aydiet/plant-patent-paecter-plant-detector

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 release is a companion checkpoint for reproducible research on plant-related patent classification. The Apache-2.0 license metadata follows the PaECTER base-model release.

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

This model is a supervised fine-tune of PaECTER. Please cite or link the original PaECTER model and paper when reusing this checkpoint.

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.

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>

Companion Resources

• —Original PaECTER base model: <https://huggingface.co/mpi-inno-comp/paecter>
• —Interactive demo Space: <https://huggingface.co/spaces/aydiet/plant-patent-classifier-demo>
• —Second-stage variety-vs-technology classifier: <https://huggingface.co/aydiet/plant-patent-paecter-variety-technology>
• —Public companion repository: <https://github.com/aydiet/plant-patent-classifier-reproducibility>
• —Manuscript context: Identifying Plant-Related Patents: Corpus Construction and Global Patterns

Example

from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

model_id = "aydiet/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)