Update model_cards/article.md

model_cards/article.md

@@ -2,45 +2,33 @@
 
 **Language model**: Type of language model to be used.
 
-**Text prompt**: The text prompt to condition the model.
+**Prefix**: Task specific prefix for task definition (see the provided examples for specific tasks).
 
-**Maximal length**: The maximal number of SMILES tokens in the generated molecule.
+**Text prompt**: The text input of the model.
 
-**Decoding temperature**: The temperature in the beam search decoding.
+**Num beams**: Number of beams to be used for the text generation.
 
-**Prefix**: A text prompt that will be passed to the mode **before** the prompt.
 
-**Top-k**: Number of top-k probability tokens to keep.
 
-**Decoding-p**: Only tokens with cumulative probabilities summing up to this value are kept.
+# Model card -- PatentGenerativeTransformer
 
-**Repetition penalty**: Penalty for repeating tokens. Leave unchanged, but for CTRL model, use 1.2.
+**Model Details**: Text+chem T5 : a multi-domain, multi-task language model to solve a wide range of tasks in both the chemical and natural language domains. Published by [Christofidellis et al.](https://arxiv.org/pdf/2301.12586.pdf)
 
+**Developers**: Dimitrios Christofidellis, Giorgio Giannone, Jannis Born and Matteo Manica from IBM Research and Ole Winther from Technical University of Denmark.
 
+**Distributors**: Model natively integrated into GT4SD.
 
-# Model card -- HuggingFace
+**Model date**: 2022.
 
-**Model Details**: Various Transformer-based language models.
-
-**Developers**: HuggingFace developers 
-
-**Distributors**: HuggingFace developers' code integrated into GT4SD.
-
-**Model date**: Varies between models.
-
-**Model type**: Different types of `transformers` language models:
-- CTRL: `CTRLLMHeadModel`
-- GPT2: `GPT2LMHeadModel`
-- XLNet: `XLNetLMHeadModel`
-- OpenAIGPT: `OpenAIGPTLMHeadModel`
-- TransfoXL: `TransfoXLLMHeadModel`
-- XLM: `XLMWithLMHeadModel`
+**Model type**: A Transformer-based language model that is trained on a multi-domain and a multi-task dataset by aggregating available datasets
+for the tasks of Forward reaction prediction, Retrosynthesis, Molecular captioning, Text-conditional de novo generation and Paragraph to actions. 
 
 **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 Text+chem T5 [Christofidellis et al.](https://arxiv.org/pdf/2301.12586.pdf)
+
 
 **License**: MIT
 
@@ -64,15 +52,10 @@ 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"
+@article{christofidellis2023unifying,
+  title={Unifying Molecular and Textual Representations via Multi-task Language Modelling},
+  author={Christofidellis, Dimitrios and Giannone, Giorgio and Born, Jannis and Winther, Ole and Laino, Teodoro and Manica, Matteo},
+  journal={arXiv preprint arXiv:2301.12586},
+  year={2023}
 }
 ```