The Epic 🤗 Hugging Face Tasks Overview

Ever felt behind in any Machine Learning tasks related to natural language processing, computer vision or audio recognition? You need to try out ‪@HuggingFace‬ (huggingface.co/), it provides thousands of pre-trained models to perform tasks in those different modalities, as well as the pipeline API to quickly download and use thise models.
In this tutorial, I'll show you a quick overview of all the ML tasks you could solve using Hugging Face pipelines, the basic API for straightforward inference. I'll be building a lot of small Streamlit apps to demonstrate each use case. There will also be teasers of all you can accomplish through the HF ecosystem, from finetuning a model on your own dataset, choosing a custom model, breaking down a pipeline to a tokenizer or processor and model, and hosting your demo on Hugging Face.
This video will be inspiring enough for you to actually try the library to supercharge your next ML project!
💵 Donate a coffee to keep me awake while editing: www.buymeacoffee.com/andfanilo
🐦Follow my Streamlit/ML/KZbinr updates on Twitter: / andfanilo
🗣️ My landing page: andfanilo.com/
👉 Links
- All the ML tasks: huggingface.co/tasks
- The actual model hub: huggingface.co/models
- Write with Transformer demo: transformer.huggingface.co/
- Thomas Wolf interview for TWIML about the start of HF: • Big Science and Embodi...
My tools (Affiliate links)
- 🎵 Music (Epidemic Sound) - www.epidemicsound.com/referra...
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🪶 A year after my first Epic video about Streamlit, here's my newest Epic video, this time about HF. Those take a loooot of time to create, I'd be grateful for a supportive comment, share or buy me a coffee donation...it would mean a lot, for real! Thanks for watching and reading so far!
⚠️ Disclaimer: This video was made possible by Hugging Face. All views and opinions expressed are solely those of the creator, and do not reflect those of Hugging Face.
Links included in this description might be affiliate links. If you purchase a product or service with the links that I provide I may receive a small commission. Thank you for supporting my channel so I can continue providing you with free content!
#streamlit #python #datascience #dataapps

[@mablewheeler4911]

Such a wonderful video. Funny, informative, clear. Keep up the awesome work!

[@andfanilo]

Oh, thank you so much for the positive feedback, you made my day :) what are you looking into building? I'll keep doing it, I hope to see you around!

[@mehdio]

Awesome work! Like the vibe, it was definitely entertaining to watch :)

[@andfanilo]

Thanks, glad you enjoyed this once in a while highly edited video, I'll go back to low-level video recordings now 😆

[@shamaldesilva9533]

WOW , now this is impressive 😍😍😍

[@andfanilo]

Thanks for the support :)

[@joelmontano6562]

Thank you for putting together an overview! it's definitely a must watch before diving into the hugging face library

[@andfanilo]

Thanks for watching :) good luck on your future Huggingface projects! What will you build?

[@orkhanamrullayev3966]

such a dynamic hands-on into to HF transformers! Great content! :)

[@andfanilo]

🔥🔥 thanks for watching! Hope it got you pumped up to building a new HF demo :D

[@theovdhd]

What a high-quality video!

[@andfanilo]

Thanks for watching :) Happy Huggingfacing!

[@hrushikway]

amazing video. loved it.

[@andfanilo]

Thanks for watching, glad you enjoyed it :D

[@1littlecoder]

Congratulations on the sponsor 🔥 Amazing video as usual!

[@andfanilo]

Thank you so much. This video has been a wild ride to edit!

[@kombosabinho]

I found you randomly on tweet and not even 30% into the video I subscribed. You are awesome bro 😎

[@andfanilo]

Welcome aboard Twitter follower! Not all videos will be of this high quality as this one was a special one XD but it'll still be mostly like this so I hope to still see you around on future videos 😊

[@ajithsanjeevapoojary]

Great Video ... Informative

[@andfanilo]

Thanks for watching 😁

[@proudindian3697]

Wow..!!nice video..!! Thanks

[@andfanilo]

Thanks for watching :D very appreciated

[@SophiaYangDS]

Amazing video 🔥🔥🔥

[@andfanilo]

💖💖 thanks for the support Sophia! Time for a little break now ahah!

[@SophiaYangDS]

@@andfanilo so proud of you! You deserve a nice break and some great food 🙌

[@andfanilo]

@@SophiaYangDS You too :) hope your month went as smoothly as it could be! Yeah you're right, I'm going to treat myself now, some pizza ould be nice ☺

[@adzrocable]

Hi Fanilo! Thanks for the overview! What is the best approach for us to build a query machine that will be able to query our dataset? I've tried openai functions for this and it worked. But not able to figure it out using huggingface. Thanks!

[@andfanilo]

Hey there! If I understood the question correctly, I haven't tested any of those approaches but I've seen PandasAI + HF Models work yet being a little slower than OpenAI ( docs.pandas-ai.com/en/latest/LLMs/llms/#huggingface-models , I did a PandasAI prototype video here kzbin.info/www/bejne/aWHCYZ1mjb6Cl9U , replacing the LLM argument by a HF Model should work ), and I've seen Huggingface Tools & Agents ( huggingface.co/docs/transformers/transformers_agents & huggingface.co/docs/transformers/custom_tools ) but if you scroll a little on the page it doesn't seem they promote Dataset Q&A that much so I'm not sure it's well integrated compared to text-to-image + there's a little note I'm going to quote "If you’re facing issues, we recommend trying out the OpenAI model which, while sadly not open-source, performs better at this given time." I don't have any real recommendation, but if you test those approaches I'd love to hear back from you :) Have a nice day!

[@bartgerritsen11199]

Love your content! I would like to see how a model can be used to answer questions about a given dataset, for instance; “What is the sum of column … for all dates after …”. Do you think it is possible?

[@andfanilo]

Thanks, looks like an interesting problem Well as a first try, if your dataset is in a dataframe: ``` data = {"a": ["2", "5", "19"], "b": ["87", "53", "69"], "c": ["1990/02/01", "1995/02/01", "2000/02/01"]} table = pd.DataFrame.from_dict(data) query = "Can you sum b for all rows of column c after year 1993?" pipeline(task="table-question-answering")(table=table, query=query) ``` did retrieve the correct cells for me, then you would need to do the necessary aggregation. Though to be honest it then depends on the kind of queries you want to build. If the vocabulary of queries is limited, I would actually try to convert it manually into a Pandas .query(). If the queries are very complex, and not really looking like SQL queries, I don't know how table QA models will fare. But as long as the query is similar to a SQL query expressed with natural language, Table Q&A huggingface.co/tasks/table-question-answering should work. In the future, you may look into higher level APIs like Haystack (haystack.deepset.ai/tutorials/15_tableqa) or Langchain (python.langchain.com/en/latest/use_cases/tabular.html#) for robust table QA pipelines. Hope it helps! I'm not really an expert in the NLP field ^^, so the HF Discord (huggingface.co/join/discord) or forum (discuss.huggingface.co/) are also good places to ask :) Have a nice day!

[@bartgerritsen11199]

Very helpful, thank you so much!❤

[@user-hv4ku2pz4z]

Great content as usual! I found plenty of useful information. Could you pls show us how to make an application using transformer tools which would do exactly what you said at 15:14? Is it possible to make it as you described at 14:46? Thanks

[@andfanilo]

Thanks for the support and for watching :) At 15:14 on video transcription you can read the whisper documentation on Hugging Face: huggingface.co/openai/whisper-large-v2, it has a good starting point. Or watch @1littlecoder's video example using the whisper library directly (kzbin.info/www/bejne/gaWkd4iVrcp7qJY or kzbin.info/www/bejne/r6isqpZuoZZ-Z7M) though it's colab/Gradio. Another rec I have is @AssemblyAI's APIs from a Streamlit app but that's a closed API :) I'll think about a Streamlit video for it, since subtitling quickly a KZbin video has actually been in my TODO list Have a nice day!

[@explorer945]

can I get the link for the neural style transfer you were showing 0:16

[@andfanilo]

This one style-transfer-webrtc.streamlit.app/ ?

[@vhater2006]

Look like we gonna all end by having a J.A.R.V.I.S like at home running on a solar SBC just waiting a 64threads model

[@faqeerhasnain]

your video has some quality knowledge, but with all respect, I dont understand the sense of that guy speaking in between and you are trying to be funny, it doesn't look funny at all, but weird and annoying, Pleaseeee don't do it next time, its reallyyyy distracting...

[@andfanilo]

Ah that's too bad, thanks for the feedback though Have a nice day!

[@jonyfrany1319]

❤ The best 🎉 video for learning 🫡

[@andfanilo]

Thank you for the support, much appreciated 😍