Update README.md

README.md

@@ -30,6 +30,27 @@ configs:
 
 The following is a high-quality dataset distilled from the Spanish subsection of [RedPajama-Data-v2](https://github.com/togethercomputer/RedPajama-Data), created using the methodology proposed in [FineWEB-Edu](https://arxiv.org/abs/2406.17557).
 
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("latam-gpt/red_pajama_es_hq") 
+```
+
+### Filtering by quality score
+
+Documents in this corpus are scored on academic quality from 2.5 to 5, with higher scores indicating better quality. The dataset can be filtered by score using standard filtering methods.
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("latam-gpt/red_pajama_es_hq")
+
+# filter the dataset for scores > 3
+filtered_ds = ds.filter(lambda x: x['score'] > 3)
+```
+
 ## Dataset creation
 
 In a nutshell, we use Llama-3.1-70B to grade the educational quality of 550k samples from the original dataset. Then, we used these samples to train a encoder-based classifier, so that it learns to assign a score from 0 to 5. Since this model is cheaper to use than an GPT, we can run it at scale over the entire dataset, thus allowing us to filter a high-quality section from it.