Update README.md

README.md

@@ -115,6 +115,113 @@ configs:
       - "2017_part_03.jsonl.gz"
       - "2017_part_04.jsonl.gz"
       - "2017_part_05.jsonl.gz"
+  - config_name: "2018"
+    data_files:
+      - "2018_part_00.jsonl.gz"
+      - "2018_part_01.jsonl.gz"
+      - "2018_part_02.jsonl.gz"
+      - "2018_part_03.jsonl.gz"
+      - "2018_part_04.jsonl.gz"
+      - "2018_part_05.jsonl.gz"
+      - "2018_part_06.jsonl.gz"
+      - "2018_part_07.jsonl.gz"
+      - "2018_part_08.jsonl.gz"
+  - config_name: "2019"
+    data_files:
+      - "2019_part_00.jsonl.gz"
+      - "2019_part_01.jsonl.gz"
+      - "2019_part_02.jsonl.gz"
+      - "2019_part_03.jsonl.gz"
+      - "2019_part_04.jsonl.gz"
+      - "2019_part_05.jsonl.gz"
+      - "2019_part_06.jsonl.gz"
+      - "2019_part_07.jsonl.gz"
+      - "2019_part_08.jsonl.gz"
+      - "2019_part_09.jsonl.gz"
+      - "2019_part_10.jsonl.gz"
+  - config_name: "2020"
+    data_files:
+      - "2020_part_00.jsonl.gz"
+      - "2020_part_01.jsonl.gz"
+      - "2020_part_02.jsonl.gz"
+      - "2020_part_03.jsonl.gz"
+      - "2020_part_04.jsonl.gz"
+      - "2020_part_05.jsonl.gz"
+      - "2020_part_06.jsonl.gz"
+      - "2020_part_07.jsonl.gz"
+      - "2020_part_08.jsonl.gz"
+      - "2020_part_09.jsonl.gz"
+      - "2020_part_10.jsonl.gz"
+      - "2020_part_11.jsonl.gz"
+      - "2020_part_12.jsonl.gz"
+      - "2020_part_13.jsonl.gz"
+      - "2020_part_14.jsonl.gz"
+      - "2020_part_15.jsonl.gz"
+  - config_name: "2021"
+    data_files:
+      - "2021_part_00.jsonl.gz"
+      - "2021_part_01.jsonl.gz"
+      - "2021_part_02.jsonl.gz"
+      - "2021_part_03.jsonl.gz"
+      - "2021_part_04.jsonl.gz"
+      - "2021_part_05.jsonl.gz"
+      - "2021_part_06.jsonl.gz"
+      - "2021_part_07.jsonl.gz"
+      - "2021_part_08.jsonl.gz"
+      - "2021_part_09.jsonl.gz"
+      - "2021_part_10.jsonl.gz"
+      - "2021_part_11.jsonl.gz"
+      - "2021_part_12.jsonl.gz"
+      - "2021_part_13.jsonl.gz"
+      - "2021_part_14.jsonl.gz"
+      - "2021_part_15.jsonl.gz"
+  - config_name: "2022"
+    data_files:
+      - "2022_part_00.jsonl.gz"
+      - "2022_part_01.jsonl.gz"
+      - "2022_part_02.jsonl.gz"
+      - "2022_part_03.jsonl.gz"
+      - "2022_part_04.jsonl.gz"
+      - "2022_part_05.jsonl.gz"
+      - "2022_part_06.jsonl.gz"
+      - "2022_part_07.jsonl.gz"
+      - "2022_part_08.jsonl.gz"
+      - "2022_part_09.jsonl.gz"
+      - "2022_part_10.jsonl.gz"
+      - "2022_part_11.jsonl.gz"
+      - "2022_part_12.jsonl.gz"
+      - "2022_part_13.jsonl.gz"
+      - "2022_part_14.jsonl.gz"
+      - "2022_part_15.jsonl.gz"
+      - "2022_part_16.jsonl.gz"
+  - config_name: "2023"
+    data_files:
+      - "2023_part_00.jsonl.gz"
+      - "2023_part_01.jsonl.gz"
+      - "2023_part_02.jsonl.gz"
+      - "2023_part_03.jsonl.gz"
+      - "2023_part_04.jsonl.gz"
+      - "2023_part_05.jsonl.gz"
+      - "2023_part_06.jsonl.gz"
+      - "2023_part_07.jsonl.gz"
+      - "2023_part_08.jsonl.gz"
+      - "2023_part_09.jsonl.gz"
+      - "2023_part_10.jsonl.gz"
+      - "2023_part_11.jsonl.gz"
+      - "2023_part_12.jsonl.gz"
+      - "2023_part_13.jsonl.gz"
+      - "2023_part_14.jsonl.gz"
+      - "2023_part_15.jsonl.gz"
+  - config_name: "2024"
+    data_files:
+      - "2024_part_00.jsonl.gz"
+      - "2024_part_01.jsonl.gz"
+      - "2024_part_02.jsonl.gz"
+      - "2024_part_03.jsonl.gz"
+      - "2024_part_04.jsonl.gz"
+      - "2024_part_05.jsonl.gz"
+      - "2024_part_06.jsonl.gz"
+
 ---
 
 This dataset is the result of processing all WARC files in the [CCNews Corpus](https://commoncrawl.org/blog/news-dataset-available), from the beginning (2016) to June of 2024.
@@ -127,17 +234,26 @@ Sample Python code to explore this dataset:
 
 ```python
 from datasets import load_dataset
+from tqdm import tqdm
 
-# Load the dataset in streaming mode
-dataset = load_dataset("stanford-oval/ccnews", streaming=True)
+# Load the news articles for the year 2016, in streaming mode
+dataset = load_dataset("stanford-oval/ccnews", "2016", streaming=True)
 
 # Print information about the dataset
 print(dataset)
 
-# To demonstrate streaming, we can iterate over a few examples
+# Iterate over a few examples
 print("\nFirst few examples:")
 for i, example in enumerate(dataset["train"].take(5)):
     print(f"Example {i + 1}:")
     print(example)
     print()
+
+# Count the number of articles (in 2016)
+row_count = 0
+for _ in tqdm(dataset["train"], desc="Counting rows", unit=" rows", unit_scale=True, unit_divisor=1000):
+    row_count += 1
+
+# Print the number of rows
+print(f"\nTotal number of articles: {row_count}")
 ```