minilm-finetuned-emotion

MiniLM Emotion Classification Model

This model is fine-tuned from microsoft/MiniLM-L12-H384-uncased to classify text into 6 emotion categories.

It can detect emotional tone from user text such as joy, sadness, anger, fear, love, and surprise.

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Model Details

• Base Model: microsoft/MiniLM-L12-H384-uncased
• Model Type: Transformer Encoder
• Framework: PyTorch
• Task: Emotion Classification
• Parameters: 33.4M

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Supported Emotions

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Example Usage

Install dependencies

pip install transformers torch

Run inference

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="nikk118/minilm-finetuned-emotion"
)

result = classifier("I am really excited about this project")

print(result)

Output:

[{'label': 'joy', 'score': 0.99}]

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Training Details

Dataset used:

https://huggingface.co/datasets/emotion

Training configuration:

• Epochs: 5
• Batch size: 64
• Learning rate: 2e-5
• Loss function: Weighted CrossEntropyLoss
• Optimizer: AdamW
• Framework: Hugging Face Trainer API

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Architecture

Model pipeline:

Input Text
   ↓
Tokenizer
   ↓
MiniLM Transformer (12 layers)
   ↓
CLS token embedding
   ↓
Linear classification layer
   ↓
Emotion prediction

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Intended Use

This model can be used for:

• Chatbots
• Sentiment analysis
• Emotion detection
• Customer feedback analysis
• Social media analysis
• NLP applications

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Limitations

• Trained only on English text
• Performance depends on input quality
• May not generalize perfectly to domain-specific text

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Author

Created by: Nikk118
Hugging Face: https://huggingface.co/nikk118

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Model Source

Base model:

https://huggingface.co/microsoft/MiniLM-L12-H384-uncased

Dataset:

https://huggingface.co/datasets/emotion