ai4bharat/indic-parler-tts
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Pankaj Munde
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nisten's
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Just had the former chief public health officer of the Netherlands🇳🇱 review a huggingface AI-doctor I made via a simple orpo-zephyr-8x22b-GPT and they think it's really good.
https://hf.co/chat/assistant/661d77310e3aea9ae571e43c
https://hf.co/chat/assistant/661d77310e3aea9ae571e43c
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Molbap's
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🚀🚀 Exciting times for the document AI community!
We're thrilled to announce the release of some of the largest OCR datasets available to the public.
🔥 With over 26 million pages , 18 billion text tokens, and 6TB of data, these resources are a significant leap forward for document AI research.
Here's how to access these datasets quickly:
This enables you to stream them directly, integrating seamlessly with your projects using the Hugging Face datasets library. On the hub, you can find them here:
pixparse/pdfa-eng-wds
pixparse/idl-wds
For lean data loading, the new [chug](https://github.com/huggingface/chug) library offers a solution with pdf decoding:
We owe a huge thank you to Peter Wyatt, Kate Tasker, Rachel Taketa, Ali Furkan Biten, Ruben Tito, and their colleagues for their contributions. Their work putting these datasets together has been invaluable. 🤗
Looking Ahead:
We're on a mission to enhance document AI capabilities, and these datasets are just the beginning. With your engagement and innovation, we're confident in the community's ability to develop robust OCR solutions. We encourage you to explore these datasets, experiment with the code, and contribute to the collective progress in document AI.
For detailed information on usage and licensing, please refer to the dataset cards on the Hugging Face hub.
We're thrilled to announce the release of some of the largest OCR datasets available to the public.
🔥 With over 26 million pages , 18 billion text tokens, and 6TB of data, these resources are a significant leap forward for document AI research.
Here's how to access these datasets quickly:
from datasets import load_dataset
pdfa_dataset = load_dataset('pixparse/pdfa-eng-wds', streaming=True)
IDL_dataset = load_dataset('pixparse/idl-wds', streaming=True)This enables you to stream them directly, integrating seamlessly with your projects using the Hugging Face datasets library. On the hub, you can find them here:
pixparse/pdfa-eng-wds
pixparse/idl-wds
For lean data loading, the new [chug](https://github.com/huggingface/chug) library offers a solution with pdf decoding:
import chug
task_cfg = chug.DataTaskDocReadCfg(
page_sampling='all',
)
data_cfg = chug.DataCfg(
source='pixparse/pdfa-eng-wds',
split='train',
batch_size=None,
format='hfids',
num_workers=0,
)
data_loader = chug.create_loader(
data_cfg,
task_cfg,
)
sample = next(iter(data_loader))We owe a huge thank you to Peter Wyatt, Kate Tasker, Rachel Taketa, Ali Furkan Biten, Ruben Tito, and their colleagues for their contributions. Their work putting these datasets together has been invaluable. 🤗
Looking Ahead:
We're on a mission to enhance document AI capabilities, and these datasets are just the beginning. With your engagement and innovation, we're confident in the community's ability to develop robust OCR solutions. We encourage you to explore these datasets, experiment with the code, and contribute to the collective progress in document AI.
For detailed information on usage and licensing, please refer to the dataset cards on the Hugging Face hub.
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andrewyng's
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11 months ago
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DeepLearning.AI just announced a new short course: Open Source Models with Hugging Face 🤗, taught by Hugging Face's own Maria Khalusova, Marc Sun and Younes Belkada!
As many of you already know, Hugging Face has been a game changer by letting developers quickly grab any of hundreds of thousands of already-trained open source models to assemble into new applications. This course teaches you best practices for building this way, including how to search and choose among models.
You'll learn to use the Transformers library and walk through multiple models for text, audio, and image processing, including zero-shot image segmentation, zero-shot audio classification, and speech recognition. You'll also learn to use multimodal models for visual question answering, image search, and image captioning. Finally, you’ll learn how to demo what you build locally, on the cloud, or via an API using Gradio and Hugging Face Spaces.
Thank you very much to Hugging Face's wonderful team for working with us on this.
You can sign up for the course here: https://www.deeplearning.ai/short-courses/open-source-models-hugging-face/
As many of you already know, Hugging Face has been a game changer by letting developers quickly grab any of hundreds of thousands of already-trained open source models to assemble into new applications. This course teaches you best practices for building this way, including how to search and choose among models.
You'll learn to use the Transformers library and walk through multiple models for text, audio, and image processing, including zero-shot image segmentation, zero-shot audio classification, and speech recognition. You'll also learn to use multimodal models for visual question answering, image search, and image captioning. Finally, you’ll learn how to demo what you build locally, on the cloud, or via an API using Gradio and Hugging Face Spaces.
Thank you very much to Hugging Face's wonderful team for working with us on this.
You can sign up for the course here: https://www.deeplearning.ai/short-courses/open-source-models-hugging-face/
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