REPOGEO REPORT · LITE
cl-tohoku/bert-japanese
Default branch main · commit e4c8b003 · scanned 6/14/2026, 8:17:37 AM
GitHub: 548 stars · 56 forks
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface cl-tohoku/bert-japanese, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- hightopics#1Add relevant topics to improve categorization
Why:
COPY-PASTE FIXjapanese-nlp, bert, pretrained-models, natural-language-processing, huggingface-transformers, language-model, deep-learning, machine-learning
- highreadme#2Explicitly state the models are from Tohoku University in the README
Why:
CURRENT# Pretrained Japanese BERT models This is a repository of pretrained Japanese BERT models.
COPY-PASTE FIX# Pretrained Japanese BERT models by Tohoku University This is the official repository for the pretrained Japanese BERT models developed by Tohoku University.
- mediumhomepage#3Add the Hugging Face model hub as the repository homepage
Why:
COPY-PASTE FIXhttps://huggingface.co/tohoku-nlp
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- BERT · recommended 1×
- Tohoku University's Japanese BERT · recommended 1×
- NICT's Japanese BERT · recommended 1×
- RoBERTa · recommended 1×
- Rinna's Japanese RoBERTa models · recommended 1×
- CATEGORY QUERYNeed a robust pre-trained model for various NLP applications involving Japanese language.you: not recommendedAI recommended (in order):
- BERT
- Tohoku University's Japanese BERT
- NICT's Japanese BERT
- RoBERTa
- Rinna's Japanese RoBERTa models
- GPT-2
- GPT-Neo
- GPT-J
- Rinna's Japanese GPT-2 models
- CyberAgent's Open-CALM models
- T5
- Sonoisa's Japanese T5 models
- DistilBERT
- Tohoku University's DistilBERT
AI recommended 14 alternatives but never named cl-tohoku/bert-japanese. This is the gap to close.
Show full AI answer
- CATEGORY QUERYWhere can I find pre-trained masked language models optimized for Japanese text analysis?you: not recommendedAI recommended (in order):
- BERT-base-Japanese (or BERT-large-Japanese) by Tohoku University
- RoBERTa-base-Japanese (or RoBERTa-large-Japanese) by Tohoku University
- ELECTRA-base-Japanese by Tohoku University
- Japanese BERT by NICT (National Institute of Information and Communications Technology)
- DistilBERT-base-Japanese by Hugging Face
- XLM-RoBERTa-base (or XLM-RoBERTa-large)
AI recommended 6 alternatives but never named cl-tohoku/bert-japanese. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesswarn
Suggestion:
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of cl-tohoku/bert-japanese?passAI named cl-tohoku/bert-japanese explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts cl-tohoku/bert-japanese in production, what risks or prerequisites should they evaluate first?passAI named cl-tohoku/bert-japanese explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- In one sentence, what problem does the repo cl-tohoku/bert-japanese solve, and who is the primary audience?passAI named cl-tohoku/bert-japanese explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
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cl-tohoku/bert-japanese — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite