["label"].split("_")[-1])
    # emotion = label_encoder.inverse_transform([num_label])[0] if label_encoder else result["label"]
    print(f"Text: {text}")
    print(f"→ {result['label']} (confidence: {result['score']:.3f})\n")