Update README.md

README.md

@@ -22,11 +22,11 @@ This repository provides PyTorch source code associated with our publication, "A
 
 Paper: [Arxiv Link](https://github.com/IBM/materials/blob/main/smi-ted/paper/smi_ted_preprint.pdf)
 
-For model weights contact: [email protected] or [email protected] .
+For more information contact: [email protected] or [email protected].
 
 ## Introduction
 
-We present a large encoder-decoder chemical foundation model, SMILES-based Transformer Encoder-Decoder (SMI-TED), pre-trained on a curated dataset of 91 million SMILES samples sourced from PubChem, equivalent to 4 billion molecular tokens. SMI-TED supports various complex tasks, including quantum property prediction, with two main variants ($289M$ and $8 \times 289M$). Our experiments across multiple benchmark datasets demonstrate state-of-the-art performance for various tasks. For model weights contact: [email protected] or [email protected] .
+We present a large encoder-decoder chemical foundation model, SMILES-based Transformer Encoder-Decoder (SMI-TED), pre-trained on a curated dataset of 91 million SMILES samples sourced from PubChem, equivalent to 4 billion molecular tokens. SMI-TED supports various complex tasks, including quantum property prediction, with two main variants ($289M$ and $8 \times 289M$). Our experiments across multiple benchmark datasets demonstrate state-of-the-art performance for various tasks. For more information contact: [email protected] or [email protected].
 
 ## Table of Contents
 
@@ -44,7 +44,7 @@ We present a large encoder-decoder chemical foundation model, SMILES-based Trans
 
 ### Pretrained Models and Training Logs
 
-We provide checkpoints of the SMI-TED model pre-trained on a dataset of ~91M molecules curated from PubChem. The pre-trained model shows competitive performance on classification and regression benchmarks from MoleculeNet. For model weights contact: [email protected] or [email protected] .
+We provide checkpoints of the SMI-TED model pre-trained on a dataset of ~91M molecules curated from PubChem. The pre-trained model shows competitive performance on classification and regression benchmarks from MoleculeNet.
 
 Add the SMI-TED `pre-trained weights.pt` to the `inference/` or `finetune/` directory according to your needs. The directory structure should look like the following:
 
@@ -126,7 +126,7 @@ Finetuning training/checkpointing resources will be available in directories nam
 
 ## Feature Extraction
 
-The example notebook [smi_ted_encoder_decoder_example.ipynb](https://github.com/IBM/materials/blob/main/smi-ted/notebooks/smi_ted_encoder_decoder_example.ipynb) contains code to load checkpoint files and use the pre-trained model for encoder and decoder tasks. It also includes examples of classification and regression tasks. For model weights contact: [email protected] or [email protected].
+The example notebook [smi_ted_encoder_decoder_example.ipynb](https://github.com/IBM/materials/blob/main/smi-ted/notebooks/smi_ted_encoder_decoder_example.ipynb) contains code to load checkpoint files and use the pre-trained model for encoder and decoder tasks. It also includes examples of classification and regression tasks. 
 
 To load smi-ted, you can simply use: