LociSimiles is a Python package for finding intertextual links in Latin literature using pre-trained language models.

# Load example query and source documents
query_doc = Document("../data/hieronymus_samples.csv")
source_doc = Document("../data/vergil_samples.csv")

# Load the pipeline with pre-trained models
pipeline = ClassificationPipelineWithCandidategeneration(
    classification_name="...",
    embedding_model_name="...",
    device="cpu",
)

# Run the pipeline with the query and source documents
results = pipeline.run(
    query=query_doc,    # Query document
    source=source_doc,  # Source document
    top_k=3             # Number of top similar candidates to classify
)

pretty_print(results)

LociSimiles provides a command-line tool for running the pipeline directly from the terminal:

locisimiles query.csv source.csv -o results.csv

locisimiles query.csv source.csv -o results.csv \
  --classification-model julian-schelb/PhilBerta-class-latin-intertext-v1 \
  --embedding-model julian-schelb/SPhilBerta-emb-lat-intertext-v1 \
  --top-k 20 \
  --threshold 0.7 \
  --device cuda \
  --verbose

• Input/Output:

  • query: Path to query document CSV file (columns: seg_id, text)
  • source: Path to source document CSV file (columns: seg_id, text)
  • -o, --output: Path to output CSV file for results (required)

• Models:

  • --classification-model: HuggingFace model for classification (default: PhilBerta-class-latin-intertext-v1)
  • --embedding-model: HuggingFace model for embeddings (default: SPhilBerta-emb-lat-intertext-v1)

• Pipeline Parameters:

  • -k, --top-k: Number of top candidates to retrieve per query segment (default: 10)
  • -t, --threshold: Classification probability threshold for filtering results (default: 0.5)

• Device:

  • --device: Choose auto, cuda, mps, or cpu (default: auto-detect)

• Other:

  • -v, --verbose: Enable detailed progress output
  • -h, --help: Show help message

The CLI saves results to a CSV file with the following columns:

• query_id: Query segment identifier
• query_text: Query text content
• source_id: Source segment identifier
• source_text: Source text content
• similarity: Cosine similarity score (0-1)
• probability: Classification confidence (0-1)
• above_threshold: "Yes" if probability ≥ threshold, otherwise "No"

Install the optional GUI extra to experiment with a minimal Gradio front end:

pip install locisimiles[gui]

Launch the interface from the command line:

locisimiles-gui