lzw1008/Emollama-chat-13b

Emollama-chat-13b: Affective Analysis LLM

Emollama-chat-13b is a 13 billion parameter model from the EmoLLMs project, developed by lzw1008, focusing on comprehensive affective analysis. It is fine-tuned from the Meta LLaMA2-chat-13B foundation model using the full AAID instruction tuning data.

Key Capabilities

• Affective Classification: Performs tasks such as sentimental polarity and categorical emotion identification.
• Affective Regression: Capable of regression tasks like sentiment strength and emotion intensity scoring.
• Instruction Following: Designed to follow instructions for various affective analysis tasks, as demonstrated by prompt examples for emotion intensity, sentiment strength, and sentiment/emotion classification.

Ethical Considerations

The developers acknowledge potential biases, incorrect predictions, and over-generalization risks inherent in LLMs, advising caution when applying the model to real-world affective analysis systems.

Usage

This model can be easily integrated into Python projects using the Hugging Face Transformers library, with examples provided for loading the tokenizer and model.

Good For

• Developers and researchers requiring an LLM specifically fine-tuned for detailed emotional understanding.
• Applications involving sentiment analysis, emotion detection, and intensity scoring based on textual input.
• Instruction-based affective analysis tasks where precise emotional classification or regression is needed.