all-roberta-large-v1
| Property | Value |
|---|---|
| Parameter Count | 355M |
| License | Apache 2.0 |
| Framework | PyTorch/Sentence-Transformers |
| Training Data | 1B+ sentence pairs |
What is all-roberta-large-v1?
all-roberta-large-v1 is a powerful sentence embedding model built on RoBERTa-large architecture, designed to map sentences and paragraphs into a 1024-dimensional dense vector space. Developed during the Hugging Face Community Week, this model was trained on an impressive dataset of over 1 billion sentence pairs, making it particularly effective for semantic search and clustering tasks.
Implementation Details
The model builds upon the roberta-large architecture and was fine-tuned using a contrastive learning objective. Training was conducted on TPU v3-8 hardware for 400k steps with a batch size of 256, using the AdamW optimizer with a 2e-5 learning rate and 500-step warmup period.
- Outputs 1024-dimensional embeddings
- 128 token sequence length limit
- Efficient mean pooling implementation
- Supports both sentence-transformers and HuggingFace Transformers APIs
Core Capabilities
- Sentence and paragraph embedding generation
- Semantic similarity computation
- Information retrieval
- Text clustering
- Cross-lingual alignment
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness comes from its extensive training on over 1 billion sentence pairs from diverse sources, including Reddit comments, scientific papers, and question-answer pairs. This broad training data makes it particularly robust for general-purpose semantic similarity tasks.
Q: What are the recommended use cases?
The model excels in tasks requiring semantic understanding of text, including document similarity comparison, semantic search implementations, clustering of text documents, and information retrieval systems. It's particularly well-suited for applications requiring high-quality sentence embeddings.
