Update README.md

README.md

@@ -1,5 +1,38 @@
 ---
 license: cc-by-4.0
+datasets:
+- MS-ML/synth2_2x4.7M
+metrics:
+- accuracy
+library_name: transformers
+tags:
+- mass-spectrometry
+- GC-EI-MS
+- Transformer
+- molecular-structure-elucidation
+- compound-identification
 ---
 
-A SpecTUS model pretrained on synth2_2x4.7M for 2x112k steps. 
+A SpecTUS model pretrained on synth2_2x4.7M for 2x112k steps. 
+
+The model is a Transformer-based neural network trained to elucidate molecular structures from GC-EI-MS spectra.
+The model was pretrained on a large dataset of 9.4M synthetic spectra generated from two identical sets of 4.7M
+compounds using the NEIMS [1] and RASSP [2] models. 
+
+We mainly aimed to give the model an understanding of the chemical space of small molecules. The training was 
+conducted with a batch size of 128 for 224,000 steps, allowing the model to process each of the 9.4 million spectra approximately three times.
+The entire pretraining process, including control evaluations every 16,000 steps, took 33 hours on a single Nvidia H100 GPU.
+
+During pretraining, the percentage of correctly reconstructed validation spectra steadily increased, but remained relatively low at the end 
+of the stage: 27\% for RASSP-generated spectra, 13\% for NEIMS-generated spectra, and 2\% for NIST spectra. However, 94\% of the generated SMILES
+strings (RASSP, NEIMS) were valid canonical molecules, with 83\% (RASSP), 65\% (NEIMS), and 11\% (NIST) having correct molecular formulas. 
+These results suggest that during the pretraining phase, the model successfully learned molecular structure rules and the relationship between atomic
+weight and m/z values, forming a good foundation for subsequent finetuning.
+
+
+The full code we used for the data processing, finetuning, evaluation model comparison and more can be found in our GitHub repository [3].
+
+
+[1]: https://github.com/brain-research/deep-molecular-massspec
+[2]: https://github.com/thejonaslab/rassp-public
+[3]: !! <TODO>