update

model_cards/article.md

@@ -1,46 +1,57 @@
 # Model documentation & parameters
 
-**Language model**: Type of language model to be used.
+**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
 
-**Text prompt**: The text prompt to condition the model.
+**Generator task**: Task in case the `PGTGenerator` model is used. Options are:
+- `title-to-abstract`
+- `abstract-to-title`
+- `abstract-to-claim`
+- `claim-to-abstract`
 
-**Maximal length**: The maximal number of SMILES tokens in the generated molecule.
+**Editor task**: Task in case the `PGTEditor` model is used. Options are:
+- `abstract`
+- `claim`
 
-**Decoding temperature**: The temperature in the beam search decoding.
+**Coherence task**: Task in case the `PGTCoherenceChecker` model is used. Options are:
+- `title-abstract`
+- `title-claim`
+- `abstract-claim`
 
-**Prefix**: A text prompt that will be passed to the mode **before** the prompt.
+**Primary text prompt**: The main text prompt for the model
 
-**Top-k**: Number of top-k probability tokens to keep.
+**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.
 
-**Decoding-p**: Only tokens with cumulative probabilities summing up to this value are kept.
+**Top-k**: Number of top-k probability tokens to keep.
 
-**Repetition penalty**: Penalty for repeating tokens. Leave unchanged, but for CTRL model, use 1.2.
+**Top-p**: Only tokens with cumulative probabilities summing up to this value are kept.
 
 
 
-# Model card -- HuggingFace
+# Model card -- PatentGenerativeTransformer
 
-**Model Details**: Various Transformer-based language models.
+**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**: HuggingFace developers 
+**Developers**: Dimitrios Christofidellis and colleagues at IBM Research.
 
-**Distributors**: HuggingFace developers' code integrated into GT4SD.
+**Distributors**: Model natively integrated into GT4SD.
 
-**Model date**: Varies between models.
+**Model date**: 2022.
 
-**Model type**: Different types of `transformers` language models:
-- CTRL: `CTRLLMHeadModel`
-- GPT2: `GPT2LMHeadModel`
-- XLNet: `XLNetLMHeadModel`
-- OpenAIGPT: `OpenAIGPTLMHeadModel`
-- TransfoXL: `TransfoXLLMHeadModel`
-- XLM: `XLMWithLMHeadModel`
+**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**: 
-All documentation available from [transformers documentation](https://huggingface.co/docs/transformers/)
+The Patent Generative Transformer (PGT) [paper by Christofidellis et al. (*ICML 2022 Workshop KRLM*)](https://openreview.net/forum?id=dLHtwZKvJmE).
 
 **License**: MIT
 
@@ -64,15 +75,10 @@ Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi
 
 ## Citation
 ```bib
-@inproceedings{wolf-etal-2020-transformers,
-    title = "Transformers: State-of-the-Art Natural Language Processing",
-    author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
-    month = oct,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
-    pages = "38--45"
+@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}
 }
 ```

model_cards/description.md

@@ -1,6 +1,6 @@
 <img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" >
 
-This UI gives access to some pretrained language models from [*HuggingFace*](https://github.com/huggingface/) that are distributed via GT4SD.
+[Patent Generative Transformers (PGT)](https://openreview.net/forum?id=dLHtwZKvJmE): A prompt based generative transformer for the patent domain (Christofidellis et al., 2022; *ICLR Workshop KRLM*).
 
 For **examples** and **documentation** of the model parameters, please see below.
 Moreover, we provide a **model card** ([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)) at the bottom of this page.