paraphrase-xlm-r-multilingual-v1-fine-tuned-for-latin
| Property | Value |
|---|---|
| Author | silencesys |
| Model Type | Sentence Transformer |
| Vector Dimensions | 768 |
| Base Architecture | XLMRoberta |
| Model URL | Hugging Face Hub |
What is paraphrase-xlm-r-multilingual-v1-fine-tuned-for-latin?
This is a specialized sentence transformer model designed specifically for Latin text processing. It's built upon the XLM-RoBERTa architecture and fine-tuned to generate high-quality sentence embeddings for Latin text. The model maps sentences and paragraphs to a 768-dimensional dense vector space, making it particularly useful for semantic search, clustering, and similarity analysis of Latin texts.
Implementation Details
The model was trained using a denoising autoencoder loss function with carefully tuned hyperparameters, including a learning rate of 3e-05 and 9 training epochs. It utilizes a batch size of 8 and implements cls pooling for generating sentence embeddings.
- Maximum sequence length: 512 tokens
- Trained with AdamW optimizer
- Implements CLS token pooling strategy
- Features warmup steps: 10000
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Text clustering
- Cross-lingual understanding with focus on Latin
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically fine-tuned for Latin text processing, combining the power of XLM-RoBERTa's multilingual capabilities with specialized training for Latin language understanding. It's particularly valuable for classical text analysis and digital humanities projects.
Q: What are the recommended use cases?
The model is ideal for tasks involving Latin text analysis, including semantic search in classical texts, document clustering, similarity analysis of Latin passages, and automated text organization in digital classics projects.
