README.md

metadata

language: la
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - intertextuality
  - philbert
pipeline_tag: sentence-similarity
base_model: bowphs/SPhilBerta
library_name: sentence-transformers
widget:
  - source_sentence: >-
      omnia fert aetas, animum quoque; saepe ego longos cantando puerum memini
      me condere soles.
    sentences:
      - saepe ego longos cantando puerum memini me condere soles.
      - Arma virumque cano, Troiae qui primus ab oris
      - litora multum ille et terris iactatus et alto
    example_title: Jerome vs. candidates
  - source_sentence: Arma virumque cano, Troiae qui primus ab oris
    sentences:
      - arma virumque cano Troiae qui primus ab oris
      - Italiam fato profugus Laviniaque venit
      - litora multum ille et terris iactatus et alto
    example_title: Vergil variants and candidates
datasets:
  - latin-intertextuality
model-index:
  - name: SPhilBerta-emb-lat-intertext-v1
    results:
      - task:
          type: sentence-similarity
          name: Sentence Similarity
        dataset:
          type: latin-intertextuality
          name: Latin Intertextuality Dataset
        metrics:
          - type: recall_at_1
            value: 0.2819
            name: Recall@1
          - type: recall_at_5
            value: 0.367
            name: Recall@5
          - type: recall_at_10
            value: 0.3989
            name: Recall@10
          - type: recall_at_20
            value: 0.4521
            name: Recall@20
          - type: recall_at_100
            value: 0.5532
            name: Recall@100
          - type: recall_at_1000
            value: 0.75
            name: Recall@1000

Latin Intertextuality Embedding Model

This model is a fine-tuned version of SPhilBerta for generating embeddings of Latin texts to detect intertextual relationships between Jerome (Hieronymus) and other classical authors. This model is intended to integrate with the LociSimiles Python package for Latin intertextuality workflows: https://pypi.org/project/locisimiles/.

Model Description

• Task: Sentence embedding for detecting intertextual links between classical Latin authors
• Model type: Sentence Transformer (Embedding Model)
• Base model: SPhilBerta
• Max input tokens: 512
• Language: Latin
• License: Apache 2.0

Usage

This model generates dense vector embeddings for Latin text that can be used for semantic similarity tasks, particularly for detecting intertextual relationships. Important: This model was trained with prompts and should be used with the appropriate prompt names for optimal performance.

from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
import numpy as np

# Load model
model = SentenceTransformer("julian-schelb/SPhilBerta-emb-lat-intertext-v1")

# Example: Jerome text and candidates (1 positive match, 2 unrelated)
queries = [
    "omnia fert aetas, animum quoque; saepe ego longos cantando puerum memini me condere soles."
]
candidates = [
    "saepe ego longos cantando puerum memini me condere soles.",  # Positive match (subset of Jerome)
    "Gallia est omnis divisa in partes tres",  # Unrelated (Caesar)
    "in nova fert animus mutatas dicere formas"  # Unrelated (Ovid)
]

# Generate embeddings using prompt names
query_embeddings = model.encode(queries, prompt_name="query")
candidate_embeddings = model.encode(candidates, prompt_name="match")

# Calculate cosine similarity matrix
cosine_similarity_matrix = cos_sim(query_embeddings, candidate_embeddings)
print("Cosine Similarity Matrix:")
print("Query vs [Positive_Match, Caesar, Ovid]")
print(f"{cosine_similarity_matrix[0].numpy()}")
print(f"Highest similarity: {cosine_similarity_matrix[0].max():.4f} (index: {cosine_similarity_matrix[0].argmax()})")

# Alternative: Manual cosine similarity calculation
query_embedding = model.encode(queries[0], prompt_name="query")
candidate_embedding = model.encode(candidates[0], prompt_name="match")
cosine_sim = np.dot(query_embedding, candidate_embedding) / (np.linalg.norm(query_embedding) * np.linalg.norm(candidate_embedding))
print(f"\nDirect cosine similarity with positive match: {cosine_sim:.4f}")

Prompts

This model was trained with the following prompts:

• Query texts: Use prompt_name="query" (corresponds to "Query: " prefix)
• Candidate texts: Use prompt_name="match" (corresponds to "Candidate: " prefix)

For best results, always use the appropriate prompt names when encoding texts for similarity comparison.

Citation

TBD