lzw1008/Emollama-chat-7b
lzw1008/Emollama-chat-7b is a 7 billion parameter instruction-following large language model, fine-tuned by lzw1008 based on Meta's LLaMA2-chat-7B. It is specifically designed for comprehensive affective analysis, including tasks like sentiment polarity, categorical emotion classification, and emotion intensity regression. This model excels at understanding and quantifying emotional states within text, making it suitable for applications requiring nuanced emotional intelligence.
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Emollama-chat-7b: Affective Analysis LLM
Emollama-chat-7b is a 7 billion parameter model from the EmoLLMs project, developed by lzw1008. It is fine-tuned on the Meta LLaMA2-chat-7B foundation model using the full AAID instruction tuning data, making it the first open-source LLM series specifically for comprehensive affective analysis with instruction-following capabilities.
Key Capabilities
- Affective Classification: Categorizes text into sentimental polarity (e.g., positive, negative) or specific categorical emotions (e.g., joy, anger).
- Affective Regression: Assigns numerical scores for sentiment strength or emotion intensity, allowing for granular analysis.
- Instruction Following: Responds to prompts for various affective analysis tasks, such as emotion intensity scoring, sentiment strength evaluation, and sentiment/emotion classification.
Good for
- Sentiment Analysis: Determining the overall positive, negative, or neutral sentiment of text.
- Emotion Detection: Identifying specific emotions expressed in written content.
- Emotion Intensity Scoring: Quantifying the strength of emotions or sentiment on a numerical scale.
- Research in Affective Computing: Providing a specialized tool for studying and applying emotional intelligence in NLP tasks.
Ethical Considerations
It is important to note that, like other LLMs, Emollama-chat-7b may exhibit biases (e.g., gender gaps) and can produce incorrect or over-generalized predictions. Users should be aware of these potential risks when applying the model to real-world affective analysis systems.