ko-sroberta-multitask
| Property | Value |
|---|---|
| Vector Dimensions | 768 |
| Architecture | RoBERTa with Mean Pooling |
| Paper | Research Paper |
| Downloads | 1,316,752 |
What is ko-sroberta-multitask?
ko-sroberta-multitask is a specialized Korean language model based on RoBERTa architecture, designed for generating meaningful sentence embeddings. It maps Korean sentences and paragraphs to 768-dimensional dense vector spaces, making it particularly effective for tasks like semantic similarity computation and text clustering.
Implementation Details
The model implements a sophisticated architecture combining RoBERTa with mean pooling strategy. It was trained using multiple tasks (KorSTS and KorNLI datasets) with a carefully tuned learning rate of 2e-05 and warmup steps of 360. The training process utilized AdamW optimizer with weight decay of 0.01.
- Supports maximum sequence length of 128 tokens
- Implements mean pooling over token embeddings
- Trained with MultipleNegativesRankingLoss and CosineSimilarityLoss
Core Capabilities
- Sentence similarity computation with high accuracy (84.77% Cosine Pearson)
- Dense vector generation for Korean text
- Support for both sentence-transformers and HuggingFace implementations
- Efficient semantic search and clustering capabilities
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its multi-task training approach combining KorSTS and KorNLI datasets, resulting in robust sentence embeddings specifically optimized for Korean language understanding. Its high performance metrics across different similarity measures (Cosine, Euclidean, Manhattan) make it particularly reliable for production use.
Q: What are the recommended use cases?
The model excels in applications requiring semantic similarity computation, including: document clustering, semantic search engines, text similarity analysis, and content recommendation systems. It's particularly well-suited for Korean language processing tasks requiring deep semantic understanding.
