Update README.md

README.md

@@ -5,7 +5,7 @@ license: apache-2.0
 
 This repository provides PyTorch source code associated with our publication, "A Large Encoder-Decoder Family of Foundation Models for Chemical Language".
 
-Paper: [Arxiv Link](paper/smi_ted_preprint.pdf)
+Paper: [Arxiv Link](https://github.com/IBM/materials/blob/main/smi-ted/paper/smi_ted_preprint.pdf)
 
 For model weights contact: eduardo.soares@ibm.com or evital@br.ibm.com .
 
@@ -101,7 +101,7 @@ Use `train_model_D.py` to train only the decoder or `train_model_ED.py` to train
 
 ## Finetuning
 
-The finetuning datasets and environment can be found in the [finetune](finetune/) directory. After setting up the environment, you can run a finetuning task with:
+The finetuning datasets and environment can be found in the [finetune](https://github.com/IBM/materials/tree/main/smi-ted/finetune) directory. After setting up the environment, you can run a finetuning task with:
 
 ```
 bash finetune/smi_ted_light/esol/run_finetune_esol.sh
@@ -111,7 +111,7 @@ Finetuning training/checkpointing resources will be available in directories nam
 
 ## Feature Extraction
 
-The example notebook [smi_ted_encoder_decoder_example.ipynb](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: eduardo.soares@ibm.com or evital@br.ibm.com.
+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: eduardo.soares@ibm.com or evital@br.ibm.com.
 
 To load smi-ted, you can simply use: