Update README.md

README.md

@@ -1,7 +1,8 @@
 ---
 license: cc-by-4.0
 datasets:
-- MS-ML/synth2_2x4.7M
+- MS-ML/synth1_2x4.7M
+- MS-ML/synth2_2x4.8M
 metrics:
 - accuracy
 library_name: transformers
@@ -9,33 +10,35 @@ tags:
 - mass-spectrometry
 - GC-EI-MS
 - Transformer
-- molecular-structure-elucidation
+- molecular-structure-reconstruction
 - compound-identification
 ---
 
-The SpecTUS model pretrained on synth2_2x4.7M for 2x112k steps. 
+The SpecTUS model pretrained on synth1_2x4.7 and synth2_2x4.8M combined for 448k 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
+The model was pretrained on a large dataset of 17.2M synthetic training spectra generated from two identical sets of 8.6M
 compounds using the [NEIMS] and [RASSP] 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.
+conducted with a batch size of 128 for 448,000 steps, allowing the model to process each of the 17.2 million spectra approximately three times.
+The entire pretraining process, including control evaluations every 16,000 steps, took 58 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: 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.
+During pretraining, the percentage of correctly reconstructed structures increased steadily but it remained relatively low at the
+end of the stage: 38% for RASSP-generated spectra, 29% for NEIMS-generated spectra, and 3% for NIST spectra. However, 96% of 
+the generated SMILES strings (RASSP, NEIMS) were valid canonical molecules, with 91% (RASSP), 78% (NEIMS), and 14% (NIST) having
+correct molecular formulas, though possibly incorrect structures. 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.
 
 We suggest to finetune the model further on experimental data (NIST, Wiley) to reach the performance reported in our [preprint]. Though we can not
-make the final model available, since it was finetuned on a proprietary dataset (NIST), you can fine-tune it yourself if you have purchased the license.
-The full code we used for the data processing, finetuning, evaluation, model comparison and more can be found in [our GitHub repository] (TODO).
+make the final model available, since it was finetuned on a proprietary dataset (NIST). If youhave purchased the NIST GC-EI-MS license, you can
+either fine-tune the model yourself using the code in [our GitHub repository] or contact us with a proof of the license and we will share the final
+model with you. The code we used for the data processing, finetuning, evaluation, model comparison and more can also be found in [our GitHub repository] (TODO).
 
 Our [preprint] (TODO) provides more information about the task background, the final finetuned model, and the experiments.
 
 [NEIMS]: https://github.com/brain-research/deep-molecular-massspec
 [RASSP]: https://github.com/thejonaslab/rassp-public
-[our GitHub repository]: !TODO!
+[our GitHub repository]: https://github.com/hejjack/SpecTUS/
 [preprint]: !TODO!