# orderedItems[].object.content
Did you know that you can caption images or retrieve characters from an image (known as OCR) using Image-to-Text models? 🖼️📄
If you want to use them, we've got you covered 🤗
See this task page to learn about it 👉 huggingface.co/tasks/image-to-text

# orderedItems[].object.contentMap.tr
Did you know that you can caption images or retrieve characters from an image (known as OCR) using Image-to-Text models? 🖼️📄
If you want to use them, we've got you covered 🤗
See this task page to learn about it 👉 huggingface.co/tasks/image-to-text

# orderedItems[].object.content
Me & Alara Dirik have done a write-up on our disaster response efforts using machine learning, released in Hugging Face blog https://t.co/yiNVylxNER

# orderedItems[].object.contentMap.tr
Me & Alara Dirik have done a write-up on our disaster response efforts using machine learning, released in Hugging Face blog https://t.co/yiNVylxNER

# orderedItems[].object.content
Our blog post on skops is out! https://www. kdnuggets.com/2023/02/skops-ne w-library-improve-scikitlearn-production.html 🙇🏻‍♀️ @adrin@kolektiva.social

# orderedItems[].object.contentMap.tr
Our blog post on skops is out! https://www. kdnuggets.com/2023/02/skops-ne w-library-improve-scikitlearn-production.html 🙇🏻‍♀️ @adrin@kolektiva.social

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Hello 👋🏼
I just uploaded all of my cheatsheets (there's 100+ of them!) on various topics including machine learning, data structures, statistics and more.
📔 GitHub repository: https://github.com/merveenoyan/my_note s
📓 Hugging Face dataset repository: https://huggingface.co/datasets/merve/my_notes

# orderedItems[].object.contentMap.tr
Hello 👋🏼
I just uploaded all of my cheatsheets (there's 100+ of them!) on various topics including machine learning, data structures, statistics and more.
📔 GitHub repository: https://github.com/merveenoyan/my_note s
📓 Hugging Face dataset repository: https://huggingface.co/datasets/merve/my_notes

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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)

# orderedItems[].object.contentMap.tr
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)

# orderedItems[].object.content
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! 🌟

# orderedItems[].object.contentMap.tr
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! 🌟

# orderedItems[].object.content
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
and it's a very light dependency ☁️✨

# orderedItems[].object.contentMap.tr
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
and it's a very light dependency ☁️✨

# orderedItems[].object.content
When versioning your experiments, it's best to keep couple of information for better reproducibility:

🧑🏻‍🔬 Your hyperparameters and attributes of preprocessors and architecture (pipeline)
📈 Metrics
👑 Feature importances (which I used ELI5 for)
✅ Requirements of your environment

# orderedItems[].object.contentMap.tr
When versioning your experiments, it's best to keep couple of information for better reproducibility:

🧑🏻‍🔬 Your hyperparameters and attributes of preprocessors and architecture (pipeline)
📈 Metrics
👑 Feature importances (which I used ELI5 for)
✅ Requirements of your environment

# orderedItems[].object.content
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 🧑🏻‍🔬💜

📓 Notebook: https://kaggle.com/code/unofficialmerv e/explainability-and-versioning
📔 Resulting repository: https://huggingface.co/scikit-learn/xg boost-example
Couple of cool things 🧵

# orderedItems[].object.contentMap.tr
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 🧑🏻‍🔬💜

📓 Notebook: https://kaggle.com/code/unofficialmerv e/explainability-and-versioning
📔 Resulting repository: https://huggingface.co/scikit-learn/xg boost-example
Couple of cool things 🧵

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productive weekend 🫠

# orderedItems[].object.contentMap.tr
productive weekend 🫠

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@RecursiveNeuron kaggle and stackoverflow surveys and reports show otherwise though

# orderedItems[].object.contentMap.tr
@RecursiveNeuron kaggle and stackoverflow surveys and reports show otherwise though

# orderedItems[].object.content
@kbaxter if I was left on a deserted island and had to pick a model it would be this zero shot BART model for sure 🫠😂

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@kbaxter if I was left on a deserted island and had to pick a model it would be this zero shot BART model for sure 🫠😂

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See below how to use zero shot classification models with few lines of code 👇🏼

# orderedItems[].object.contentMap.tr
See below how to use zero shot classification models with few lines of code 👇🏼

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Zero shot text classification is the most underrated and useful task for various NLP tasks 😍

These models can predict what a given text is about without explicitly being trained on the classes 🤯 You can use these models off the shelf for topic classification or sentiment analysis, or even use it for labelling! 🏷

Learn more here 👉🏼 https://huggingface.co/tasks/zero-shot-classification

# orderedItems[].object.contentMap.tr
Zero shot text classification is the most underrated and useful task for various NLP tasks 😍

These models can predict what a given text is about without explicitly being trained on the classes 🤯 You can use these models off the shelf for topic classification or sentiment analysis, or even use it for labelling! 🏷

Learn more here 👉🏼 https://huggingface.co/tasks/zero-shot-classification

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Since this is open-source & crowd-sourced, you can contribute your app to the documentation too!
Get started here: https://huggingface2.notion.site/Add-y our-Space-to-Tasks-d7ad0ed035674c3bbd5909188565f872

# orderedItems[].object.contentMap.tr
Since this is open-source & crowd-sourced, you can contribute your app to the documentation too!
Get started here: https://huggingface2.notion.site/Add-y our-Space-to-Tasks-d7ad0ed035674c3bbd5909188565f872

# orderedItems[].object.content
Our crowdsourced documentation project Tasks at Hugging Face now include example Spaces for a given task! 😍
Check them out at http://huggingface.co/tasks 💗

# orderedItems[].object.contentMap.tr
Our crowdsourced documentation project Tasks at Hugging Face now include example Spaces for a given task! 😍
Check them out at http://huggingface.co/tasks 💗

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@vicki @b0rk @SebRaschka @JayAlammar @eugeneyan @brohrer hehe thank you Vicki! adding more to the list 👇🏼
@akhaliq @minimaxir @SebRaschka @julien_c @osanseviero @mmitchell_ai @SashaMTL @anton_l @pcuenq @abidlabs

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@vicki @b0rk @SebRaschka @JayAlammar @eugeneyan @brohrer hehe thank you Vicki! adding more to the list 👇🏼
@akhaliq @minimaxir @SebRaschka @julien_c @osanseviero @mmitchell_ai @SashaMTL @anton_l @pcuenq @abidlabs

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@knutjaegersberg @minimaxir I think FLAN isn't benchmarked but it seems very promising.
I feel like for abstractive I didn't observe much difference between BART and T5 back when I was working with them 😅 I will check FLAN 🍮

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@knutjaegersberg @minimaxir I think FLAN isn't benchmarked but it seems very promising.
I feel like for abstractive I didn't observe much difference between BART and T5 back when I was working with them 😅 I will check FLAN 🍮

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@knutjaegersberg @minimaxir I just checked for both:
https://paperswithcode.com/task/extrac tive-document-summarization
https://paperswithcode.com/task/abstra ctive-text-summarization
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

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@knutjaegersberg @minimaxir I just checked for both:
https://paperswithcode.com/task/extrac tive-document-summarization
https://paperswithcode.com/task/abstra ctive-text-summarization
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

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@knutjaegersberg @minimaxir 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

# orderedItems[].object.contentMap.tr
@knutjaegersberg @minimaxir 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