metadata
license: apache-2.0
library_name: sentence-transformers
pipeline_tag: sentence-similarity
base_model: unsloth/embeddinggemma-2
tags:
- sentence-transformers
- feature-extraction
MoMa analytical pattern embedder
EmbeddingGemma 2 fine-tuned to match a user's task to the Analytical Pattern (AP) that answers it, for the search of the DataGEMS MoMa Management service. Text only, 768 dimensions.
It is a LoRA adapter merged into unsloth/embeddinggemma-2 at revision c8f89cc7ecb4ba5122cd15fdd487422b2b487daf.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("<this repository>", config_kwargs={"vision_config": None, "audio_config": None})
task = model.encode_query("Which students are at risk of failing?")
aps = model.encode_document(["Translates a natural-language string into a safe SQL query, ..."])
Queries and AP texts are given different prefixes, so use encode_query and
encode_document rather than encode. The AP text the model was trained on is the one
AnalyticalPattern.describe() builds from an AP's operators, not the AP's own description.
Dataset, training and benchmark: https://github.com/datagems-eosc/moma-management/tree/main/packages/analytical-pattern-embedder