Voxel51/getting-started-labeled-validation
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Voxel51
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Visual AI, Computer vision, Multimodal AI, Data Centric AI
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TheSteve0ย
updated
2
datasets
4 days ago
Post
1713
๐ SUPER BLACK FRIDAY DEAL ๐
Train almost any model on a variety of tasks such as llm finetuning, text classification/regression, summarization, question answering, image classification/regression, object detection, tabular data, etc for FREE using AutoTrain locally. ๐ฅ
https://github.com/huggingface/autotrain-advanced
Train almost any model on a variety of tasks such as llm finetuning, text classification/regression, summarization, question answering, image classification/regression, object detection, tabular data, etc for FREE using AutoTrain locally. ๐ฅ
https://github.com/huggingface/autotrain-advanced
Post
5535
INTRODUCING Hugging Face AutoTrain Client ๐ฅ
Fine-tuning models got even easier!!!!
Now you can fine-tune SOTA models on all compatible dataset-model pairs on Hugging Face Hub using Python on Hugging Face Servers. Choose from a number of GPU flavors, millions of models and dataset pairs and 10+ tasks ๐ค
To try, install autotrain-advanced using pip. You can ignore dependencies and install without --no-deps and then you'd need to install some dependencies by hand.
"pip install autotrain-advanced"
Github repo: https://github.com/huggingface/autotrain-advanced
Fine-tuning models got even easier!!!!
Now you can fine-tune SOTA models on all compatible dataset-model pairs on Hugging Face Hub using Python on Hugging Face Servers. Choose from a number of GPU flavors, millions of models and dataset pairs and 10+ tasks ๐ค
To try, install autotrain-advanced using pip. You can ignore dependencies and install without --no-deps and then you'd need to install some dependencies by hand.
"pip install autotrain-advanced"
Github repo: https://github.com/huggingface/autotrain-advanced
- 6 replies
abhishekย
authored
a
paper
3 months ago
Post
1856
๐จ NEW TASK ALERT ๐จ
Extractive Question Answering: because sometimes generative is not all you need ๐
AutoTrain is the only open-source, no code solution to offer so many tasks across different modalities. Current task count: 23 ๐
Check out the blog post on getting started with this task: https://huggingface.co/blog/abhishek/extractive-qa-autotrain
Extractive Question Answering: because sometimes generative is not all you need ๐
AutoTrain is the only open-source, no code solution to offer so many tasks across different modalities. Current task count: 23 ๐
Check out the blog post on getting started with this task: https://huggingface.co/blog/abhishek/extractive-qa-autotrain
harpreetsahotaย
posted
an
update
7 months ago
Post
2147
The Coachella of Computer Vision, CVPR, is right around the corner. In anticipation of the conference, I curated a dataset of the papers.
I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP.
The dataset consists of the following fields:
- An image of the first page of the paper
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Here's how I created the dataset ๐๐ผ
Generic code for building this dataset can be found [here](https://github.com/harpreetsahota204/CVPR-2024-Papers).
This dataset was built using the following steps:
- Scrape the CVPR 2024 website for accepted papers
- Use DuckDuckGo to search for a link to the paper's abstract on arXiv
- Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper
- Use pdf2image to save the image of paper's first page
- Use GPT-4o to extract keywords from the abstract
Voxel51/CVPR_2024_Papers
I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP.
The dataset consists of the following fields:
- An image of the first page of the paper
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title: The title of the paper-
authors_list: The list of authors-
abstract: The abstract of the paper-
arxiv_link: Link to the paper on arXiv-
other_link: Link to the project page, if found-
category_name: The primary category this paper according to [arXiv taxonomy](https://arxiv.org/category_taxonomy)-
all_categories: All categories this paper falls into, according to arXiv taxonomy-
keywords: Extracted using GPT-4oHere's how I created the dataset ๐๐ผ
Generic code for building this dataset can be found [here](https://github.com/harpreetsahota204/CVPR-2024-Papers).
This dataset was built using the following steps:
- Scrape the CVPR 2024 website for accepted papers
- Use DuckDuckGo to search for a link to the paper's abstract on arXiv
- Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper
- Use pdf2image to save the image of paper's first page
- Use GPT-4o to extract keywords from the abstract
Voxel51/CVPR_2024_Papers
Post
3318
You can now train/finetune custom sentence transformer embedding models using AutoTrain. Read blog: https://huggingface.co/blog/abhishek/finetune-custom-embeddings-autotrain
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- 2 replies
Post
2936
๐จ NEW TASK ALERT ๐จ
๐ AutoTrain now supports Object Detection! ๐
Transform your projects with these powerful new features:
๐น Fine-tune any supported model from the Hugging Face Hub
๐น Seamless logging with TensorBoard or W&B
๐น Support for local and hub datasets
๐น Configurable training for tailored results
๐น Train locally or leverage Hugging Face Spaces
๐น Deployment-ready with API inference or Hugging Face endpoints
AutoTrain: https://hf.co/autotrain
๐ AutoTrain now supports Object Detection! ๐
Transform your projects with these powerful new features:
๐น Fine-tune any supported model from the Hugging Face Hub
๐น Seamless logging with TensorBoard or W&B
๐น Support for local and hub datasets
๐น Configurable training for tailored results
๐น Train locally or leverage Hugging Face Spaces
๐น Deployment-ready with API inference or Hugging Face endpoints
AutoTrain: https://hf.co/autotrain
Post
3063
๐๐๐๐ Introducing AutoTrain Configs! ๐๐๐๐
Now you can train models using yaml config files! ๐ฅ These configs are easy to understand and are not at all overwhelming. So, even a person with almost zero knowledge of machine learning can train state of the art models without writing any code. Check out example configs in the config directory of autotrain-advanced github repo and feel free to share configs by creating a pull request ๐ค
Github repo: https://github.com/huggingface/autotrain-advanced
Now you can train models using yaml config files! ๐ฅ These configs are easy to understand and are not at all overwhelming. So, even a person with almost zero knowledge of machine learning can train state of the art models without writing any code. Check out example configs in the config directory of autotrain-advanced github repo and feel free to share configs by creating a pull request ๐ค
Github repo: https://github.com/huggingface/autotrain-advanced
- 2 replies
Post
3072
Post
2371
Trained another version of llama3-8b-instruct which beats the base model. This time without losing too many points on gsm8k benchmark. Again, using AutoTrain ๐ฅ pip install autotrain-advanced
Trained model: abhishek/autotrain-llama3-orpo-v2
Trained model: abhishek/autotrain-llama3-orpo-v2
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- 1 reply
Post
3476
With AutoTrain, you can already finetune the latest llama3 models without writing a single line of code. Here's an example finetune of llama3 8b model:
abhishek/autotrain-llama3-no-robots
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- 2 replies
Post
2172
FiftyOne Datasets <> Hugging Face Hub Integration!
As of yesterday's release of FiftyOne
You can now load Parquet datasets from the hub and have them converted directly into FiftyOne datasets. To load MNIST, for example:
You can also load FiftyOne datasets directly from the hub. Here's how you load the first 1000 samples from the VisDrone dataset:
And tying it all together, you can push your FiftyOne datasets directly to the hub:
Major thanks to @tomaarsen @davanstrien @severo @osanseviero and @julien-c for helping to make this happen!!!
Full documentation and details here: https://docs.voxel51.com/integrations/huggingface.html#huggingface-hub
As of yesterday's release of FiftyOne
0.23.8, the FiftyOne open source library for dataset curation and visualization is now integrated with the Hugging Face Hub!You can now load Parquet datasets from the hub and have them converted directly into FiftyOne datasets. To load MNIST, for example:
pip install -U fiftyoneimport fiftyone as fo
import fiftyone.utils.huggingface as fouh
dataset = fouh.load_from_hub(
"mnist",
format="ParquetFilesDataset",
classification_fields="label",
)
session = fo.launch_app(dataset)You can also load FiftyOne datasets directly from the hub. Here's how you load the first 1000 samples from the VisDrone dataset:
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
dataset = fouh.load_from_hub("jamarks/VisDrone2019-DET", max_samples=1000)
# Launch the App
session = fo.launch_app(dataset)And tying it all together, you can push your FiftyOne datasets directly to the hub:
import fiftyone.zoo as foz
import fiftyone.utils.huggingface as fouh
dataset = foz.load_zoo_dataset("quickstart")
fouh.push_to_hub(dataset, "my-dataset")Major thanks to @tomaarsen @davanstrien @severo @osanseviero and @julien-c for helping to make this happen!!!
Full documentation and details here: https://docs.voxel51.com/integrations/huggingface.html#huggingface-hub
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- 3 replies
harpreetsahotaย
posted
an
update
10 months ago
Post
google/gemma-7b-it is super good!
I wasn't convinced at first, but after vibe-checking it...I'm quite impressed.
I've got a notebook here, which is kind of a framework for vibe-checking LLMs.
In this notebook, I take Gemma for a spin on a variety of prompts:
โข [nonsensical tokens]( harpreetsahota/diverse-token-sampler
โข [conversation where I try to get some PII)( harpreetsahota/red-team-prompts-questions)
โข [summarization ability]( lighteval/summarization)
โข [instruction following]( harpreetsahota/Instruction-Following-Evaluation-for-Large-Language-Models
โข [chain of thought reasoning]( ssbuild/alaca_chain-of-thought)
I then used LangChain evaluators (GPT-4 as judge), and track everything in LangSmith. I made public links to the traces where you can inspect the runs.
I hope you find this helpful, and I am certainly open to feedback, criticisms, or ways to improve.
Cheers:
You can find the notebook here: https://colab.research.google.com/drive/1RHzg0FD46kKbiGfTdZw9Fo-DqWzajuoi?usp=sharing
I wasn't convinced at first, but after vibe-checking it...I'm quite impressed.
I've got a notebook here, which is kind of a framework for vibe-checking LLMs.
In this notebook, I take Gemma for a spin on a variety of prompts:
โข [nonsensical tokens]( harpreetsahota/diverse-token-sampler
โข [conversation where I try to get some PII)( harpreetsahota/red-team-prompts-questions)
โข [summarization ability]( lighteval/summarization)
โข [instruction following]( harpreetsahota/Instruction-Following-Evaluation-for-Large-Language-Models
โข [chain of thought reasoning]( ssbuild/alaca_chain-of-thought)
I then used LangChain evaluators (GPT-4 as judge), and track everything in LangSmith. I made public links to the traces where you can inspect the runs.
I hope you find this helpful, and I am certainly open to feedback, criticisms, or ways to improve.
Cheers:
You can find the notebook here: https://colab.research.google.com/drive/1RHzg0FD46kKbiGfTdZw9Fo-DqWzajuoi?usp=sharing
harpreetsahotaย
posted
an
update
12 months ago
Post
โ๐ผTwo new models dropped today ๐๐ฝ
1) ๐ฉ๐พโ๐ป ๐๐๐๐ข๐๐จ๐๐๐ซ-๐๐
๐๐ฝ Supports ๐ ๐ฅ๐๐ง๐ ๐ฎ๐๐ ๐๐ฌ: C, C# C++, GO, Rust, Python, Java, and Javascript.
๐๐ฝ Released under the ๐๐ฉ๐๐๐ก๐ ๐.๐ ๐ฅ๐ข๐๐๐ง๐ฌ๐
๐ฅ ๐๐ฎ๐ง๐๐ก๐๐ฌ ๐๐๐จ๐ฏ๐ ๐ข๐ญ๐ฌ ๐ฐ๐๐ข๐ ๐ก๐ญ ๐๐ฅ๐๐ฌ๐ฌ ๐จ๐ง ๐๐ฎ๐ฆ๐๐ง๐๐ฏ๐๐ฅ: Beats out CodeGen 2.5 7B and StarCoder 7B on most supported languages. Has a 3-point lead over StarCoderBase 15.5B for Python
๐ป ๐ป๐๐ ๐๐ ๐๐๐:
๐ ๐๐จ๐๐๐ฅ ๐๐๐ซ๐: Deci/DeciCoder-6B
๐ ๐๐จ๐ญ๐๐๐จ๐จ๐ค: https://colab.research.google.com/drive/1QRbuser0rfUiFmQbesQJLXVtBYZOlKpB
๐ชง ๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐ ๐๐๐ ๐๐ฉ๐๐๐: Deci/DeciCoder-6B-Demo
2) ๐จ ๐๐๐๐ข๐๐ข๐๐๐ฎ๐ฌ๐ข๐จ๐ง ๐ฏ๐.๐
๐๐ฝ Produces quality images on par with Stable Diffusion v1.5, but ๐.๐ ๐ญ๐ข๐ฆ๐๐ฌ ๐๐๐ฌ๐ญ๐๐ซ ๐ข๐ง ๐๐% ๐๐๐ฐ๐๐ซ ๐ข๐ญ๐๐ซ๐๐ญ๐ข๐จ๐ง๐ฌ
๐๐ฝ Employs a ๐ฌ๐ฆ๐๐ฅ๐ฅ๐๐ซ ๐๐ง๐ ๐๐๐ฌ๐ญ๐๐ซ ๐-๐๐๐ญ ๐๐จ๐ฆ๐ฉ๐จ๐ง๐๐ง๐ญ ๐ฐ๐ก๐ข๐๐ก ๐ก๐๐ฌ ๐๐๐ ๐ฆ๐ข๐ฅ๐ฅ๐ข๐จ๐ง ๐ฉ๐๐ซ๐๐ฆ๐๐ญ๐๐ซ๐ฌ.
๐๐ฝ Uses an optimized scheduler, ๐๐ช๐ฎ๐๐๐ณ๐๐๐๐๐++, which ๐๐ฎ๐ญ๐ฌ ๐๐จ๐ฐ๐ง ๐ญ๐ก๐ ๐ง๐ฎ๐ฆ๐๐๐ซ ๐จ๐ ๐ฌ๐ญ๐๐ฉ๐ฌ ๐ง๐๐๐๐๐ ๐ญ๐จ ๐ ๐๐ง๐๐ซ๐๐ญ๐ ๐ ๐ช๐ฎ๐๐ฅ๐ข๐ญ๐ฒ ๐ข๐ฆ๐๐ ๐ ๐๐ซ๐จ๐ฆ ๐๐ ๐ญ๐จ ๐๐.
๐๐ฝ Released under the ๐๐ซ๐๐๐ญ๐ข๐ฏ๐๐๐ ๐๐ฉ๐๐ง ๐๐๐๐++-๐ ๐๐ข๐๐๐ง๐ฌ๐.
๐ป ๐ป๐๐ ๐๐ ๐๐๐:
๐ ๐๐จ๐๐๐ฅ ๐๐๐ซ๐: Deci/DeciDiffusion-v2-0
๐ ๐๐จ๐ญ๐๐๐จ๐จ๐ค: https://colab.research.google.com/drive/11Ui_KRtK2DkLHLrW0aa11MiDciW4dTuB
๐ชง ๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐ ๐๐๐ ๐๐ฉ๐๐๐: Deci/DeciDiffusion-v2-0
Help support the projects by liking the model cards and the spaces!
Cheers and happy hacking!
1) ๐ฉ๐พโ๐ป ๐๐๐๐ข๐๐จ๐๐๐ซ-๐๐
๐๐ฝ Supports ๐ ๐ฅ๐๐ง๐ ๐ฎ๐๐ ๐๐ฌ: C, C# C++, GO, Rust, Python, Java, and Javascript.
๐๐ฝ Released under the ๐๐ฉ๐๐๐ก๐ ๐.๐ ๐ฅ๐ข๐๐๐ง๐ฌ๐
๐ฅ ๐๐ฎ๐ง๐๐ก๐๐ฌ ๐๐๐จ๐ฏ๐ ๐ข๐ญ๐ฌ ๐ฐ๐๐ข๐ ๐ก๐ญ ๐๐ฅ๐๐ฌ๐ฌ ๐จ๐ง ๐๐ฎ๐ฆ๐๐ง๐๐ฏ๐๐ฅ: Beats out CodeGen 2.5 7B and StarCoder 7B on most supported languages. Has a 3-point lead over StarCoderBase 15.5B for Python
๐ป ๐ป๐๐ ๐๐ ๐๐๐:
๐ ๐๐จ๐๐๐ฅ ๐๐๐ซ๐: Deci/DeciCoder-6B
๐ ๐๐จ๐ญ๐๐๐จ๐จ๐ค: https://colab.research.google.com/drive/1QRbuser0rfUiFmQbesQJLXVtBYZOlKpB
๐ชง ๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐ ๐๐๐ ๐๐ฉ๐๐๐: Deci/DeciCoder-6B-Demo
2) ๐จ ๐๐๐๐ข๐๐ข๐๐๐ฎ๐ฌ๐ข๐จ๐ง ๐ฏ๐.๐
๐๐ฝ Produces quality images on par with Stable Diffusion v1.5, but ๐.๐ ๐ญ๐ข๐ฆ๐๐ฌ ๐๐๐ฌ๐ญ๐๐ซ ๐ข๐ง ๐๐% ๐๐๐ฐ๐๐ซ ๐ข๐ญ๐๐ซ๐๐ญ๐ข๐จ๐ง๐ฌ
๐๐ฝ Employs a ๐ฌ๐ฆ๐๐ฅ๐ฅ๐๐ซ ๐๐ง๐ ๐๐๐ฌ๐ญ๐๐ซ ๐-๐๐๐ญ ๐๐จ๐ฆ๐ฉ๐จ๐ง๐๐ง๐ญ ๐ฐ๐ก๐ข๐๐ก ๐ก๐๐ฌ ๐๐๐ ๐ฆ๐ข๐ฅ๐ฅ๐ข๐จ๐ง ๐ฉ๐๐ซ๐๐ฆ๐๐ญ๐๐ซ๐ฌ.
๐๐ฝ Uses an optimized scheduler, ๐๐ช๐ฎ๐๐๐ณ๐๐๐๐๐++, which ๐๐ฎ๐ญ๐ฌ ๐๐จ๐ฐ๐ง ๐ญ๐ก๐ ๐ง๐ฎ๐ฆ๐๐๐ซ ๐จ๐ ๐ฌ๐ญ๐๐ฉ๐ฌ ๐ง๐๐๐๐๐ ๐ญ๐จ ๐ ๐๐ง๐๐ซ๐๐ญ๐ ๐ ๐ช๐ฎ๐๐ฅ๐ข๐ญ๐ฒ ๐ข๐ฆ๐๐ ๐ ๐๐ซ๐จ๐ฆ ๐๐ ๐ญ๐จ ๐๐.
๐๐ฝ Released under the ๐๐ซ๐๐๐ญ๐ข๐ฏ๐๐๐ ๐๐ฉ๐๐ง ๐๐๐๐++-๐ ๐๐ข๐๐๐ง๐ฌ๐.
๐ป ๐ป๐๐ ๐๐ ๐๐๐:
๐ ๐๐จ๐๐๐ฅ ๐๐๐ซ๐: Deci/DeciDiffusion-v2-0
๐ ๐๐จ๐ญ๐๐๐จ๐จ๐ค: https://colab.research.google.com/drive/11Ui_KRtK2DkLHLrW0aa11MiDciW4dTuB
๐ชง ๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐ ๐๐๐ ๐๐ฉ๐๐๐: Deci/DeciDiffusion-v2-0
Help support the projects by liking the model cards and the spaces!
Cheers and happy hacking!
Post
Happy to announce, brand new, open-source Hugging Face Competitions platform ๐ Now, create a machine learning competition for your friends, colleagues or the world for FREE* and host it on Hugging Face: the AI community building the future. Creating a competition requires only two steps:
pip install competitions, then run competitions create and create competition by answering a few questions ๐ฅ Checkout the github repo: https://github.com/huggingface/competitions and docs: https://hf.co/docs/competitions
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- 6 replies
Post
Train ANYTHING on Hugging Face Spaces hardware using AutoTrain SpaceRunner: https://hf.co/blog/stefan-it/autotrain-flair-mobie ๐ฅ
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- 9 replies