Embeddings
agent Temporal Evolution
118
Total Embedding Vectors
7
Documents Embedded
0
Period-Aware
1
Methods Used
Document Embeddings 7 documents
About Embeddings
What are Embeddings?
Embeddings are dense vector representations of text that capture semantic meaning. Similar texts have similar embeddings, enabling semantic search and comparison.
Period-Aware Models
- Pre-1850: all-mpnet-base-v2 (768 dims)
- 1850-1950: all-mpnet-base-v2 (768 dims)
- 1950-2000: all-MiniLM-L6-v2 (384 dims)
- 2000+: all-roberta-large-v1 (1024 dims)
Usage
Period-aware embeddings select models optimized for historical eras, enabling accurate semantic comparison across time periods.