From edb150b7f71e75af3070271a69a1287359d41ffc Mon Sep 17 00:00:00 2001
From: Kohulan Rajan
Date: Mon, 14 Oct 2024 17:31:40 +0200
Subject: [PATCH] feat: include training information README.md
---
README.md | 66 +++++++++++++++++++++++++++++++++++--------------------
1 file changed, 42 insertions(+), 24 deletions(-)
diff --git a/README.md b/README.md
index 5f7de73..0497ef2 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
-V2.0
+ STOUT V2.0
@@ -47,6 +47,7 @@ V2.0
Key Features •
Installation •
How To Use •
+ Training STOUT •
Acknowledgements •
Citation
@@ -57,33 +58,41 @@ V2.0
## Key Features
-
- - 🧪 Translate SMILES to IUPAC names
- - 🔬 Convert IUPAC names back to valid SMILES strings
- - 🤖 Powered by advanced transformer models
- - 💻 Cross-platform support (Linux, macOS, Windows via Ubuntu shell)
- - 🚀 High-performance chemical nomenclature translation
-
+- 🧪 Translate SMILES to IUPAC names
+- 🔬 Convert IUPAC names back to valid SMILES strings
+- 🤖 Powered by advanced transformer models
+- 💻 Cross-platform support (Linux, macOS, Windows via Ubuntu shell)
+- 🚀 High-performance chemical nomenclature translation
+- 🧠 Training code available for custom model development
## Installation
-Choose your preferred installation method:
+Choose your preferred installation method:
📦 PyPI Installation
-pip install STOUT-pypi
+
+```bash
+pip install STOUT-pypi
+```
🐍 Conda Environment Setup
-conda create --name STOUT python=3.10
+
+```bash
+conda create --name STOUT python=3.10
conda activate STOUT
-conda install -c decimer stout-pypi
+conda install -c decimer stout-pypi
+```
📥 Direct Repository Installation
-pip install git+https://github.com/Kohulan/Smiles-TO-iUpac-Translator.git
+
+```bash
+pip install git+https://github.com/Kohulan/Smiles-TO-iUpac-Translator.git
+```
## How To Use
@@ -102,6 +111,23 @@ SMILES = translate_reverse(IUPAC_name)
print(f"🔬 SMILES of {IUPAC_name} is: {SMILES}")
```
+## Training STOUT
+
+For researchers interested in training their own STOUT models or understanding the training process, we provide the training code in a separate repository:
+
+[STOUT Training Repository](https://github.com/Kohulan/IWOMI_Tutorials/tree/IWOMI_2024/STOUT_Training)
+
+This repository contains the necessary scripts and instructions for training STOUT models. Please note that training requires significant computational resources and a large dataset. Refer to the README in the training repository for detailed instructions.
+
+## Model Card
+
+> Rajan, K., Steinbeck, C., & Zielesny, A. (2024). STOUT V2 - Model library (Version v3). Zenodo. https://doi.org/10.5281/zenodo.13318286
+
+### Model Use
+- Primary intended uses: Translation between SMILES and IUPAC names for chemical compounds
+- Primary intended users: Chemists, researchers, and developers in the field of cheminformatics
+- Out-of-scope use cases: Not intended for critical applications where 100% accuracy is required
+
## Acknowledgements
@@ -128,25 +154,17 @@ print(f"🔬 SMILES of {IUPAC_name} is: {SMILES}")
## Citation
-
1. Rajan, K., Zielesny, A. & Steinbeck, C. STOUT: SMILES to IUPAC names using neural machine translation. J Cheminform 13, 34 (2021). https://doi.org/10.1186/s13321-021-00512-4
-
-
-2. Rajan K, Zielesny A, Steinbeck C. STOUT V2.0: SMILES to IUPAC name conversion using transformer models. ChemRxiv. 2024; https://doi.org/10.26434/chemrxiv-2024-089vs
-
-## Model Card
-
-Rajan, K., Steinbeck, C., & Zielesny, A. (2024). STOUT V2 - Model library. Zenodo. https://doi.org/10.5281/zenodo.13318286
-
+2. Rajan K, Zielesny A, Steinbeck C. STOUT V2.0: SMILES to IUPAC name conversion using transformer models. ChemRxiv. 2024; https://doi.org/10.26434/chemrxiv-2024-089vs
-Repository Analytics
+## Repository Analytics
-
+---
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