model_cards/article.md

# Model documentation & parameters

**Model type**: Type of PGT model to be used:
- `PGTGenerator`: A model for part-of-patent generator.
- `PGTEditor`: An algorithm for part-of-patent editing.
- `PGTCoherenceChecker`: An algorithm for patent coherence check.

**Generator task**: Task in case the `PGTGenerator` model is used. Options are:
- `title-to-abstract`
- `abstract-to-title`
- `abstract-to-claim`
- `claim-to-abstract`

**Editor task**: Task in case the `PGTEditor` model is used. Options are:
- `abstract`
- `claim`

**Coherence task**: Task in case the `PGTCoherenceChecker` model is used. Options are:
- `title-abstract`
- `title-claim`
- `abstract-claim`

**Primary text prompt**: The main text prompt for the model

**Secondary text prompt**: The secondary text prompt for the model (only used for `PGTCoherenceChecker`).

**Maximal length**: The maximal number of tokens in the generated sequences.

**Top-k**: Number of top-k probability tokens to keep.

**Top-p**: Only tokens with cumulative probabilities summing up to this value are kept.



# Model card -- PatentGenerativeTransformer

**Model Details**: Patent Generative Transformer (PGT), a transformer-based multitask language model trained to facilitate the patent generation process. Published by [Christofidellis et al. (*ICML 2022 Workshop KRLM*)](https://openreview.net/forum?id=dLHtwZKvJmE)

**Developers**: Dimitrios Christofidellis and colleagues at IBM Research.

**Distributors**: Model natively integrated into GT4SD.

**Model date**: 2022.

**Model type**: 
- `PGTGenerator`: A model for part-of-patent generator
- `PGTEditor`: An algorithm for part-of-patent editing.
- `PGTCoherenceChecker`: An algorithm for patent coherence check

**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: 
N.A.

**Paper or other resource for more information**: 
The Patent Generative Transformer (PGT) [paper by Christofidellis et al. (*ICML 2022 Workshop KRLM*)](https://openreview.net/forum?id=dLHtwZKvJmE).

**License**: MIT

**Where to send questions or comments about the model**: Open an issue on [GT4SD repository](https://github.com/GT4SD/gt4sd-core).

**Intended Use. Use cases that were envisioned during development**: N.A.

**Primary intended uses/users**: N.A.

**Out-of-scope use cases**: Production-level inference, producing molecules with harmful properties.

**Metrics**: N.A.

**Datasets**: N.A.

**Ethical Considerations**: Unclear, please consult with original authors in case of questions.

**Caveats and Recommendations**: Unclear, please consult with original authors in case of questions.

Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)

## Citation
```bib
@inproceedings{christofidellis2022pgt,
  title={PGT: a prompt based generative transformer for the patent domain},
  author={Christofidellis, Dimitrios and Torres, Antonio Berrios and Dave, Ashish and Roveri, Manuel and Schmidt, Kristin and Swaminathan, Sarath and Vandierendonck, Hans and Zubarev, Dmitry and Manica, Matteo},
  booktitle={ICML 2022 Workshop on Knowledge Retrieval and Language Models},
  year={2022}
}
```