---
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)
```