add example

README.md

@@ -919,30 +919,6 @@ dataset_info:
     num_examples: 2513
   download_size: 20817460471
   dataset_size: 22122441600
-- config_name: default
-  features:
-  - name: data
-    sequence: float16
-  - name: test_loss
-    dtype: float16
-  - name: test_acc
-    dtype: float16
-  - name: train_loss
-    dtype: float16
-  - name: train_acc
-    dtype: float16
-  splits:
-  - name: '0'
-    num_bytes: 77328384
-    num_examples: 2688
-  - name: '1'
-    num_bytes: 77328384
-    num_examples: 2688
-  - name: '2'
-    num_bytes: 77328384
-    num_examples: 2688
-  download_size: 218320869
-  dataset_size: 231985152
 - config_name: fm-16
   features:
   - name: data
@@ -3679,14 +3655,6 @@ configs:
     path: c10-16/298-*
   - split: '299'
     path: c10-16/299-*
-- config_name: default
-  data_files:
-  - split: '0'
-    path: data/0-*
-  - split: '1'
-    path: data/1-*
-  - split: '2'
-    path: data/2-*
 - config_name: fm-16
   data_files:
   - split: '0'
@@ -5095,8 +5063,6 @@ configs:
     path: lm1b-3-24/199-*
 ---
 
-**The dataset is being prepared and uploaded**
-
 This is the dataset of trained neural network checkpoints used to meta-train the NiNo model from https://github.com/SamsungSAILMontreal/nino/.
 
 It contains 1000 models in total:
@@ -5120,3 +5086,19 @@ The dataset corresponds to the first 4 columns (in-distribution tasks) in Table
 
 This Table is from the `Accelerating Training with Neuron Interaction and Nowcasting Networks` paper, see https://arxiv.org/abs/2409.04434 for details.
 
+# Example
+
+Download and access trajectories of the fm-16 task (other three datasets: 'c10-16', 'lm1b-3-24', 'lm1b-2-32').
+The original trajectories was saved as float16, but when loading huggingface may convert it to float32 or float64 depending on the version.
+
+```python
+from datasets import load_dataset
+import numpy as np
+
+# Load the 'fm-16' task
+dataset = load_dataset('SamsungSAILMontreal/nino_metatrain', 'fm-16')  # you can use streaming=True for checking out individual samples
+
+# Once downloaded, access the t-th state in the i-th trajectory: 
+print(np.array(dataset[str(i)][t]['data']).shape)  # (14378,)
+
+```