[PoC] Add resources to each model's doc page#19767
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The documentation is not available anymore as the PR was closed or merged. |
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One question I have: does it matter if the resources diverge from the API described in the docs? |
sgugger
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Looks like a great idea! I don't think this can be easily automated however as there a re a lot of specific examples.
| - [`ViTForImageClassification`] is supported by the official [image classification scripts](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [notebooks](https://github.com/huggingface/notebooks/tree/main/examples). | ||
| - Demo notebooks regarding inference as well as fine-tuning ViT on custom data can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/VisionTransformer). | ||
| - [`ViTForMaskedImageModeling`] is supported by the official [image pretraining script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining). | ||
| - [Blog](https://huggingface.co/blog/fine-tune-vit): Fine-Tune ViT for Image Classification with 🤗 Transformers. |
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Can we group those by model class (and thus task)? Here the first and last are both linked to ViTForImageClassification for instance.
| - [Blog](https://www.philschmid.de/image-classification-huggingface-transformers-keras): Image Classification with Hugging Face Transformers and `Keras`. | ||
| - [Blog](Deploying TensorFlow Vision Models in Hugging Face with TF Serving) Deploying TensorFlow Vision Models in Hugging Face with TF Serving. | ||
| - [Blog](https://huggingface.co/blog/deploy-tfserving-kubernetes): Deploying 🤗 ViT on Kubernetes with TF Serving. | ||
| - [Blog](https://huggingface.co/blog/deploy-vertex-ai): Deploying 🤗 ViT on Vertex AI. |
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Same here, group by class and have a section for deployment.
LysandreJik
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I really like that too. I think it'll be much simpler to understand model architecture this way.
stevhliu
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Great idea, I think having the resources directly available here will have the most impact and visibility!
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| - [`TFViTForImageClassification`] is supported by the [example scripts](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification) and [example notebooks](https://github.com/huggingface/notebooks/tree/main/examples). | ||
| - [Blog](https://www.philschmid.de/image-classification-huggingface-transformers-keras): Image Classification with Hugging Face Transformers and `Keras`. | ||
| - [Blog](Deploying TensorFlow Vision Models in Hugging Face with TF Serving) Deploying TensorFlow Vision Models in Hugging Face with TF Serving. |
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| - [Blog](Deploying TensorFlow Vision Models in Hugging Face with TF Serving) Deploying TensorFlow Vision Models in Hugging Face with TF Serving. | |
| - [Blog](https://huggingface.co/blog/tf-serving-vision) Deploying TensorFlow Vision Models in Hugging Face with TF Serving. |
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@stevhliu, I think this is a pretty significant improvement for the model pages. Would you be down to try doing something similar for other high-profile architectures? If so, I would encourage the following:
Would you be down to kickstart such a project? In doing so, I'm sure we'll have more visibility over what we can automate, and what we cannot automate. I'm pretty sure we can automate a portion of it but I don't think we can have high-quality resources here only using automation. |
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Closing this PR as it was just a PoC. Will continue the work in other PRs |
What does this PR do?
This PR is a small PoC to add resources (notebooks, scripts, blogs, etc.) to each model's doc page.
Ideally (not sure if it's possible), but it'd be great if we could (partially) automate this.
The idea being that, if a new model is added to the FOR_IMAGE_CLASSIFICATION_MAPPING for instance, the doc page automatically will add a link to the image classification notebooks and scripts. Not sure if this is possible, cc @sgugger.