locisimiles 1.6.0

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

Loci Similes

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

Basic Usage

# 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)

# Save results to CSV or JSON
pipeline.to_csv("results.csv")
pipeline.to_json("results.json")

Command-Line Interface

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

Basic Usage

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

Two-Stage Pipeline Example

locisimiles query.csv source.csv -o results.csv \
  --pipeline two-stage \
  --classification-model julian-schelb/xlm-roberta-large-class-lat-intertext-v1 \
  --embedding-model julian-schelb/multilingual-e5-large-emb-lat-intertext-v1 \
  --top-k 20 \
  --threshold 0.85 \
  --device cuda \
  --verbose

Word2Vec Retrieval Example

locisimiles query.csv source.csv -o results.csv \
  --pipeline word2vec-retrieval \
  --word2vec-model-path ./models/latin_w2v_bamman_lemma300_100_1.model \
  --word2vec-interval 2 \
  --word2vec-order-free \
  --top-k 20 \
  --threshold 0.85

Latin BERT Retrieval Example (Gong-Style)

locisimiles query.csv source.csv -o results.csv \
  --pipeline latin-bert-retrieval \
  --latin-bert-model ashleygong03/bamman-burns-latin-bert \
  --top-k 20 \
  --threshold 0.85

If --word2vec-model-path is not provided, the CLI expects a local model at:

models/latin_w2v_bamman_lemma300_100_1.model

Word2Vec mode requires pre-lemmatized input in the same CSV format (seg_id, text).

Options

• 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: xlm-roberta-large-class-lat-intertext-v1)
  • --embedding-model: HuggingFace model for embeddings (default: multilingual-e5-large-emb-lat-intertext-v1)
  • --word2vec-model-path: Local path to a gensim .model file (Word2Vec pipeline)

• Pipeline Parameters:

  • --pipeline: Select two-stage or word2vec-retrieval (default: two-stage)
  • -k, --top-k: Number of top candidates to retrieve per query segment (default: 10)
  • -t, --threshold: Decision threshold for output filtering (default: 0.85)
  • --word2vec-interval: Max token gap for Word2Vec bigrams (default: 0)
  • --word2vec-order-free: Enable order-insensitive Word2Vec bigrams

• Device:

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

• Other:

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

Output Format

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"

Optional Gradio GUI

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

In the GUI, choose Word2Vec Retrieval (Burns-Style) in Pipeline Configuration to enable Word2Vec controls:

• Word2Vec Model Path: local gensim .model file
• Bigram Interval: token gap for bigram generation
• Order-Free Bigrams: optional order-insensitive matching

If the model path is invalid or missing, processing fails with a clear error message.