Update README.md

README.md

@@ -1,4 +1,11 @@
 ---
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: test
+    path: data/test-*
 dataset_info:
   features:
   - name: label
@@ -12,15 +19,37 @@ dataset_info:
   - name: content
     dtype: string
   splits:
-  - name: test
-    num_bytes: 18182813
-    num_examples: 40000
   - name: train
     num_bytes: 163359702
     num_examples: 360000
+  - name: test
+    num_bytes: 18182813
+    num_examples: 40000
   download_size: 120691417
   dataset_size: 181542515
 ---
-# Dataset Card for "amazon_polarity_10_pct"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+# Amazon Polarity 10pct
+
+This is a direct subset of the original [Amazon Polarity](https://huggingface.co/datasets/amazon_polarity) dataset, downsampled 10pct with a random shuffle
+
+### Dataset Summary
+
+For quicker testing on Amazon Polarity. See https://huggingface.co/datasets/amazon_polarity for details and attributions
+
+
+### Source Data
+```python
+from datasets import ClassLabel, Dataset, DatasetDict, load_dataset
+
+ds_full = load_dataset("amazon_polarity", streaming=True)
+ds_train_10_pct = Dataset.from_list(list(ds_full["train"].shuffle(seed=42).take(360_000)))
+ds_test_10_pct = Dataset.from_list(list(ds_full["test"].shuffle(seed=42).take(40_000)))
+
+ds_10_pct = DatasetDict({"train": ds_train_10_pct, "test": ds_test_10_pct})
+# Need to recreate the class labels
+class_label = ClassLabel(num_classes=2, names=["negative", "positive"])
+ds_10_pct = ds_10_pct.map(lambda row: {"title": row["title"], "content": row["content"], "label": "negative" if not row["label"] else "positive"})
+ds_10_pct = ds_10_pct.cast_column("label", class_label)
+```
+