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Werea-co
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https://werea.co

AI & ML interests

Head of AI @ Werea · AI/ML Engineer AI/ML Engineer building end-to-end intelligent systems across LLMs, NLP, Computer Vision, RAG, Retrieval, Agents, and Applied AI. From data and model development to fine-tuning, evaluation, optimization, deployment, and production AI systems.

Recent Activity

reacted to theirpost with 👍 about 14 hours ago
🇹🇷 **You tried to break our Turkish NER model.** And that's exactly what we wanted. After sharing Werea-TR-NER, we asked the Hugging Face community to challenge our ~110M parameter model with difficult Turkish sentences. Some worked. Some exposed weaknesses. And that's more valuable than pretending a benchmark score tells the whole story. Our published WikiANN-tr result: **91.7% Entity F1** 👤 PERSON → 94.2% 📍 LOCATION → 91.4% 🏢 ORGANIZATION → 89.2% But now we want to go further. 🔥 **ROUND 2** Send me a Turkish sentence designed specifically to break the model. Ambiguous names. Companies that sound like people. Locations hidden inside organization names. Turkish suffixes. Slang. Anything nasty. **Try to make it fail.** I'll collect the hardest examples and use them to build a public adversarial Turkish NER evaluation set. 🤗 Model: https://huggingface.co/Werea-co/Werea-TR-NER 🇹🇷 Werea: https://huggingface.co/Werea-co Follow me if you want to see whether the community can break it — and what we build from the failures. #TurkishNLP #HuggingFace #NER #OpenSourceAI
posted an update 3 days ago
🇹🇷 **You tried to break our Turkish NER model.** And that's exactly what we wanted. After sharing Werea-TR-NER, we asked the Hugging Face community to challenge our ~110M parameter model with difficult Turkish sentences. Some worked. Some exposed weaknesses. And that's more valuable than pretending a benchmark score tells the whole story. Our published WikiANN-tr result: **91.7% Entity F1** 👤 PERSON → 94.2% 📍 LOCATION → 91.4% 🏢 ORGANIZATION → 89.2% But now we want to go further. 🔥 **ROUND 2** Send me a Turkish sentence designed specifically to break the model. Ambiguous names. Companies that sound like people. Locations hidden inside organization names. Turkish suffixes. Slang. Anything nasty. **Try to make it fail.** I'll collect the hardest examples and use them to build a public adversarial Turkish NER evaluation set. 🤗 Model: https://huggingface.co/Werea-co/Werea-TR-NER 🇹🇷 Werea: https://huggingface.co/Werea-co Follow me if you want to see whether the community can break it — and what we build from the failures. #TurkishNLP #HuggingFace #NER #OpenSourceAI
posted an update 6 days ago
🇹🇷 **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 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: https://huggingface.co/Werea-co/Werea-TR-NER 🇹🇷 Werea: https://huggingface.co/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
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