---
pipeline_tag: sentence-similarity
language: 
- en
tags:
- linktransformer
- sentence-transformers
- sentence-similarity
- tabular-classification

---

# gbpatentdata/lt-patent-inventor-linking

This is a [LinkTransformer](https://linktransformer.github.io/) model. At its core this model this is a sentence transformer model [sentence-transformers](https://www.SBERT.net) model - it just wraps around the class. 
This model has been fine-tuned on the model: `sentence-transformers/all-mpnet-base-v2`. It is pretrained for the language: `en`.

## Usage (Sentence-Transformers)

To use this model using sentence-transformers:

```python 
from sentence_transformers import SentenceTransformer

# load 
model = SentenceTransformer("matthewleechen/lt-patent-inventor-linking")
```

## Usage (LinkTransformer)

To use this model for clustering with [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) installed:

```python
import linktransformer as lt
import pandas as pd

df_lm_matched = lt.cluster_rows(df, # df should be a dataset of unique patent-inventors 
                                model='matthewleechen/lt-patent-inventor-linking',
                                on=['name', 'occupation', 'year', 'address', 'firm', 'patent_title'], # cluster on these variables 
                                cluster_type='SLINK', # use SLINK algorithm 
                                cluster_params={ # default params 
                                    'threshold': 0.1,
                                    'min cluster size': 1,
                                    'metric': 'cosine'
                                }
    )
)

```

## Evaluation

We evaluate using the standard [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) information retrieval metrics. Our test set evaluations are available [here](https://huggingface.co/gbpatentdata/lt-patent-inventor-linking/blob/main/Information-Retrieval_evaluation_test_results.csv).


## Training
The model was trained with the parameters:

**DataLoader**:

`torch.utils.data.dataloader.DataLoader` of length 31 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```

**Loss**:

`linktransformer.modified_sbert.losses.SupConLoss_wandb` 

Parameters of the fit()-Method:
```
{
    "epochs": 100,
    "evaluation_steps": 16,
    "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
    "max_grad_norm": 1,
    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
    "optimizer_params": {
        "lr": 2e-05
    },
    "scheduler": "WarmupLinear",
    "steps_per_epoch": null,
    "warmup_steps": 3100,
    "weight_decay": 0.01
}
```
```
LinkTransformer(
  (0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
  (2): Normalize()
)
```

## Citation 

If you use our model or custom training/evaluation data in your research, please cite our accompanying paper as follows:

```
@article{bct2025,
  title = {300 Years of British Patents},
  author = {Enrico Berkes and Matthew Lee Chen and Matteo Tranchero},
  journal = {arXiv preprint arXiv:2401.12345},
  year = {2025},
  url = {https://arxiv.org/abs/2401.12345}
}
```

Please also cite the original LinkTransformer authors:

```
@misc{arora2023linktransformer,
                  title={LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models},
                  author={Abhishek Arora and Melissa Dell},
                  year={2023},
                  eprint={2309.00789},
                  archivePrefix={arXiv},
                  primaryClass={cs.CL}
                }

```