Buckets:
🤗 Datasets, check![[datasets-check]]
Well, that was quite a tour through the 🤗 Datasets library -- congratulations on making it this far! With the knowledge that you've gained from this chapter, you should be able to:
- Load datasets from anywhere, be it the Hugging Face Hub, your laptop, or a remote server at your company.
- Wrangle your data using a mix of the
Dataset.map()andDataset.filter()functions. - Quickly switch between data formats like Pandas and NumPy using
Dataset.set_format(). - Create your very own dataset and push it to the Hugging Face Hub.
- Embed your documents using a Transformer model and build a semantic search engine using FAISS.
In Chapter 7, we'll put all of this to good use as we take a deep dive into the core NLP tasks that Transformer models are great for. Before jumping ahead, though, put your knowledge of 🤗 Datasets to the test with a quick quiz!
Xet Storage Details
- Size:
- 933 Bytes
- Xet hash:
- 647679f7a3ca0e6547c494527f46642fe7260afc11b618d84fdda518f5b22c6e
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.