A small tool to view real-world ActivityPub objects as JSON! Enter a URL
or username from Mastodon or a similar service below, and we'll send a
request with
the right
Accept
header
to the server to view the underlying object.
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"content": "<p>Did you know that you can caption images or retrieve characters from an image (known as OCR) using Image-to-Text models? ๐ผ๏ธ๐ <br />If you want to use them, we've got you covered ๐ค<br />See this task page to learn about it ๐ huggingface.co/tasks/image-to-text</p>",
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"tr": "<p>Did you know that you can caption images or retrieve characters from an image (known as OCR) using Image-to-Text models? ๐ผ๏ธ๐ <br />If you want to use them, we've got you covered ๐ค<br />See this task page to learn about it ๐ huggingface.co/tasks/image-to-text</p>"
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"content": "<p>Me & Alara Dirik have done a write-up on our disaster response efforts using machine learning, released in Hugging Face blog <a href=\"https://t.co/yiNVylxNER\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"\">t.co/yiNVylxNER</span><span class=\"invisible\"></span></a></p>",
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"tr": "<p>Me & Alara Dirik have done a write-up on our disaster response efforts using machine learning, released in Hugging Face blog <a href=\"https://t.co/yiNVylxNER\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"\">t.co/yiNVylxNER</span><span class=\"invisible\"></span></a></p>"
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"content": "<p>Our blog post on skops is out! <a href=\"https://www.kdnuggets.com/2023/02/skops-new-library-improve-scikitlearn-production.html\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://www.</span><span class=\"ellipsis\">kdnuggets.com/2023/02/skops-ne</span><span class=\"invisible\">w-library-improve-scikitlearn-production.html</span></a> ๐๐ปโโ๏ธ @adrin@kolektiva.social</p>",
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"tr": "<p>Our blog post on skops is out! <a href=\"https://www.kdnuggets.com/2023/02/skops-new-library-improve-scikitlearn-production.html\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://www.</span><span class=\"ellipsis\">kdnuggets.com/2023/02/skops-ne</span><span class=\"invisible\">w-library-improve-scikitlearn-production.html</span></a> ๐๐ปโโ๏ธ @adrin@kolektiva.social</p>"
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"conversation": "tag:sigmoid.social,2023-01-21:objectId=5254774:objectType=Conversation",
"content": "<p>Hello ๐๐ผ <br />I just uploaded all of my cheatsheets (there's 100+ of them!) on various topics including machine learning, data structures, statistics and more.<br />๐ GitHub repository: <a href=\"https://github.com/merveenoyan/my_notes\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">github.com/merveenoyan/my_note</span><span class=\"invisible\">s</span></a><br />๐ Hugging Face dataset repository: <a href=\"https://huggingface.co/datasets/merve/my_notes\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">huggingface.co/datasets/merve/</span><span class=\"invisible\">my_notes</span></a></p>",
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"tr": "<p>Hello ๐๐ผ <br />I just uploaded all of my cheatsheets (there's 100+ of them!) on various topics including machine learning, data structures, statistics and more.<br />๐ GitHub repository: <a href=\"https://github.com/merveenoyan/my_notes\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">github.com/merveenoyan/my_note</span><span class=\"invisible\">s</span></a><br />๐ Hugging Face dataset repository: <a href=\"https://huggingface.co/datasets/merve/my_notes\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">huggingface.co/datasets/merve/</span><span class=\"invisible\">my_notes</span></a></p>"
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"conversation": "tag:sigmoid.social,2023-01-20:objectId=5169908:objectType=Conversation",
"content": "<p>You also don't even need to host them openly, when pushing, set `private` to True and your repositories will be completely private (or if you work with an organization, people in your organization can see when you push to your org, e.g. a lab, company or an ML competition team)</p>",
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"tr": "<p>You also don't even need to host them openly, when pushing, set `private` to True and your repositories will be completely private (or if you work with an organization, people in your organization can see when you push to your org, e.g. a lab, company or an ML competition team)</p>"
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"content": "<p>A cool thing with versioning in Hugging Face Hub is that you can access the info in the model card programmatically to run analyses on your experiments, e.g. run multiple experiments, automatically create and push model cards for all of them, pull info from model cards and write a small script for analysis on which model is the best! ๐</p>",
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"tr": "<p>A cool thing with versioning in Hugging Face Hub is that you can access the info in the model card programmatically to run analyses on your experiments, e.g. run multiple experiments, automatically create and push model cards for all of them, pull info from model cards and write a small script for analysis on which model is the best! ๐</p>"
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"content": "<p>skops already saves some of the above information for you, and rest can be added (as table, metric, plots and more!) so you can just create a script for your training and run it every time you train a model<br />and it's a very light dependency โ๏ธโจ</p>",
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"tr": "<p>skops already saves some of the above information for you, and rest can be added (as table, metric, plots and more!) so you can just create a script for your training and run it every time you train a model<br />and it's a very light dependency โ๏ธโจ</p>"
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"content": "<p>When versioning your experiments, it's best to keep couple of information for better reproducibility:</p><p>๐ง๐ปโ๐ฌ Your hyperparameters and attributes of preprocessors and architecture (pipeline)<br />๐ Metrics<br />๐ Feature importances (which I used ELI5 for)<br />โ
Requirements of your environment</p>",
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Requirements of your environment</p>"
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"content": "<p>I just released a new notebook that demonstrates one of my favorite libraries ELI5 and how to version your models with model card for experiment tracking using skops ๐ง๐ปโ๐ฌ๐</p><p>๐ Notebook: <a href=\"https://kaggle.com/code/unofficialmerve/explainability-and-versioning\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">kaggle.com/code/unofficialmerv</span><span class=\"invisible\">e/explainability-and-versioning</span></a><br />๐ Resulting repository: <a href=\"https://huggingface.co/scikit-learn/xgboost-example\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">huggingface.co/scikit-learn/xg</span><span class=\"invisible\">boost-example</span></a><br />Couple of cool things ๐งต</p>",
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"content": "<p>productive weekend ๐ซ </p>",
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"content": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://techhub.social/@RecursiveNeuron\" class=\"u-url mention\">@<span>RecursiveNeuron</span></a></span> kaggle and stackoverflow surveys and reports show otherwise though</p>",
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"content": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@kbaxter\" class=\"u-url mention\">@<span>kbaxter</span></a></span> if I was left on a deserted island and had to pick a model it would be this zero shot BART model for sure ๐ซ ๐</p>",
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"content": "<p>See below how to use zero shot classification models with few lines of code ๐๐ผ</p>",
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"content": "<p>Zero shot text classification is the most underrated and useful task for various NLP tasks ๐</p><p>These models can predict what a given text is about without explicitly being trained on the classes ๐คฏ You can use these models off the shelf for topic classification or sentiment analysis, or even use it for labelling! ๐ท</p><p>Learn more here ๐๐ผ <a href=\"https://huggingface.co/tasks/zero-shot-classification\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">huggingface.co/tasks/zero-shot</span><span class=\"invisible\">-classification</span></a></p>",
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"content": "<p>Since this is open-source & crowd-sourced, you can contribute your app to the documentation too! <br />Get started here: <a href=\"https://huggingface2.notion.site/Add-your-Space-to-Tasks-d7ad0ed035674c3bbd5909188565f872\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">huggingface2.notion.site/Add-y</span><span class=\"invisible\">our-Space-to-Tasks-d7ad0ed035674c3bbd5909188565f872</span></a></p>",
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"content": "<p>Our crowdsourced documentation project Tasks at Hugging Face now include example Spaces for a given task! ๐ <br />Check them out at <a href=\"http://huggingface.co/tasks\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">http://</span><span class=\"\">huggingface.co/tasks</span><span class=\"invisible\"></span></a> ๐</p>",
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"content": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://jawns.club/@vicki\" class=\"u-url mention\">@<span>vicki</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://social.jvns.ca/@b0rk\" class=\"u-url mention\">@<span>b0rk</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://mastodon.social/@SebRaschka\" class=\"u-url mention\">@<span>SebRaschka</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@JayAlammar\" class=\"u-url mention\">@<span>JayAlammar</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://recsys.social/@eugeneyan\" class=\"u-url mention\">@<span>eugeneyan</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://recsys.social/@brohrer\" class=\"u-url mention\">@<span>brohrer</span></a></span> hehe thank you Vicki! adding more to the list ๐๐ผ <br /><span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@akhaliq\" class=\"u-url mention\">@<span>akhaliq</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@minimaxir\" class=\"u-url mention\">@<span>minimaxir</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://mastodon.social/@SebRaschka\" class=\"u-url mention\">@<span>SebRaschka</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://macaw.social/@julien_c\" class=\"u-url mention\">@<span>julien_c</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@osanseviero\" class=\"u-url mention\">@<span>osanseviero</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://mastodon.social/@mmitchell_ai\" class=\"u-url mention\">@<span>mmitchell_ai</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://mastodon.social/@SashaMTL\" class=\"u-url mention\">@<span>SashaMTL</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@anton_l\" class=\"u-url mention\">@<span>anton_l</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@pcuenq\" class=\"u-url mention\">@<span>pcuenq</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@abidlabs\" class=\"u-url mention\">@<span>abidlabs</span></a></span></p>",
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"content": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@knutjaegersberg\" class=\"u-url mention\">@<span>knutjaegersberg</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@minimaxir\" class=\"u-url mention\">@<span>minimaxir</span></a></span> I think FLAN isn't benchmarked but it seems very promising.<br />I feel like for abstractive I didn't observe much difference between BART and T5 back when I was working with them ๐
I will check FLAN ๐ฎ</p>",
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"tr": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@knutjaegersberg\" class=\"u-url mention\">@<span>knutjaegersberg</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@minimaxir\" class=\"u-url mention\">@<span>minimaxir</span></a></span> I think FLAN isn't benchmarked but it seems very promising.<br />I feel like for abstractive I didn't observe much difference between BART and T5 back when I was working with them ๐
I will check FLAN ๐ฎ</p>"
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"content": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@knutjaegersberg\" class=\"u-url mention\">@<span>knutjaegersberg</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@minimaxir\" class=\"u-url mention\">@<span>minimaxir</span></a></span> I just checked for both:<br /><a href=\"https://paperswithcode.com/task/extractive-document-summarization\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">paperswithcode.com/task/extrac</span><span class=\"invisible\">tive-document-summarization</span></a><br /><a href=\"https://paperswithcode.com/task/abstractive-text-summarization\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">paperswithcode.com/task/abstra</span><span class=\"invisible\">ctive-text-summarization</span></a><br />it depends on dataset apparently but what I said is still applicable. if I did applied ML I'd use longformer as seq2seq models are hard to get right for generation and tend to speak gibberish</p>",
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"tr": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@knutjaegersberg\" class=\"u-url mention\">@<span>knutjaegersberg</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@minimaxir\" class=\"u-url mention\">@<span>minimaxir</span></a></span> I just checked for both:<br /><a href=\"https://paperswithcode.com/task/extractive-document-summarization\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">paperswithcode.com/task/extrac</span><span class=\"invisible\">tive-document-summarization</span></a><br /><a href=\"https://paperswithcode.com/task/abstractive-text-summarization\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" translate=\"no\"><span class=\"invisible\">https://</span><span class=\"ellipsis\">paperswithcode.com/task/abstra</span><span class=\"invisible\">ctive-text-summarization</span></a><br />it depends on dataset apparently but what I said is still applicable. if I did applied ML I'd use longformer as seq2seq models are hard to get right for generation and tend to speak gibberish</p>"
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"content": "<p><span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@knutjaegersberg\" class=\"u-url mention\">@<span>knutjaegersberg</span></a></span> <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@minimaxir\" class=\"u-url mention\">@<span>minimaxir</span></a></span> depends on whether you want it generative or extractive, extractive ones tend to be more stable. I might try ChatGPT but I'd say for abstractive it's a seq2seq model (BART) and for extractive it's maybe BERT based or something like longformer</p>",
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