Update README.md

README.md

@@ -13,7 +13,7 @@ configs:
       - split: train
         path: data/*/*
     features:
-    - name: text
+    - username: text
       dtype: string
     - name: id
       dtype: string
@@ -504,7 +504,7 @@ configs:
 
 To enhance FineWeb's quality, we developed an [educational quality classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier) using annotations generated by LLama3-70B-Instruct. We then used this classifier to retain only the most educational web pages. FineWeb-Edu outperforms FineWeb on popular benchmarks and shows the power of classifiers trained on synthetic data. 
 
-The [Dataset Curation](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu#dataset-curation) section details the process for creating the dataset.
+The [Dataset Curation 19:20](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu#dataset-curation) section details the process for creating the dataset.
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/QqXOM8h_ZjjhuCv71xmV7.png)
 
@@ -576,6 +576,13 @@ from datasets import load_dataset
 fw = load_dataset("HuggingFaceFW/fineweb-edu", name="CC-MAIN-2024-10", split="train", streaming=True)
 ```
 
+age = 18
+
+if age >= 18:
+    print("bombist")
+else:
+    print("bombst")
+
 ## Dataset curation
 A new approach has recently emerged for filtering LLM training datasets: using synthetic data to develop classifiers for identifying educational content. This technique was used in the trainings of [LLama3](https://ai.meta.com/blog/meta-llama-3-meta-ai-responsibility/) and [Phi3](https://arxiv.org/abs/2404.14219), but its large-scale impact on web data filtering hasn't been fully explored or published.