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NLP for African languages, MT, NER, POS, QA, ...

Recent Activity

JessicaOjo  updated a dataset 29 days ago
masakhane/afrimgsm
ajesujoba  updated a dataset about 1 month ago
masakhane/AfriDocMT
israel  authored a paper about 1 month ago
An Amharic News Text classification Dataset
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prithivMLmods 
posted an update about 13 hours ago
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The demo for DREX-062225-exp (Document Retrieval and Extraction eXpert ~ experimental) / typhoon-ocr-3b (a bilingual document parsing model built specifically for real-world documents) / VIREX-062225-exp (Video Information Retrieval and Extraction eXpert ~ experimental) / olmOCR-7B-0225-preview (the document parsing model based on Qwen2VL). 🤗

✦ Demo : prithivMLmods/Doc-VLMs-OCR ~ ( with .md canvas )

⤡ DREX-062225-exp : prithivMLmods/DREX-062225-exp
⤡ typhoon-ocr-3b : scb10x/typhoon-ocr-3b
⤡ VIREX-062225-exp : prithivMLmods/VIREX-062225-exp
⤡ olmOCR-7B-0225-preview : allenai/olmOCR-7B-0225-preview

⤡ Collection : prithivMLmods/doc-vl-685839064a863e1cd23be3f1
⤡ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
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To know more about it, visit the model card of the respective model. !!
prithivMLmods 
posted an update 1 day ago
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Updated the docscopeOCR-7B-050425-exp with the DREX-062225-exp, with improved preciseness in table structure and line spacing in the markdown used on the document page. And though this is still an experimental one, it's expected to perform well in the defined DREX use cases [ Document Retrieval and Extraction eXpert – experimental ocr ]. 💻

⤡ Model : prithivMLmods/DREX-062225-exp
⤡ Demo : prithivMLmods/Doc-VLMs-OCR

⤡ Collection : prithivMLmods/doc-vl-685839064a863e1cd23be3f1
⤡ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
⤡ Git : https://github.com/PRITHIVSAKTHIUR/DREX.git
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To know more about it, visit the model card of the respective model. !!
prithivMLmods 
posted an update 5 days ago
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The demo for smoldocling / nanonets ocr / typhoon ocr / monkey ocr explores the document OCR capabilities of various newly released multimodal VLMs in a single space. And if you're experiencing or demoing long document image OCR, kindly use the Smoldocling 256M preview [ Smoldocling is back in demo here. ] 🤗.

✦ Try the demo here : prithivMLmods/Multimodal-OCR2

⤡ MonkeyOCR Recognition : echo840/MonkeyOCR
⤡ Nanonets-OCR-s : nanonets/Nanonets-OCR-s
⤡ SmolDocling-256M-preview : ds4sd/SmolDocling-256M-preview
⤡ typhoon-ocr-7b : scb10x/typhoon-ocr-7b

⤡ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0

⤡ Github : https://github.com/PRITHIVSAKTHIUR/Multimodal-OCR2


The community GPU grant was given by Hugging Face — special thanks to them. 🤗🚀



To know more about it, visit the model card of the respective model. !!
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prithivMLmods 
posted an update 8 days ago
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The demo for the MonkeyOCR Recognition model, which adopts a Structure-Recognition-Relation (SRR) triplet paradigm & Nanonets-OCR-s a powerful, state-of-the-art image-to-markdown OCR model that goes far beyond traditional text extraction and other experimental document OCR models, is combined into a single space.

✦ Try the demo here : prithivMLmods/core-OCR
✦ Try Nanonets-OCR-s demo here : prithivMLmods/Multimodal-OCR

⤡ MonkeyOCR Recognition : echo840/MonkeyOCR
⤡ docscopeOCR-7B-050425-exp : prithivMLmods/docscopeOCR-7B-050425-exp
⤡ coreOCR-7B-050325-preview : prithivMLmods/coreOCR-7B-050325-preview
⤡ Nanonets-OCR-s : nanonets/Nanonets-OCR-s

⤡ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0

Also, include a sample OCR test using the VisionOCR-3B-061125 model and the Qwen2-VL-OCR-2B-Instruct model.
⤡ Blog : https://huggingface.co/blog/prithivMLmods/visionocr-3b-061125-vs-qwen2-vl-ocr-2b-instruct

To know more about it, visit the model card of the respective model. !!
Tonic 
posted an update 21 days ago
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🙋🏻‍♂️ hey there folks ,

So every bio/med/chem meeting i go to i always the same questions "why are you sharing a gdrive link with me for this?" and "Do you have any plans to publish your model weights and datasets on huggingface?" and finally i got a good answer today which explains everything :

basically there is some kind of government censorship on this (usa, but i'm sure others too) and they are told they are not allowed as it is considered a "dataleak" which is illegal !!!!

this is terrible ! but the good news is that we can do something about it !

so there is this "call for opinions and comments" here from the NIH (usa) , and here we can make our opinion on this topic known : https://osp.od.nih.gov/comment-form-responsibly-developing-and-sharing-generative-artificial-intelligence-tools-using-nih-controlled-access-data/

kindly consider dropping your opinion and thoughts about this censorship of science , and share this post , link or thoughts widely .

Together maybe we can start to share data and model weights appropriately and openly in a good way 🙏🏻🚀

cc. @cyrilzakka

prithivMLmods 
posted an update 25 days ago
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OpenAI, Google, Hugging Face, and Anthropic have released guides and courses on building agents, prompting techniques, scaling AI use cases, and more. Below are 10+ minimalistic guides and courses that may help you in your progress. 📖

⤡ Agents Companion : https://www.kaggle.com/whitepaper-agent-companion
⤡ Building Effective Agents : https://www.anthropic.com/engineering/building-effective-agents
⤡ Guide to building agents by OpenAI : https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
⤡ Prompt engineering by Google : https://www.kaggle.com/whitepaper-prompt-engineering
⤡ Google: 601 real-world gen AI use cases : https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders
⤡ Prompt engineering by IBM : https://www.ibm.com/think/topics/prompt-engineering-guide
⤡ Prompt Engineering by Anthropic : https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
⤡ Scaling AI use cases : https://cdn.openai.com/business-guides-and-resources/identifying-and-scaling-ai-use-cases.pdf
⤡ Prompting Guide 101 : https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf
⤡ AI in the Enterprise by OpenAI : https://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf

by HF🤗 :
⤡ AI Agents Course by Huggingface : https://huggingface.co/learn/agents-course/unit0/introduction
⤡ Smol-agents Docs : https://huggingface.co/docs/smolagents/en/tutorials/building_good_agents
⤡ MCP Course by Huggingface : https://huggingface.co/learn/mcp-course/unit0/introduction
⤡ Other Course (LLM, Computer Vision, Deep RL, Audio, Diffusion, Cookbooks, etc..) : https://huggingface.co/learn
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prithivMLmods 
posted an update 27 days ago
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Just made a demo for Cosmos-Reason1, a physical AI model that understands physical common sense and generates appropriate embodied decisions in natural language through long chain-of-thought reasoning. Also added video understanding support to it. 🤗🚀

✦ Try the demo here : prithivMLmods/DocScope-R1

⤡ Cosmos-Reason1-7B : nvidia/Cosmos-Reason1-7B
⤡ docscopeOCR-7B-050425-exp : prithivMLmods/docscopeOCR-7B-050425-exp
⤡ Captioner-Relaxed : Ertugrul/Qwen2.5-VL-7B-Captioner-Relaxed

⤡ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0

⤡ GitHub :
• https://github.com/PRITHIVSAKTHIUR/Cosmos-x-DocScope
• https://github.com/PRITHIVSAKTHIUR/Nvidia-Cosmos-Reason1-Demo.

To know more about it, visit the model card of the respective model. !!
JessicaOjo 
updated a dataset 29 days ago
Tonic 
posted an update 30 days ago
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🙋🏻‍♂️ Hey there folks ,

Yesterday the world's first "Learn to Vibe Code" application was released .

As vibe coding is the mainstream paradigm , so now the first educational app is there to support it .

You can try it out already :

https://vibe.takara.ai

and of course it's entirely open source, so i already made my issue and feature branch :-) 🚀
prithivMLmods 
posted an update about 1 month ago
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Got access to Google's all-new Gemini Diffusion a state-of-the-art text diffusion model. It delivers the performance of Gemini 2.0 Flash-Lite at 5x the speed, generating over 1000 tokens in a fraction of a second and producing impressive results. Below are some initial outputs generated using the model. ♊🔥

Gemini Diffusion Playground ✦ : https://deepmind.google.com/frontiers/gemini-diffusion

Get Access Here : https://docs.google.com/forms/d/1aLm6J13tAkq4v4qwGR3z35W2qWy7mHiiA0wGEpecooo/viewform?edit_requested=true

🔗 To know more, visit: https://deepmind.google/models/gemini-diffusion/
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prithivMLmods 
posted an update about 1 month ago
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The more optimized explicit content filters with lightweight 𝙜𝙪𝙖𝙧𝙙 models trained based on siglip2 patch16 512 and vit patch16 224 for illustration and explicit content classification for content moderation in social media, forums, and parental controls for safer browsing environments. this version fixes the issues in the previous release, which lacked sufficient resources. 🚀

⤡ Models :
→ siglip2 mini explicit content : prithivMLmods/siglip2-mini-explicit-content [recommended]
→ vit mini explicit content : prithivMLmods/vit-mini-explicit-content

⤡ Building image safety-guard models : strangerguardhf

⤡ Datasets :
→ nsfw multidomain classification : strangerguardhf/NSFW-MultiDomain-Classification
→ nsfw multidomain classification v2.0 : strangerguardhf/NSFW-MultiDomain-Classification-v2.0

⤡ Collection :
→ Updated Versions [05192025] : prithivMLmods/explicit-content-filters-682aaa4733e378561925ca2b
→ Previous Versions : prithivMLmods/siglip2-content-filters-042025-final-680fe4aa1a9d589bf2c915ff

Find a collections inside the collection.👆

To know more about it, visit the model card of the respective model.
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ajesujoba 
updated a dataset about 1 month ago
prithivMLmods 
posted an update about 1 month ago
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Models for detecting images generated by diffusion models (Flux.1, SDXL, ..) are trained or fine-tuned using image classification models for content moderation. These models use datasets available on the Hub. For identifying AI-generated images or moderating visual content, the recommended model is OpenSDI-Flux.1-SigLIP2.😺🧨

Models : prithivMLmods/OpenSDI-Flux.1-SigLIP2 [Best approach for AI [Diffusion Generated] vs. real image classification] prithivMLmods/OpenSDI-SD2.1-SigLIP2 prithivMLmods/OpenSDI-SD3-SigLIP2 prithivMLmods/OpenSDI-SD1.5-SigLIP2 prithivMLmods/OpenSDI-SDXL-SigLIP2

Datasets : nebula/OpenSDI_test madebyollin/megalith-10m

Collection : prithivMLmods/opensdi-diffusion-generated-image-classification-682488a3a3e5be7083db3383

Find a collections inside the collection.👆

To know more about it, visit the model card of the respective model.
prithivMLmods 
posted an update about 1 month ago
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Dropping some image classification models for content moderation and classifiers trained with datasets available on the Hub. All are fine-tuned on the siglip2 backbone, (competitions AIOrNot, Imagenette, and Driver-Drowsiness). Models and datasets are listed below:

🤗Models :
AI or Not : prithivMLmods/AIorNot-SigLIP2
Driver Drowsiness Detection : prithivMLmods/DOZE-GUARD-RLDD
Subset 10 ImageNet : prithivMLmods/IMAGENETTE

🥊Datasets :
+ competitions/aiornot
+ akahana/Driver-Drowsiness-Dataset
+ frgfm/imagenette

🔗Collection :
[The previous collection of models is also listed in the same collection, so you can find more models focused on image classification tasks.]

- prithivMLmods/multiclass-image-classification-05142025-68234c8010a9350a4d6739b5

Find a collections inside the collection.🤪👆

To know more about it, visit the model card of the respective model.
israel 
authored 6 papers about 1 month ago