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I feel like the most important thing on Hugging Face is actually Hugging Face Hub. So basically at Hugging Face we are trying to solve open source machine learning in general and this involves a couple of problems. One of them is reproducibility of your experiments and also how easy to infer your models are such that people can just go and stress test your models and see if it works for your use case. The essence of the open source ML in general that can your model actually be used by someone else?

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Welcome to another episode of Practical AI. This is Daniel Whitenack. I'm a data scientist with SIL International and, and I'm joined as always by my co host Chris Benson, who is a tech strategist at Lockheed Martin. How you doing, Chris?

I'm doing okay, Daniel. So good to be talking to you today.

It is, it's, it's wonderful. It's a little bit of an odd day. I'm joining from a hotel in Dublin because I'm attending acl, which for those that don't know is one of the big NLP conferences in the research world. And that's been fun. It's been tiring because I just forgot how tiring an in person conference was.

Chris. So is it everyone else dragging around too? Are they all just over?

I think so. There's, there's not consistent coffee in all the places, which is unfortunate. So that's, that's tough. But one thing that I have heard mentioned quite a bit at ACL consistently is hugging face. And we're really privileged today to have Mirva from Hugging Face with us. She's a developer, advocate, engineer at Hugging Face. Really creating a lot of great content on the web and great tutorials and also making really significant contributions on the open source side. I'm really excited to have you with us, Mirva.

I'm so happy that you have invited me. Thank you so much.

Yeah, great to have you here. I'm wondering if you're, if you could kind of give us a little bit of the backstory of how you got connected with Hugging Face and nlp maybe more generally. Like what was the state of sort of hugging face when you joined? Because we'll be talking a lot about like kind of how it's progressing in this episode, but I'm wondering kind of what its state was when you joined and what got you excited about it at that time.

Great question. So I have met NLP at my senior year at the university actually, and literally my first project was text mining and doing a classification using naive bias in R about climate change sentiments. You know, if people believe it or not. I have scraped some tweets and, you know, classified them in a data science class that I have taken and I was like, I'm going to make this my job. And then I later joined to boot camps, did masters and you know, started working somewhere as a machine learning engineer. You know, did everything I could. I worked two years as a machine learning engineer doing mostly natural language processing and also a bit of analytics on the side. I was building chatbots, I was doing information retrieval tools and so forth and I was using Hugging Face back then, but prior to that, prior to building information retrieval tools and also like putting Hugging Face into my chatbot with the birth embeddings and I how I met Hugging Face was that I have watched Thomas Wolf's video on future of NLP and I was like, he's explaining it so well and I have become a fan. And then once I have seen him posting about this community Sprint on data sets and I was like, can I join as well? And then I started contributing to Hugging Face and then I later tried my chances with the audio Sprint as well and I was like, I met people over there and I have learned a lot of things, like, things like, you know, like they have taught me about cicd, styling, formatting and contributing to open source actually, which is, it was my first time contributing to open source and I was so excited when my first PR got merged and I was like, you know, I kept helping other people out over there and I was like, I'm going to be that person that is going to help people out in this.

So that's, that's so amazing. I love that story because it is very intimidating for a lot of people to figure out that process of committing an open source. And of course some communities are more welcoming than others. I know Hugging Faces is very welcoming and there's a lot of great discussion that happens and all of that, but this is a little bit off topic. But I think it's on on topic at least as far as the open source side of Hugging Face. What sort of advice would you give people that are looking maybe to start contributing to the open source side of data science or machine learning or that side of the world, the open source community that maybe not even just with Hugging Face, but there's so many great tools out there, whether it's Spacey or TensorFlow, it's you know, itself or all sorts of things.

So Basically libraries like SciKit learn or you know, Hugging Face Transformers occasionally have sprints in which the contributors are, you know, talking to you, you know they are giving you issues or like if there is a for instance, like if we are going to train models, there is a list of languages that will models need to be trained on and there's a data set. So all you have to do is to actually train the model and you know, improve it. So there are a couple of sprints also. Same with SciKitlearn as far as I know. You know, you can get help from contributors actively rather than being in async in GitHub and I would suggest to be, you know, aware of those sprints and community events like in Hugging Face we have a lot of them. For instance, recently we had like a sprint about renewing the docs and adding type hints and other stuff for the TensorFlow side of transformers, which was a good first contribution in my opinion. So I think sprints are a good way to begin with and other than that it's just good first issues. On the repository you mentioned that you.

Were kind of building chatbots before joining Hugging Face. Could you tell us a little bit more about that and like maybe how that shaped what you perceived as what was kind of needed in NLP tooling? It sounds like you found a lot of what you thought was needed in terms of Hugging Face and Transformers. But how did that process of like trying to build a chatbot, what did that teach you or help you learn or maybe introduce to you in terms of challenges for people wanting to do that sort of thing.

So it heavily depends on what you're building actually. So in my first job I was building an automation bot that was talking to you and automatically creating appointment for you or cancel your appointment in the background for us. For service companies over there, I was mostly doing machine learning Part it is usually about how you solve the text classification problem and improve your data. I was using GRASS open source and if you are having a narrow domain chatbot like, I don't know, pizza ordering chatbot or whatever, it's a very easy to solve problem because mostly you are solving a text classification problem at the end, trying to understand the end user and like to iterate over Your model and everything. It's easy. For my second job, it was really hard because I come from an applied math, operations research type of background and I do not have any developer back background. And I had to do the API side and you know, learning flask and everything. I was doing both the backend and the chatbot itself. And I was also building this tool that was, that helped. We had some researchers. The chatbot was about, you know, like it was like a friendly chatbot, sort of like replica. But you could ask questions to it about your life standards or like you can ask questions like, hey, I cannot sleep, what can I do about it? And we had like a researcher team that was looking into these answers in the papers and looking for statistical evidence that certain thing is good for your health. But the chat was rather so hard to make because basically conversational agents are divided into two. You have stuff like blunderbots, dialogpt, generative models that basically you can talk about anything like T0, whatever. The second part is chatbots that are based on intent and action, which is like you have to write your own training data. It's not anything like zero shot or whatever. You have to define all of your actions to every single intent. It's hard to make a generalization over this and still be in control of what your bot is going to say. Because we know that these language models are a bit biased. You know, they tend to be sexist, racist, rude sometimes. So like it's a hard problem to solve. So that's why I kind of quit on doing that because like I kind of gave. I would rather, you know, work in a, you know, chatbot that had narrower domain because like it's not solvable basically without language models. And like with language models, I would rather not put a language model in front of an end user freely with no filtering or whatever. So. And I also like help them out. Like there was this research team and I have built a tool that would answer their questions from the research papers. And for that I have used sentence transformers which I was using through hugging face. Like first time I can, I cannot forget how I use Pipeline for the first time. And I was incredibly amazed on. I was like, it's just one line of code and I can just get answers to my question. I just pass my model. I have fine tuned the model based on some. Basically there was this biomedical birth and I have fine tuned it on some task for information retrieval. And I called Pipeline and I was like, is this for real? Like, does this actually work? And I was pretty Amazed. And then later I looked into it and I was like, they made an abstraction over, you know, all of the, you know, pre processing, inference and post processing and put it in a box. And like, how smart is this? You know, it's like an engineering marvel, you know, like it's just amazing.

So I had a totally different follow up question minute ago, but I'm actually wanting to ask you about this. I think a lot of the folks that we talk to on the show have come from developer backgrounds and they're kind of, they kind of already have that and they're moving into other skills and you've come in the reverse way from that and you kind of had this, this moment there. What was the hardest thing as you were transitioning into this skill set, you know, as you're talking about this history and that was kind of an awe moment that you had. What's the hardest thing that to kind of move into being able to be productive.

So basically in my previous job I was just shipping stuff and you know, like my, you know, quality of my code wasn't nitpicked and everything to like, you know, my PRs weren't passing very, you know, in a long time. So in here, because we are working in, I'm working with, you know, like very big teams and very big code basis, I can see how I can refactor things or you know, like how can, how I can improve things. So I'm mostly learning development in a way and also like how UX matters. That's a very like, I feel like it's a billion dollar question, how do you handle your UX and how you develop tools? Because like most of my time at Hugging Face is actually passing with either developing a tool for people. On the Keras side, I recently started working on SciKitlearn as well. On the other side, I'm just building fancy demos to showcase people what Transformers can do or other libraries can do for machine learning in general. I have realized later on that UX actually is hard when you do not come from that background. And also how you can improve your code, it's just endless. There will always be someone nitpicking your code and it's just the most beautiful thing because you keep learning from that. So I am just grateful to work here and I feel like I did improve myself from the start, but it was like at first it was hard because previously I was only optimizing my models and you know, nobody questioned my quote, you know, that much.

Merva, you've already mentioned like a Number of things that I'd love to dig into a little bit deeper because there's all sorts of pieces of the puzzle that fit into sort of what Hugging Face is and the ecosystem. I was wondering if you could kind of help us just like set the stage for this discussion with you have Hugging Face. You have sort of model and data set stuff, you have Transformers somehow Keras and like you even mentioned Scikit Learn. Could you just kind of give us an overview of like, you know, how you would see the Hugging Face ecosystem and how the various pieces fit together.

Yeah, sure. Though, like what Hugging Face is working on? Like if you were to ask to a random person in the company, I feel like the answers would differ. But I feel like the most important thing on Hugging Face is actually Hugging Face Hub. The reason why is because it's so basically at Hugging Face we are trying to solve, you know, open source machine learning in general. And this involves a couple of problems. Like one of them is reproducibility of your experiments and also how easy to infer your models are such that people can just go and stress test your models and see if it works for your use case. And another thing is, you know, like the essence of the open source ML in general that can your model actually be used by someone else for their own use case? Which is not, not likely for the most of the, you know, like tasks like tabular data related stuff. But you know, it applies for computer vision tasks, audio classification tasks at least within language or it applies for NLP because your features are usually universal. If not it's language specific. But you know, for, at least for computer vision you can do, you can just go ahead and just pick a object detection or like image segmentation model and use it in your use case. So with the Hub we are actually trying to do this and we want to have, we want to get people to declare the limitations of their model, declare the biases in their model so that we can have good open source models on the hub. And we don't only have transformers on the hub, we have like various libraries. So for instance we host the Stanza models from Stanford nlp, we have Keras models. Like we are integrating various libraries, lnlp, Keras, Pytorch image models, you name it. For instance, with spaces, what we want to do is we want to get people to see if a thing is possible. Like for instance I can just demonstrate a very small thing like a product and do a POC to my colleagues. There are like couple of use cases and things you can Use Hugging Face Hub for your end to end workflows. But like my favorite thing inside is I think Spaces right now because I'm a master's student. Most painful thing for me and I know for the TAs and professors is to actually reproduce my project and you know, like setting up the environments, you know, running it and you know, like you have to specify how to do that instead of, instead, like, you know, I'm just sending them the Spaces link of my project. For instance, this year I have, you know like Fourier transform space with Streamlit and I have just sent it to them. It's also good for, you know, like in my previous job I was like a machine learning engineer and I have built like in my first job I was looking for ways to just put my model out there and I had zero idea how to use Flask or you know, like write front end or whatever. Like it was so hard for me. Like, why would I be spending my time, like, especially if you're in a startup, like you do everything, why would I spend my time just to, you know, it's, it's. I'm not even putting this into production. I just want to showcase this to a client or like, you know, the end user. Like why would I spend most of my time just trying to put this over there through, I don't know, like just build a demo like that doesn't even look good with, I don't know, Flask and just channel it with Ngrok or whatever. I think when I was on board that being on board that Spaces wasn't better and like not so many people had access to it. When I discovered, you know, Spaces and Streamlit and Gradio and I was like, this really touches many pain points on that side, especially if you're a data scientist. Most of the data scientists are actually statisticians or you know, math folks who do not have development background but are working in startups. So it's actually very smart to just write like five lines of codes and just drag and drop your app py file into Spaces and voila. You can just show it to your clients or your end user or your teacher or your family or your favorite pets.

I have a question. That is you've been taking us through this and it kind of almost starts with the fact that as, you know, kind of having gotten to the expertise that you've gotten to, but you've kind of taken us through this development as you've taken this journey of learning and the ecosystem around Hugging Face has grown tremendously over that time and the tools are getting amazing. And as you are communicating this to people who are getting into it, you've got a big challenge just to communicate the ecosystem and all the things that are available. But how do you also, you clearly from, from what you were just talking about, kind of remember that beginner's mind. And so as you're bringing new people into the community and teaching them how to be effective and productive in what they're doing with within the ecosystem, how have you managed to stay grounded in that way so that you can accomplish both? You can sync with them at that beginner level and yet you can get them up to that point where they can run themselves.

It's really hard actually because there is so many good stuff in the ecosystem. It's just understanding the user journey and what they are going through and trying to touch where you can fix their problems in their journey. So Hugging Face recently started to invest in tabular data as well. And because I was previously a data scientist, I know what an average data scientist does. And I think a couple of things you can do is for instance use data sets library to host your data sets which in most of the platforms you cannot host data sets. That's more than 100 gigabytes by the way. And Hugging Face data sets allow you to do that. And you can even stream your data set. You know, like take your data set just do like an exploratory data analysis. You can if you want to do a presentation and if you don't want to show people notebook, you can do that through streamlet or gradual like you know, graphs about your data or like profile. And then after that you can just train your model and push it to the hub and build a space for it so that you can show what your model is capable of. You know, you can just put your baseline and see like let people test it so that it works. So I feel like the answer changes a lot according to what you are working on and like what side you are on on the equation. Like are you a machine learning engineer? Are you a data scientist? Also like it changes according to the person you are asking to you. But I really like to ask people about their journey and see what type of problem we can solve with that. So for instance, for end to end things, it's more like that. On the other hand, if you're an NLP person, you can again take a data set train a model with transformers for the previous use case you cannot do much with transformers because it's not used much in the Tabular data. But like we have a couple of integrations for the, you know, various libraries. And I can say like for instance, type of problems we are solving are like for instance, we want you to reproduce your experiments and like we want other people to know that models have limitations and everything. So for instance, currently what I'm working on on the hub is for the scikit Learn at least I want to enable collaboration for Scikit Learn. Like I am currently designing automated model cards for Scikit Learn in which it automatically produces model card that has your models attributes, also the data sets attributes. I have done the same for the Keras and for instance I have put inside metrics, you know, from model history. I have put models architecture using graphics. Like you can also have like tensorboard logs and you write like one line of code to just push your model to the hub and let it host your tensorboard logs and your model card over there. And if sometimes like if your model is working out of the box, then there is an inference widget as well. The same way you can just, with one line of code you can just load your model. Yeah, like we want to tackle reproducibility this way so that people know that this model has this metric, it has this hyperparameters that this, you know, training. We want to version them this way. So for nlp again you can just train a model, push it to the hugging face hub and just like the inference widget opens or like you can build a demo with again very few lines of code. Because like Gradio for instance, Gradio has the same philosophy as Transformers, I would say it has something called pipe. It leverages hugging faces pipelines to load an interface. So when you call interface load on a model, it automatically knows what type of input that model takes, what type of output that model takes. It will just create the interface for you in one line of code. I'm always amazed by the abstractions done to save your time as a developer. You know, I think every user has a different story. I would first get to know the person and tell them, you know, hey, you can do it like this. And just like you can utilize Hugging Face Hub like this because otherwise it's incredibly distributed. Like there is so many things in the ecosystem. I have also done a project called Hugging Face Tasks. I am still maintaining it. So I have come up with this when I was on board. So basically I have worked with a lot of software developers who wanted to build machine learning products. And I know that these people, they just need to know basic Python like if they want to do a POC to data scientists to actually express what they want, because they do not need to, like for the, for the poc, they do not need to know much about machine learning. So all they have to do is just go to Hub and you know, filter the models, find the model according to their use case and just call pipeline or inference API on it. So most of the people do not know that and they also do not know what the tasks are capable of, like what you can do with an object detection model or what you can achieve with the named entity recognition model. So I wanted to show them, hey, if you want to build X products, then you can just filter for these models and just call pipeline on that model and use it and like check the models metrics. You know, like if this metric is on this level, then this means that model is good. This model takes X as an input and outputs Y as an output. So that's why you can use it for, I don't know, information retrieval. It's also a bit complicated from the machine learning side. So many fancy things going on, but you actually do not know to need all of that. You just need to know which task is suited for you and you just need to call it. I have developed this with this beautiful team of developers and we have released that, I think it was around January or February. I just want to just go and tell every single software developer, hey, you just need to know this and you do not need to learn machine learning from the scratch.

So Merva, I. I can definitely hear like just the great respect that you have for your team and also like this collaborative environment that you're obviously a part of. And I know that even just today, the day that we're recording this, anyway, Hugging Face, you know, announced more collaborative features and community features on the Hub. I was wondering kind of from your perspective and how you've like grown to work internally with the Hugging Face team on different models and different tools and that sort of thing. What are you excited about in terms of the collaborative features of Hugging Face and what this might enable for the future of the Hub?

Good question. So we have announced the pull requests and the community feature today in which you can open a pull request to someone else's repository. And this repository can be a model repository which it contains model and the model files like configuration or tokenizer. If it's an NLP model, it has model card that improves reproducibility and open source machine learning. You have data set repositories in which you have Data sets, cards and data sets themselves. Or it can be a space repository in which it has the application file. Or if you do not host your model on Hugging face, it might have your model. So this way people can improve each other's works like we do in GitHub. So in here we do not want actually duplicate work of GitHub. But given hugging Face is mostly focused on models and the infrastructure as well. Like we use Git's large file system to host models and data sets that are very big. Previously we were versioning data sets and versioning models data set spaces. Like why not do pull requests on them? So this might mean like, for instance, I have a big tensorflow model and people want to use in Pytorch. Or for instance, you know, someone has a Pytorch model they want to. But I want to use it in the TensorFlow ecosystem because TensorFlow has like nice production level tools in the TensorFlow Extended. So if I want to, I can just port it, but if I also want to contribute those weights to the repository, then I can just do that. I can just open a pull request in order to contribute those tensorflow weights to that repository. Or if someone else has a space that is broken or needs to be improved, I don't know like by means of anything like needs a description or something like that, or like limitations that I have found in that space, if it has a bias that I have stress tested and needs to be declared, then I can just open a pull request or just discuss that, hey, I have found this bias in your model and either let's declare this or try to improve the model or if I have a data set then I can just change, I can just tweak stuff in the data set itself and just contribute that and also have like discussions regarding the models. Because like for instance, like if someone has a specific model that has that is using a different, different mask token or whatever they are, if their inference widget is problematic, I just want to go and tell them, hey, you can improve your model like this or you can improve your space like this. If you were to cache this function, then your space would be faster. You know, I just want to go and tell them that. But I cannot. I wasn't able to do it because like there was no way, except for, you know, like there's a Twitter handle on the people's profiles, which I think, like if I were to just go and tell them, it would be a bit creepy. So it's nice that we have like now, now we have A discussion section which I can, you know, just tell people, hey, if you were to do this, you know, your, that would be faster. Or if I, I can just open a pull request and let them see how their space is improved because then they can just clone and just pull and just, you know, serve it on their local or just make another space and just see before merging that how their work would look like how my work looks like on their space or model.

Yeah, I'm curious, you know, I think that'll be a really big change because you know, you've referenced GitHub and if you think about what GitHub did for the open source world by coming into being, Git was already there. The social aspect and that collaborative aspect, it fundamentally changed the community worldwide. Like, I mean it was not the same thereafter. Yeah, how do you envision the social changes or what do you aspire to or hope for for the changes based on this? Do you think it will propel the community in the same way, that same kind of massive shift that we saw in the, in the broader open source.

World for GitHub, it's literally my favorite social, probably all of us.

Good analogy, Chris.

Yeah, I just love, you know, like how to follow people and you know, see what they are starting, you know, to see interesting projects over there. And I feel like at some point we might evolve to that as well. You know, someone else stars of space and you know, like that's a really interesting one. So let me just go and look and like even more like maybe like, I don't know, messaging or whatever, I am not in control of this. I just know that, you know, like we are also trying to somehow like increase the collaboration and that's what GitHub actually achieved. Like there are awesome libraries out there where people contribute to, but for machine learning side it's not the optimal thing to use. Like for instance recruiters or like technical interviewers. They wouldn't go to all of my GitHub machine learning projects and even if they did, they won't understand anything. But like for instance I have spaces in which someone could just go ahead and see that hey, this person is actually doing in this space what I'm looking for. How did they achieve that? And maybe I should hire them or just hosting model weights, like very heavyweight model weights and just cloning them is a pain. Why would I just want to clone everything in a repository that is a model? I would love to see if that model works first through a widget or space and just do that. So for that Side, it's more optimal and we are looking for ways to increase the collaboration and like give people more like a better UX in collaboration with features like this. I'm also excited to see what's next. I feel like the what's next is, you know, not book weavers and such. I'm quite excited for that. So let's see what happens. I am like, I am not full in full control of the hub roadmap, but like it's mostly about the collaboration. Yesterday there was another feature launched and it was so in model cards you have a metadata section in which you define languages and everything. You can also define the models that are in a specific paper. It redirects you to the paper itself where you can see which model is actually there. So again, we are also investing in evaluation. So sort of like papers with code leaderboards in which you can see, you know, which model is state of the art in the task in the Hugging Face hub, so you can directly use that model. It's more about, you know, evaluating the model and doing a leaderboard style thing. It's mostly about again like open source machine learning what we are trying to do rather than social media nets work. But it might evolve that way.

Yeah, I have a digression that includes an ACL story. I forgot who I saw on Twitter yesterday. I forget who from Hugging Face said, we're going to announce something tomorrow. And I said, oh great, I'll, you know, I'll check.

I think it's Julian.

Yeah, Julian. Yeah. So I was like, okay, I'll check. But I was in talks most of the day, right. And the one talk, the last talk I went to today was called Quality at a Glance. It was from the Masakane group, which works in African languages, Julia and others. And they analyzed a whole bunch of open data sets, crawled data sets that are on Hugging Face as well, but they're sort of used widely. And they looked at the quality of those data sets and found very, very interesting and disturbing things. Like I think in common crawl aligned, there's certain languages, all of the data in that language is not in that language. Like 0% of the data labeled in that language is in that language. So one of those I think was like the romanized Arabic includes 0% Romanized Arabic. I was thinking about this as I was leaving ACL and then I was like, oh yeah, I got to check Twitter to see what hugging faces thing is. And then I looked and it was like, make PRs on data sets. And I was like, oh, I need to like Circle back around and go right back in there and like talk to them about how we can get some PRs on like CC aligned and other data sets. So it's just like perfect.

Yeah, yeah.

Perfect timing.

Yeah, yeah, yeah. Someone else can just open the, open a discussion about how that language doesn't come. We do like, we have like a really great ethics team. Like you probably know them. We have Meg, we have Sasha, we have Yasin. Like they are just doing amazing work. We recently have Jada and every time something happens we see an inappropriate use case around humans. For instance, the use cases around personal identifiable information is actually sometimes problematic. We do stress tests, the spaces and models to, you know, reach out to the people, hey, you know, your model might have bias, would you declare it? So we do actually care about the limitations around models and also ethical restrictions regarding, you know, you know, biases, personal information and everything as much as we can. So yeah, it's a hard problem to solve because it's all philosophical. Like in the end, like ethics is a bit philosophical but like how you can actually put that in practice is a big question. And in case of Hugging Face Hub, we do care about, in the models that we train in big science or the models that we have on the Hub, we do care that if it has a bias, we declare it. We care about the data and everything.

Yeah, it's so important. And yeah, I really appreciate hugging face really taking a stance there and putting a lot of effort into that. So as we kind of close out here pretty soon, you've mentioned a bunch of things that you either are working on or have worked on as part of the open source ecosystem around hugging face. I'm wondering what's that thing that keeps you up at night or the thing that's on your mind that you'd love to do or dig into but you haven't yet. What excites you or maybe like is, is something you want to dig into at some point in the future.

So in hugging face, like there is a certain group of people like me that do not really have specific things. Like in hugging face we just, nobody, nobody actually tells you, you know, you should do xy. You just, you know, go and pick a responsibility and that's your thing from that moment. So what I did was so far like I did the tasks and I did like a Keras integration in which I have done model cards and tensorboard and stuff. And like I really become so happy whenever I see Keras repository with the model card inside because I know that people actually Find it useful and just keep using it. I am currently working on how we can, how we can use Hugging Face Hub for the tabular data. So I'm working with an amazing scikit Learn core contributor that is currently in Hugging Face. He's Adrian. Like we are currently working on a package that is focused on how we can improve the production capabilities of Psychiturn. Because like you used for instance Pickle, which is, which can run arbitrary code on your machine. If you just pull any pickle and just deserialize it. It's a bit hard problem to tackle and we want to post whatever information we can have about the model. So like currently I'm working on that side. For instance, if it's a tree based model, you can visualize a tree. If it's a clustering model. Depending on what type of clustering model that is, it can be like a dendrogram or like a visual like with the pca or if you have like you know, linear model, you can put the hyperplane. So I am trying to, you know, put those stuff and also what model has learned through feature importance, shapi values and everything. So what I want is I want people to call one line of code and just push their models on the hub which will create these model cards with you know, several information. And I also am working like we are all supporting Gradio and I'm also working on how we can leverage Gradio for table or data stuff as well. Because like previously Gradio was mainly focused on the modalities like you know, text or audio or computer vision. And the components are, if you take a look at the documentation are focused on that. But like we can do, you know, like you can just drag and drop a data set and just automatically visualize everything regarding that data set. And that's quite magical. Like I remember first time I used Pandas profiling and also data analysis baseline library. You know, like there are a couple of libraries that enable you to profile your data sets, train baseline models, do like an automl like teapots. I remember using denim. I was like this is witchcraft. Like this is so good. You know, it's just, it saves so much time. And I was like, you know, recently I realized we can actually do that on the hub. Currently I am building like two tools. One is like profiling a data set and like a Gradio. It's like a Gradio interface that does it. We have recently released in Graduate something called Blocks which is more flexible than an interface. So you can, you have tabs and you have rows and you, you can put multiple stuff inside. So I'm currently building two spaces. Like as I told you, I either built like I maintain or add something to the Hugging Face Hub library or something else or do demos. So currently I'm working on something that is like an auto eda, like a Pandas profiling. And another thing is an automl tool sort of. So yeah, these type of things also like save a lot of time and also lower the barrier of entry, I think because like you have a baseline model and you can just, it will push your best baseline model to hub and maybe you can create like a space really easily on that, which you can later go and tell your local data scientist, hey, I want this but improved version. I really love this. You know, I was a big fan of hugging face for like, I don't know, since I saw Thomas's video. I think it's been two years or something, I don't remember. And I'm still a big fan of hugging face. Like I go and talk in, you know, Python conferences and people approach me, I say, I'm a big fan of your conference. And I'm like, me too.

That's great. Well, we're certainly big fans here and appreciate the way that you're building, the way that you're building really community and collaboration around AI and data sets and all of these things. So yeah, really appreciate your work, Mirva. And yeah, appreciate you taking time to talk to us. It was fun.

Thank you so much for inviting me. I enjoy, like talk. I could talk about hugging face all day and I would, you know, worry that people would get bored of me. So it's nice to meet people like you who are fans of hugging. You guys as well.

Of course. We'll talk to you soon.

Bye Bye.

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