---
license: cc-by-nc-4.0
language:
- en
base_model:
- anferico/bert-for-patents
tags:
- patent
- embeddings
- contrastive-learning
- information-retrieval
pipeline_tag: feature-extraction
---

# PatentMap-V0-SecPair-Drawing

**PatentMap-V0-SecPair-Drawing** is a patent embedding model trained on abstract + drawing sections with section-pair augmentation. It is part of the PatentMap V0 model collection.

## Model Details

- **Base Model:** [anferico/bert-for-patents](https://huggingface.co/anferico/bert-for-patents)
- **Training Objective:** Contrastive learning (InfoNCE loss)
- **Architecture:** BERT-large (340M parameters)
- **Embedding Dimension:** 1024
- **Max Sequence Length:** 512 tokens
- **Vocabulary Size:** 39860
- **Training Data:** USPTO patent applications (2010-2018) from [HUPD corpus](https://huggingface.co/datasets/HUPD/hupd)

### Training Configuration

- **Patent Sections Used:** abstract + drawing
- **Data Augmentation:** dropout + section_pair
- **Batch Size:** 512
- **Learning Rate:** 1e-5

### Special Tokens

This model includes additional patent-specific special tokens:
- `[drawing]`

## Usage

### Input Format

This model expects patent text formatted with special tokens:

- **For abstract**: `Title [SEP] [abstract] Abstract text`
- **For other sections**: `[section] Section text` (no title prefix)

Example:
```python
# Abstract with title
text = "Smart thermostat system [SEP] [abstract] A thermostat system comprising..."

# Claim without title
text = "[claim] A method comprising: step 1, step 2..."
```

### Code Example

```python
from transformers import AutoTokenizer, AutoModel
import torch

# Load model and tokenizer
model_name = "ZoeYou/PatentMap-V0-SecPair-Drawing"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Format patent text
title = "Smart thermostat system"
abstract = "A thermostat system comprising a temperature sensor..."
patent_text = f"{title} [SEP] [abstract] {abstract}"

# Encode and get embeddings
inputs = tokenizer(patent_text, return_tensors="pt", padding=True, truncation=True, max_length=512)

with torch.no_grad():
    outputs = model(**inputs)
    embeddings = outputs.last_hidden_state[:, 0, :]  # CLS token
    
print(embeddings.shape)  # torch.Size([1, 1024])
```

## Evaluation

This model has been evaluated on multiple patent-specific tasks:

- **IPC Classification** (linear probe and KNN)
- **Prior Art Search** (recall@k, nDCG@k)
- **Embedding Quality Metrics** (uniformity, alignment, topology)

For detailed evaluation results, see the [PatentMap paper](https://arxiv.org/abs/2511.10657).

## Intended Use

This model is designed for:
- Patent document retrieval
- Patent similarity search
- Prior art discovery
- IPC classification
- Patent landscape analysis

## Citation

If you use this model, please cite:

```bibtex
@article{zuo2025patent,
  title={Patent Representation Learning via Self-supervision},
  author={Zuo, You and Gerdes, Kim and de La Clergerie, Eric Villemonte and Sagot, Beno{\^i}t},
  journal={arXiv preprint arXiv:2511.10657},
  year={2025}
}
```

## Model Collection

This model is part of the PatentMap V0 collection. For an overview of all models, see [PatentMap-V0](https://huggingface.co/ZoeYou/patentmapv0-models).

## License

This model is released under CC BY-NC 4.0 license (non-commercial use only).

## Contact

For questions or issues, please open an issue on the [GitHub repository](https://github.com/ZoeYou/patentmapv0) or contact the authors.