Update README.md

README.md

@@ -39,7 +39,7 @@ human protein pairs interact directly or indirectly.
     Reimand et al. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 2016 Jul 8;44(W1):W83-9. doi: 10.1093/nar/gkw199.
 
 ## Associated code 
-  Additional code examples can be found on our [GitHub](), including:
+  Additional code examples can be found on our [GitHub](https://github.com/KDrewLab/DirectContacts2_analysis.git), including:
   importing the [DirectContacts2 model](sfisch/DirectContacts2_AutoGluon) to make predictions, importing the 
   training and testing data, or using the full feature matrix.
   
@@ -51,12 +51,12 @@ is used to is used train, test, and make predictions with the model.
 
 This can be downloaded using the following:
 
-      $ pip install autogluon==0.4.0
+      $ pip install autogluon==0.8.2
 
 Then it can be imported as:
 
     >>> from autogluon.tabular import TabularPredictor
-Note that to perform operations with our model the **0.4.0 version** must be used
+Note that to perform operations with our model the **0.8.2 version** must be used
 
 The [DirectContacts2 model](sfisch/DirectContacts2_AutoGluon) can be accessed through HuggingFace with [huggingface_hub](https://huggingface.co/docs/hub/index)
 
@@ -64,7 +64,7 @@ The [DirectContacts2 model](sfisch/DirectContacts2_AutoGluon) can be accessed th
 
     >>> model_dir = snapshot_download(repo_id="sfisch/DirectContacts2_AutoGluon")
 
-    >>> predictor = TabularPredictor.load(f"{model_dir}/DirectContacts2_Autogluon_Model_20230405")
+    >>> predictor = TabularPredictor.load(f"{model_dir}/DirectContacts2_Autogluon_Model")
 
 ## Using the training and testing data
 
@@ -90,7 +90,7 @@ Jupyter notebooks containing more in-depth examples of model training, testing,
 All other files, such as the full feature matrix, can be accessed via Huggingface_hub.
 
     >>> from huggingface_hub import hf_hub_download
-    >>> full_file = hf_hub_download(repo_id="sfisch/DirectContacts2", filename='full/direct_contacts2_full_feature_matrix_20220625.csv.gz', repo_type='dataset')
+    >>> full_file = hf_hub_download(repo_id="sfisch/DirectContacts2", filename='full/humap3_full_feature_matrix_20220625.csv.gz', repo_type='dataset')
 
 This just provides the file for download. Depending on your workflow, if you wish to use as a pandas dataframe for example: