Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,76 @@
+---
+license: cc-by-4.0
+task_categories:
+- question-answering
+- text-generation
+language:
+- en
+tags:
+- adaption-autoscientist
+- adapted-dataset
+size_categories:
+- 1K<n<10K
+---
+
+# Agriculture Advisor (Adaption-adapted) v1
+
+The **adapted** dataset used to fine-tune our agricultural advisory model for the
+[Adaption AutoScientist Challenge](https://adaptionlabs.ai/blog/autoscientist-challenge).
+
+Produced by running [`15juneee/agriculture-advisor-seed-v1`](https://huggingface.co/datasets/15juneee/agriculture-advisor-seed-v1) through
+Adaption's `datasets.run`. The seed carries the prompts and the curation; this carries
+the completions the model was actually trained on.
+
+## Rows
+
+17,039 rows. Adaption writes its output to `enhanced_prompt` / `enhanced_completion`
+and leaves the uploaded `prompt` / `completion` columns intact, so both the input and
+the adapted output are inspectable side by side.
+
+Median adapted completion length: **6,317 characters**.
+
+## Adaptation configuration
+
+| Setting | Value |
+|---|---|
+| `brand_controls.blueprint` | domain blueprint (see below) |
+| `brand_controls.hallucination_mitigation` | **true** (web-search grounding) |
+| `brand_controls.length` | `detailed` |
+| `recipes.deduplication` | true |
+| `recipes.prompt_rephrase` | true |
+| `recipes.reasoning_traces` | true |
+| `training_type` | `instruction_dataset` |
+
+## Why the adaptation mattered, measured
+
+We first fine-tuned on the **raw** seed and the resulting models *lost* to their own
+base model - 28.7% and 42.5% win rates over 200 held-out pairs. Scoring the raw training
+completions with the same behavioural checks used on model output explained why: the
+data was worse than the base on the very behaviours the blueprint specifies.
+
+| Check | Raw seed | Base model | **Adapted** |
+|---|---|---|---|
+| well-structured output | 47% | 94% | **100%** |
+| hedges / flags uncertainty | 35% | 37-54% | **85%** |
+| asks for missing detail | 0% | 20% | **18%** |
+| invents a specific policy (lower is better) | 0% | 0% | **0%** |
+
+Adaption's own dataset-quality evaluation on this run reported average message quality
+rising from **6.66 to 8.52**, with average completion quality **9.72** (median 10).
+
+The training targets are now better than the base model on the measured rubric, which is
+the condition the earlier runs failed.
+
+## Provenance and licence
+
+Derived from the seed dataset, which composes CC0-1.0, Apache-2.0, MIT and CC-BY-4.0
+sources; see the seed card for the full attribution table. Released under **CC-BY-4.0**,
+the most restrictive of the upstream licences.
+
+## Limitations
+
+Completions are model-generated. `hallucination_mitigation` grounds generation in web
+search, which reduces fabrication but does not eliminate it, and no row was
+expert-reviewed. Treat this as high-quality advisory *text*, not verified fact.
+
+Not a substitute for local agricultural extension services. Any pesticide, herbicide or veterinary guidance must be checked against the current product label and local regulations.