Add files using upload-large-folder tool

README.md

@@ -30,7 +30,7 @@ For more information about the models, see:
 - Assembly: hg38
 
 ## Directory structure
-- `fold_0`: Model: Cross-validation fold: Fold 0
+- `fold_0`: Model of 5-fold cross-validation: Fold 0
     - `model.chrombpnet.fold_0.encid.h5`: full chrombpnet model that combines both bias and corrected model in .h5 format
     - `model.chrombpnet_nobias.fold_0.encid.h5`: bias-corrected accessibility model in .h5 format (Use for all biological discovery)
     - `model.bias_scaled.fold_0.encid.h5`: bias model in .h5 format
@@ -38,10 +38,10 @@ For more information about the models, see:
     - `model.chrombpnet_nobias.fold_0.encid.tar`: bias-corrected accessibility model in SavedModel format (Use for all biological discovery). After being untarred, it results in a directory named "chrombpnet_wo_bias".
     - `model.bias_scaled.fold_0.encid.tar`: bias model in SavedModel format. After being untarred, it results in a directory named "bias_model_scaled".
     - `logs.models.fold_0.encid`: folder containing log files for training models
-- `fold_1`: Model: Cross-validation fold: Fold 1
-- `fold_2`: Model: Cross-validation fold: Fold 2
-- `fold_3`: Model: Cross-validation fold: Fold 3
-- `fold_4`: Model: Cross-validation fold: Fold 4
+- `fold_1`: Model of 5-fold coss-validation: Fold 1
+- `fold_2`: Model of 5-fold cross-validation: Fold 2
+- `fold_3`: Model of 5-fold cross-validation: Fold 3
+- `fold_4`: Model of 5-fold cross-validation: Fold 4
 
 # Instructions
 ## 1. Pseudocode for loading models in .h5 format 
@@ -109,7 +109,7 @@ predictions = softmax(outputs["logits_profile_predictions"]) * (np.exp(outputs["
 ```
 
 ## Docker image to load and use the models
-https://hub.docker.com/r/kundajelab/chrombpnet-atlas/ (tag:v1)
+- https://hub.docker.com/r/kundajelab/chrombpnet-atlas/ (tag:v1)
 
 ## Code for ChromBPNet
 - https://github.com/kundajelab/chrombpnet/