AfriGuard
Collection
Can LLMs recognize safety risks, characterize the type of risk, and respond appropriately in African Languages? • 6 items • Updated
AI & ML interests
NLP for African languages, MT, NER, POS, QA, ...
Recent Activity
ajesujoba
updated a
collection 1 day ago
israel
updated a
collection 5 days ago
difficulty in Downloading the dataset
#1 opened 3 months ago
by 
satya8484
Convert dataset to Parquet
1
#3 opened 18 days ago
by 
anrilombard
omarkamali
posted an update about 1 month ago
Post
204
Apparently grammar can travel without a passport 🌍
We trained a new neural grammar model *Tarakib* across 166 languages on top of Sawtone [1] - itself trained on nearly 400 langs. We then tested it on 28 languages that contributed no training updates to the grammar task.
The surprising result: it still learned useful parts-of-speech structure based on the foundational linguistic representation offered by Sawtone.
Performance varied considerably, and the transfer was not limited to close, same-script relatives but crossed writing systems. We found connections across language families, and about a quarter of the stable transfer links crossed writing systems.
This does not mean universal parsing is solved. But it suggests that Sawtone's representations contain universal grammatical clues that a shared model can reuse in languages it was never directly taught to parse.
A small step toward "spaCy for every language" ✌️
1. https://www.omneitylabs.com/models/sawtone
We trained a new neural grammar model *Tarakib* across 166 languages on top of Sawtone [1] - itself trained on nearly 400 langs. We then tested it on 28 languages that contributed no training updates to the grammar task.
The surprising result: it still learned useful parts-of-speech structure based on the foundational linguistic representation offered by Sawtone.
Performance varied considerably, and the transfer was not limited to close, same-script relatives but crossed writing systems. We found connections across language families, and about a quarter of the stable transfer links crossed writing systems.
This does not mean universal parsing is solved. But it suggests that Sawtone's representations contain universal grammatical clues that a shared model can reuse in languages it was never directly taught to parse.
A small step toward "spaCy for every language" ✌️
1. https://www.omneitylabs.com/models/sawtone
aj11anuj
authored 3
papers 3 months ago
Lingo_Research_Group at SemEval-2026 Task 9: Evaluating Prompt Variants for Polarization Detection
Paper • 2606.03334 • Published
From Script to Semantics: Prompting Strategies for African NLI
Paper • 2606.03304 • Published
Sample-Size Scaling of the African Languages NLI Evaluation
Paper • 2606.03219 • Published
dutkulang
updated a
Space 4 months ago
abumafrim
updated a
dataset 4 months ago
abumafrim
published a
dataset 4 months ago
israel
updated a
collection 5 months ago
Post
3,508
🙋🏻♂️ Hey there folks ,
Turns out : if we predict 🌏 earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.
Sentinel-2 imagery 🛰️basically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.
meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize 📡earth-bound response .
I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.
At least that's the concept !
check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth
- Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding
- Code: https://github.com/Josephrp/Nutonic
- Dataset: NuTonic/sat-vl-sft-training-ready-v1
- Model: NuTonic/lspace
- Training: NuTonic/lspace-trackio
- Evals: NuTonic/Patagonia_Eval
Turns out : if we predict 🌏 earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.
Sentinel-2 imagery 🛰️basically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.
meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize 📡earth-bound response .
I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.
At least that's the concept !
check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth
- Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding
- Code: https://github.com/Josephrp/Nutonic
- Dataset: NuTonic/sat-vl-sft-training-ready-v1
- Model: NuTonic/lspace
- Training: NuTonic/lspace-trackio
- Evals: NuTonic/Patagonia_Eval
- 3 replies
Post
4,516
🙋🏻♂️ Hey there folks,
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
Check this one out :
NuTonic/sat-bbox-metadata-sft-v1
the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .
hope you like it ! 🚀
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
Check this one out :
NuTonic/sat-bbox-metadata-sft-v1
the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .
hope you like it ! 🚀
- 2 replies
Post
3,762
🙋🏻♂️ Hey there folks ,
I'm sharing huggingface's largest dataset of annotated statelite images today.
check it out here : NuTonic/sat-image-boundingbox-sft-full
I hope you like it , the idea is to be able to use this with small vision models 🚀
I'm sharing huggingface's largest dataset of annotated statelite images today.
check it out here : NuTonic/sat-image-boundingbox-sft-full
I hope you like it , the idea is to be able to use this with small vision models 🚀