update README

README.md

@@ -14,15 +14,14 @@ size_categories:
 Datasets accompanying the paper "Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain".
 
 ## Quick Start
+
+### azure_vm_traces_2017
 ```python
 from datasets import load_dataset
 
 dataset = load_dataset('Salesforce/cloudops_tsf', 'azure_vm_traces_2017')
-```
+print(dataset)
 
-## Available Datasets
-### azure_vm_traces_2017
-```python
 DatasetDict({
     train_test: Dataset({
         features: ['start', 'target', 'item_id', 'feat_static_cat', 'feat_static_real', 'past_feat_dynamic_real'],
@@ -37,6 +36,9 @@ DatasetDict({
 
 ### borg_cluster_data_2011
 ```python
+dataset = load_dataset('Salesforce/cloudops_tsf', 'borg_cluster_data_2011')
+print(dataset)
+
 DatasetDict({
     train_test: Dataset({
         features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
@@ -51,6 +53,9 @@ DatasetDict({
 
 ### alibaba_cluster_trace_2018
 ```python
+dataset = load_dataset('Salesforce/cloudops_tsf', 'alibaba_cluster_trace_2018')
+print(dataset)
+
 DatasetDict({
     train_test: Dataset({
         features: ['start', 'target', 'item_id', 'feat_static_cat', 'past_feat_dynamic_real'],
@@ -71,7 +76,7 @@ config = load_dataset_builder('Salesforce/cloudops_tsf', 'azure_vm_traces_2017')
 print(config)
 
 CloudOpsTSFConfig(
-    name='alibaba_cluster_trace_2018',
+    name='azure_vm_traces_2017',
     version=1.0.0,
     data_dir=None,
     data_files=None,
@@ -79,23 +84,32 @@ CloudOpsTSFConfig(
     prediction_length=48,
     freq='5T',
     stride=48,
-    univariate=False,
-    multivariate=True,
-    optional_fields=('feat_static_cat', 'past_feat_dynamic_real'),
+    univariate=True,
+    multivariate=False,
+    optional_fields=(
+        'feat_static_cat',
+        'feat_static_real',
+        'past_feat_dynamic_real'
+    ),
     rolling_evaluations=12,
-    test_split_date=Period('2018-01-08 11:55', '5T'),
+    test_split_date=Period('2016-12-13 15:55', '5T'),
     _feat_static_cat_cardinalities={
         'pretrain': (
-            ('container_id', 64457),
-            ('app_du', 9484)),
+            ('vm_id', 177040),
+            ('subscription_id', 5514),
+            ('deployment_id', 15208),
+            ('vm_category', 3)
+        ),
         'train_test': (
-            ('container_id', 6048),
-            ('app_du', 1292)
+            ('vm_id', 17568),
+            ('subscription_id', 2713),
+            ('deployment_id', 3255),
+            ('vm_category', 3)
         )
     },
-    target_dim=2,
-    feat_static_real_dim=0,
-    past_feat_dynamic_real_dim=6
+    target_dim=1,
+    feat_static_real_dim=3,
+    past_feat_dynamic_real_dim=2
 )
 ```
 ```test_split_date``` is provided to achieve the same train-test split as given in the paper.
@@ -117,11 +131,11 @@ The datasets were processed from the following original sources. Please cite the
   * https://github.com/alibaba/clusterdata
 
 ## Citation
-```
+<pre>
 @article{woo2023pushing,
   title={Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain},
   author={Woo, Gerald and Liu, Chenghao and Kumar, Akshat and Sahoo, Doyen},
   journal={arXiv preprint arXiv:2310.05063},
   year={2023}
 }
-```
+</pre>