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Model: wallfacers/weft-lineage-extractor-jvm-1.5b
Source: Original Platform

README.md

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+---
+license: other
+license_name: weft-research
+license_link: https://github.com/wallfacers/data-weave
+pipeline_tag: text-generation
+library_name: transformers
+base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+datasets:
+- wallfacers/weft-script-lineage-synth
+language:
+- en
+tags:
+- research-artifact
+- negative-result
+- memorization
+- domain-shift
+- cross-language
+- data-lineage
+- etl
+- lora
+model-index:
+- name: weft-lineage-extractor-jvm-1.5b
+  results:
+  - task:
+      type: table-level-lineage-extraction
+      name: ETL table-level data-lineage extraction (Scala/Java)
+    dataset:
+      type: real-jvm-etl
+      name: real Scala/Java ETL (human gold, n=141)
+    metrics:
+    - type: precision
+      value: 0.165
+      name: Table precision (real JVM, out-of-distribution)
+    - type: accuracy
+      value: 0.418
+      name: Read/write direction accuracy (real JVM)
+---
+
+# weft-lineage-extractor-jvm-1.5b — cross-language reproduction of a NEGATIVE RESULT
+
+> ## ⚠️ RESEARCH ARTIFACT — the Scala/Java (JVM) point of a *synthetic-only* training study. Not a production tool.
+>
+> ### ✅ Resolved by real data: use **[weft-lineage-extractor-3b](https://huggingface.co/wallfacers/weft-lineage-extractor-3b)** (real corpus, real precision **0.64**). Full study: **[weft-lineage-extractor-1.5b](https://huggingface.co/wallfacers/weft-lineage-extractor-1.5b)**.
+
+The **JVM (Scala/Java)** point of a study showing that **synthetic-only training** induces a
+**verbatim memorization leak** in small ETL table-lineage models — and that the failure
+**reproduces, worse, across languages**. Synthetic held-out looks near-perfect (~0.99); on real
+Scala/Java ETL it drops to **precision 0.165**, with **40.8%** of hallucinations being table names
+recited verbatim from the synthetic training pool — the **highest leak** of all scale/language
+points, because real JVM code is the most out-of-distribution.
+
+Same recipe as the [1.5B main model](https://huggingface.co/wallfacers/weft-lineage-extractor-1.5b)
+(LoRA on Qwen2.5-Coder-1.5B), extended with synthetic Scala/Java forms; `task_type: SCALA | JAVA`.
+
+## This point's numbers (table-level, Convention A)
+
+| metric | synthetic held-out | real Scala/Java ETL (n=141, non-empty 28) |
+|---|---|---|
+| precision | ~0.99 | **0.165** |
+| direction accuracy | — | **0.418** |
+| **verbatim memorization leak** | — | **40.8%** |
+
+The leak metric is **gold-independent** and stable to full-file re-adjudication (verbatim
+40.4% → 40.8%). Given a real `SqlDao.java` it cannot parse, the model emits training-pool names
+like `dws.dws_member_point_df` — memorized generator vocabulary, not a label artifact.
+
+## Where it fits (scale / cross-language curve)
+
+| point | real precision | real direction | verbatim leak |
+|---|---|---|---|
+| 0.5B (Py/Sh) | 0.243 | 0.369 | 37.4% |
+| 1.5B (Py/Sh) | 0.270 | 0.496 | 22.4% |
+| 3B (Py/Sh, synthetic) | 0.325 | 0.468 | 10.9% |
+| **1.5B + JVM (this, real JVM)** | **0.165** | **0.418** | **40.8%** |
+| **3B (real corpus)** | **0.64** | — | **~0** |
+
+"Add another language" does not fix the real-world gap; **real training data does**.
+
+## Intended use
+
+- ✅ Reproducing / studying the cross-language reproduction of the memorization-leak failure.
+- ❌ Not for production lineage — use the [real-corpus 3B](https://huggingface.co/wallfacers/weft-lineage-extractor-3b).
+
+## Usage, prompt format, training details, citation
+
+Identical to the main model (this variant adds `task_type: SCALA | JAVA` and JVM system-prompt wording):
+**[weft-lineage-extractor-1.5b](https://huggingface.co/wallfacers/weft-lineage-extractor-1.5b)**.
+
+- **Dataset & reports:** [wallfacers/weft-script-lineage-synth](https://huggingface.co/datasets/wallfacers/weft-script-lineage-synth)
+- **Base model:** [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct)
+- **Platform:** [Weft (data-weave)](https://github.com/wallfacers/data-weave)