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README.md

@@ -1,31 +1,38 @@
 ---
-datasets:
-- MOSES
 tags:
-- safe
-- datamol-io
-- molecule-design
-- smiles
-- generated_from_trainer
+  - safe
+  - datamol-io
+  - molecule-design
+  - smiles
+  - generated_from_trainer
+datasets:
+  - katielink/moses
 model-index:
-- name: SAFE_20M
-  results: []
+  - name: SAFE_20M
+    results: []
 ---
 
 # SAFE_20M
 
-This model was trained from scratch on the MOSES dataset converted to SAFE format for molecule generation tasks.
-It achieves the following results on the evaluation set:
+SAFE_20M is a transformer-based model designed for molecular generation tasks. This model was trained from scratch on the [MOSES](https://huggingface.co/datasets/katielink/moses) dataset, which has been converted from SMILES to the SAFE (SMILES Augmented For Encoding) format to enhance molecular representation for machine learning applications.
+
+## Evaluation Results
 
-- Loss: 0.4024
+On the evaluation set, SAFE_20M achieved the following result:
 
-## Model description
+- **Loss:** 0.4024
 
-SAFE_20M is a transformer-based model designed for molecular generation tasks. It was trained on the MOSES dataset, which has been converted to the SAFE (SMILES Augmented For Encoding) format. This format is specifically tailored for improved molecular representation in machine learning tasks.
+## Model Description
 
-The model is intended to generate valid and diverse molecular structures, which can be useful in various applications such as drug discovery, materials science, and chemical engineering.
+SAFE_20M leverages the SAFE framework to generate valid and diverse molecular structures. By converting the MOSES dataset from SMILES to SAFE format, the model benefits from improved molecular encoding, facilitating better performance in various applications such as:
 
-This model utilizes the SAFE framework, which was introduced in the following paper:
+- **Drug Discovery:** Identifying potential drug candidates with desirable properties.
+- **Materials Science:** Designing new materials with specific characteristics.
+- **Chemical Engineering:** Innovating chemical processes and compounds.
+
+### SAFE Framework
+
+The SAFE framework, integral to SAFE_20M, was introduced in the following paper:
 
 ```bibtex
 @article{noutahi2024gotta,
@@ -42,42 +49,43 @@ This model utilizes the SAFE framework, which was introduced in the following pa
 
 We acknowledge and thank the authors for their valuable contribution to the field of molecular design.
 
-## Intended uses & limitations
+## Intended Uses & Limitations
+
+### Intended Uses
 
-This model is primarily intended for:
+SAFE_20M is primarily intended for:
 
-- Generating molecular structures
-- Exploring chemical space for drug discovery
-- Assisting in the design of new materials
+- **Generating Molecular Structures:** Creating novel molecules with desired properties.
+- **Exploring Chemical Space:** Navigating the vast landscape of possible chemical compounds for research and development.
+- **Assisting in Material Design:** Facilitating the creation of new materials with specific functionalities.
 
-Limitations:
+### Limitations
 
-- The model's output should be validated by domain experts before practical application
-- Generated molecules may not always be synthetically feasible
-- The model's knowledge is limited to the chemical space represented in the MOSES dataset
+- **Validation Required:** Outputs should be validated by domain experts before practical application.
+- **Synthetic Feasibility:** Generated molecules may not always be synthetically feasible.
+- **Dataset Scope:** The model's knowledge is limited to the chemical space represented in the MOSES dataset.
 
-## Training and evaluation data
+## Training and Evaluation Data
 
-The model was trained on the MOSES (MOlecular SEtS) dataset, a benchmark dataset for molecular generation. The MOSES dataset was converted to the SAFE format.
+The model was trained on the [MOSES (MOlecular SEtS)](https://huggingface.co/datasets/katielink/moses) dataset, a benchmark dataset for molecular generation. The dataset was converted from SMILES to the SAFE format to enhance molecular representation for machine learning tasks.
 
-## Training procedure
+## Training Procedure
 
-### Training hyperparameters
+### Training Hyperparameters
 
 The following hyperparameters were used during training:
 
-- learning_rate: 0.0005
-- train_batch_size: 32
-- eval_batch_size: 32
-- seed: 42
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 64
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 20000
-- num_epochs: 10.0
+- **Learning Rate:** 0.0005
+- **Training Batch Size:** 32
+- **Evaluation Batch Size:** 32
+- **Seed:** 42
+- **Gradient Accumulation Steps:** 2
+- **Total Training Batch Size:** 64
+- **Optimizer:** Adam (betas=(0.9, 0.999), epsilon=1e-08)
+- **Learning Rate Scheduler:** Linear with 20,000 warmup steps
+- **Number of Epochs:** 10
 
-### Training results
+### Training Results
 
 | Training Loss | Epoch  |  Step  | Validation Loss |
 | :-----------: | :----: | :----: | :-------------: |
@@ -327,9 +335,28 @@ The following hyperparameters were used during training:
 |    0.3983     | 9.9213 | 244000 |     0.4026      |
 |    0.3997     | 9.9620 | 245000 |     0.4025      |
 
-### Framework versions
+### Framework Versions
 
-- Transformers 4.43.3
-- Pytorch 2.4.0+cu121
-- Datasets 2.20.0
-- Tokenizers 0.19.1
+- **Transformers:** 4.43.3
+- **PyTorch:** 2.4.0+cu121
+- **Datasets:** 2.20.0
+- **Tokenizers:** 0.19.1
+
+## Acknowledgements
+
+We acknowledge and thank the authors of the SAFE framework for their valuable contribution to the field of molecular design.
+
+## References
+
+```bibtex
+@article{noutahi2024gotta,
+  title={Gotta be SAFE: a new framework for molecular design},
+  author={Noutahi, Emmanuel and Gabellini, Cristian and Craig, Michael and Lim, Jonathan SC and Tossou, Prudencio},
+  journal={Digital Discovery},
+  volume={3},
+  number={4},
+  pages={796--804},
+  year={2024},
+  publisher={Royal Society of Chemistry}
+}
+```