Update README.md

README.md

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     path: data/train-*
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-# Red Pajama's High Quality Spanish subset
+# RedPajama's High Quality Spanish subset
 
 ## What is this?
 
-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 for [FineWEB-Edu](https://arxiv.org/abs/2406.17557).
+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).
 
 ## Dataset creation
 
-In a nutshell, we use Llama-3.1-70B to grade the educational quality of various samples from the original dataset. Then, we used these 500K samples to train a classifier using an encoder-based model, so that it learns to assign a score from 0 to 5. Since this model is way cheaper to use than an LLM, we run it over the entire dataset, thus getting a high-quality section from it.
+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.
 
 Here is an overview of the architecture:
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b15c3f20037ec5d7c91aa6/H5xPOHy_4RhMEDtGvsnTE.png)
 
-For more detailed information on how this dataset was created, refer to [our implementation](https://github.com/latam-gpt/llm-data-eval).
+For more detailed information on how this dataset was created, refer to [our open implementation](https://github.com/latam-gpt/llm-data-eval).
 
 ## License