🏗️ Building on HF

Göktuğ Düşünen PRO

GoktugD

9 3

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

posted an update about 9 hours 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

posted an update 3 days ago

🇹🇷 One of our small Turkish models quietly reached **500+ monthly downloads** on Hugging Face. **Werea-TR-TextRestore — only 300M parameters.** Its job is simple: `istanbulda hava cok guzel` → `İstanbul'da hava çok güzel.` A lightweight model for restoring Turkish text: • diacritics • punctuation • casing • corrupted text **96.5% word accuracy** on real Turkish news sentences. And it runs without sending your text to a cloud API. 🤗 Try the model: https://huggingface.co/Werea-co/Werea-TR-TextRestore 🇹🇷 Built in Türkiye. Open source. If you're working on Turkish NLP, I'd love to hear what we should build next. #TurkishNLP #HuggingFace #OpenSourceAI #NLP

posted an update 12 days ago

🇹🇷 We started with one question: **How much of the Turkish AI stack can we build openly?** Today, Werea has grown to **19 open models on Hugging Face.** Not just LLMs. 📄 Document AI — Werea-DocOCR-1B 🛡️ Cybersecurity — Werea-NanoSOC-8B 🔐 Privacy / KVKK — Werea-KVKK-Agent-4B 🔍 Retrieval — DUSUNEN-Rota-270M 🎙️ Speech — Werea-TSS 🧠 Turkish NLP — NER, NLI, QA, Intent, Sentiment, Topic & more 👁️ Computer Vision — Gemstone And we want the results to be measurable. Some of our published benchmarks: 🏷️ NER → **91.7% F1** — WikiANN-tr 🗂️ Topic → **92.8% accuracy** — TTC4900 🎯 Intent → **88.2% accuracy** — MASSIVE-tr 🧩 NLI → **74.5% accuracy** — XNLI-tr ❓ QA → **72.7% F1** — TQuAD2 📄 DocOCR → **0.15% CER** on our held-out Turkish enterprise document test Our goal isn't to upload as many models as possible. Our goal is to build an **open Turkish AI ecosystem**: Models. Datasets. Benchmarks. Demos. Real applications. Built from Türkiye. 🇹🇷 Open to everyone. 🤗 Explore Werea: https://huggingface.co/Werea-co 📄 Document AI: https://huggingface.co/Werea-co/Werea-DocOCR-1B 🛡️ NanoSOC: https://huggingface.co/Werea-co/Werea-NanoSOC-8B 🔐 KVKK Agent: https://huggingface.co/Werea-co/Werea-KVKK-Agent-4B 🔍 Rota: https://huggingface.co/Werea-co/DUSUNEN-Rota-270M-v3 If you're building Turkish AI, follow Werea — there's much more coming.

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Post

🇹🇷 **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:
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

Post

2399

🇹🇷 One of our small Turkish models quietly reached **500+ monthly downloads** on Hugging Face.

**Werea-TR-TextRestore — only 300M parameters.**

Its job is simple:

istanbulda hava cok guzel
→ İstanbul'da hava çok güzel.

A lightweight model for restoring Turkish text:
• diacritics
• punctuation
• casing
• corrupted text

**96.5% word accuracy** on real Turkish news sentences.

And it runs without sending your text to a cloud API.

🤗 Try the model:
Werea-co/Werea-TR-TextRestore

🇹🇷 Built in Türkiye. Open source.

If you're working on Turkish NLP, I'd love to hear what we should build next.

#TurkishNLP #HuggingFace #OpenSourceAI #NLP