Post
1405
🇹🇷 **Can a 110M model understand Turkish names, places and organizations this well?**
We tested Werea-TR-NER on the human-labeled WikiANN Turkish test set:
**91.7% Entity F1**
👤 Person → **94.2%**
📍 Location → **91.4%**
🏢 Organization → **89.2%**
Only ~110M parameters.
Try it with a difficult Turkish sentence 👇
→ Ahmet Yılmaz — PERSON
→ İstanbul — LOCATION
→ Werea — ORGANIZATION
But easy examples are boring.
**Give me the hardest Turkish sentence you can think of.**
I'll run the most interesting ones through the model and share the failures too.
🤗 Model:
Werea-co/Werea-TR-NER
🇹🇷 Werea:
Werea-co
**Follow Werea if you're interested in open Turkish AI — we're publishing the models, benchmarks and failures openly.**
#TurkishNLP #HuggingFace #NER #OpenSourceAI
We tested Werea-TR-NER on the human-labeled WikiANN Turkish test set:
**91.7% Entity F1**
👤 Person → **94.2%**
📍 Location → **91.4%**
🏢 Organization → **89.2%**
Only ~110M parameters.
Try it with a difficult Turkish sentence 👇
Ahmet Yılmaz İstanbul'da Werea şirketinde çalışıyor.→ Ahmet Yılmaz — PERSON
→ İstanbul — LOCATION
→ Werea — ORGANIZATION
But easy examples are boring.
**Give me the hardest Turkish sentence you can think of.**
I'll run the most interesting ones through the model and share the failures too.
🤗 Model:
Werea-co/Werea-TR-NER
🇹🇷 Werea:
**Follow Werea if you're interested in open Turkish AI — we're publishing the models, benchmarks and failures openly.**
#TurkishNLP #HuggingFace #NER #OpenSourceAI