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README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: mpi-inno-comp/paecter
+library_name: transformers
+pipeline_tag: text-classification
+tags:
+- patents
+- plant-patents
+- plant-breeding
+- text-classification
+- paecter
+- reproducibility
+---
+
+# PAEcTER Plant-Related Patent-Family Detector
+
+This is the first-stage production classifier for the manuscript *Identifying
+Plant-Related Patents: Corpus Construction and Global Patterns*. It fine-tunes
+`mpi-inno-comp/paecter` to classify English patent-family title and abstract
+text as plant-related or not plant-related.
+
+This private draft release is prepared for review before public publication.
+The Apache-2.0 metadata follows the base model license and should be confirmed
+before making the repository public.
+
+## Model Details
+
+- Base model: `mpi-inno-comp/paecter`
+- Architecture: `BertForSequenceClassification`
+- Task: binary text classification
+- Input text: English title, blank line, English abstract
+- Maximum length: 512 tokens
+- Labels:
+  - `no`: not plant-related
+  - `yes`: plant-related
+- Recommended decision rule: classify as `yes` when `p_yes >= 0.992003`
+
+## Training Data
+
+The model was trained on the repository's internal 600-family labeled DOCDB
+sample, split into 420 train, 90 validation, and 90 test families. The row-level
+labeling queue, patent text, split manifest, and prediction files are not part
+of this model release.
+
+## Evaluation
+
+Internal validation and test metrics use the validation-selected threshold
+`p_yes >= 0.992003`.
+
+| Split | PR-AUC | Precision | Recall | F1 |
+|---|---:|---:|---:|---:|
+| Validation | 0.9989 | 0.9677 | 1.0000 | 0.9836 |
+| Test | 0.9761 | 0.9091 | 0.9677 | 0.9375 |
+
+An external positive-only recall check on 450 independently curated
+plant-related families recovered 447 families at the training-derived threshold
+(recall = 0.993). This external check tests recall transfer, not precision.
+
+## Intended Use
+
+Use this model to score English title and abstract text for patent families
+where the goal is a high-confidence plant-related corpus. The threshold is part
+of the documented release behavior and should be recalibrated if used on a
+different corpus or label distribution.
+
+## Limitations
+
+- The labeled training set contains 600 families, so borderline behavior can
+  shift across jurisdictions, time periods, or technical domains.
+- Inputs longer than 512 tokens are truncated.
+- The external gold check is positive-only and does not estimate external
+  precision.
+- The model is trained on English title and abstract text, not full patent
+  claims or descriptions.
+
+## Reproducibility
+
+Reference artifacts in the companion repository:
+
+- Training report: `metadata/paecter_report_2026-01-29_rerun.md`
+- Model card source: `metadata/model_card_paecter.md`
+- Public companion repository:
+  <https://github.com/aydiet/plant-patent-classifier-reproducibility>
+
+## Example
+
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+
+model_id = "YOUR_NAMESPACE/plant-patent-paecter-plant-detector"
+text = "Drought tolerant maize plant\n\nA maize plant with improved drought tolerance is provided."
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForSequenceClassification.from_pretrained(model_id)
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding="max_length", max_length=512)
+
+with torch.no_grad():
+    probs = torch.softmax(model(**inputs).logits, dim=-1)[0]
+
+p_yes = float(probs[model.config.label2id["yes"]])
+label = "yes" if p_yes >= 0.992003 else "no"
+print(label, p_yes)
+```