luke-japanese-large-sentiment-analysis-wrime
| Property | Value |
|---|---|
| Base Model | LUKE-japanese-large-lite |
| Author | Mizuiro-sakura |
| Task | Sentiment Analysis |
| Language | Japanese |
| Dataset | WRIME |
| Paper | LUKE Paper |
What is luke-japanese-large-sentiment-analysis-wrime?
This is a specialized Japanese sentiment analysis model built on top of the LUKE (Language Understanding with Knowledge-based Embeddings) architecture. It's specifically fine-tuned to analyze text for eight distinct emotions: joy, sadness, anticipation, surprise, anger, fear, disgust, and trust. The model leverages LUKE's entity-aware self-attention mechanism to provide accurate emotion classification for Japanese text.
Implementation Details
The model is implemented using the LUKE architecture, which uniquely treats words and entities as independent tokens. It utilizes an entity-aware self-attention mechanism that extends the traditional transformer architecture by considering token types during attention score computation. The model supports sequences up to 512 tokens and requires PyTorch, SentencePiece, and an up-to-date version of the Transformers library.
- Built on LUKE-japanese-large-lite foundation
- Implements entity-aware self-attention mechanism
- Fine-tuned on the WRIME dataset for emotion classification
- Supports 8 distinct emotional categories
Core Capabilities
- Multi-class emotion classification for Japanese text
- Entity-aware contextual understanding
- Support for long-form text analysis (up to 512 tokens)
- Robust emotion detection across various text types
Frequently Asked Questions
Q: What makes this model unique?
This model combines LUKE's advanced entity-aware architecture with comprehensive emotion classification capabilities, specifically optimized for Japanese text. The integration of eight distinct emotional categories makes it particularly valuable for detailed sentiment analysis tasks.
Q: What are the recommended use cases?
The model is ideal for Japanese text emotion analysis in applications such as social media monitoring, customer feedback analysis, content moderation, and research in computational linguistics. It's particularly useful when detailed emotional understanding beyond simple positive/negative sentiment is required.
