Update README.md

README.md

@@ -23,8 +23,8 @@ We mainly aimed to give the model an understanding of the chemical space of smal
 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
+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.