sagawa/ReactionT5v1-forward

This is a ReactionT5 pre-trained to predict the products of reactions. You can use the demo here.

## Example Usage ```python from transformers import AutoTokenizer, T5ForConditionalGeneration

This is a ReactionT5 pre-trained to predict the reactants of reactions. You can use the demo here.

SMILES2IUPAC-canonical-base was designed to accurately translate SMILES chemical names to IUPAC standards.

The T5 Large for Medical Text Summarization is a specialized variant of the T5 transformer model, fine-tuned for the task of summarizing medical text. This model is designed to generate concise and coherent summaries of medical documents, research papers, clinical notes, and other…

+ Model Name: Med_English2Spanish + Model Type: Transformer-based Neural Machine Translation (NMT) Model + Task: English to Spanish Medical Translation