| --- |
| 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`. |
|
|
| ```python |
| 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 |
|
|
