anrilombard
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safe-100m

@@ -1,199 +1,87 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+tags:
+  - safe
+  - datamol-io
+  - molecule-design
+  - smiles
+  - generated_from_trainer
+datasets:
+  - sagawa/ZINC-canonicalized
+model-index:
+  - name: SAFE_100M
+    results: []
 ---
+# SAFE_100M
+This model was trained from scratch on the ZINC dataset converted to SAFE format for molecule generation tasks.
+It achieves the following results on the evaluation set:
+- Loss: 0.3887
+- Accuracy: 0.6707
+## Model description
+SAFE_100M is a transformer-based model designed for molecular generation tasks. It was trained on the ZINC dataset (https://huggingface.co/datasets/sagawa/ZINC-canonicalized), 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.
+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.
+This model utilizes the SAFE framework, which was introduced in the following paper:
+```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}
+}
+```
+We acknowledge and thank the authors for their valuable contribution to the field of molecular design.
+## Intended uses & limitations
+This model is primarily intended for:
+- Generating molecular structures
+- Exploring chemical space for drug discovery
+- Assisting in the design of new materials
+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 ZINC dataset
+## Training and evaluation data
+The model was trained on the ZINC dataset (https://huggingface.co/datasets/sagawa/ZINC-canonicalized), which was converted to the SAFE format. The ZINC dataset is a large collection of commercially available chemical compounds for virtual screening.
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 100
+- eval_batch_size: 100
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 200
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 10000
+- training_steps: 250000
+### Framework versions
+- Transformers 4.44.2
+- Pytorch 2.4.0+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1