Transformer
Chatbot using Tensorflow (Model is transformer) ko
. ├── data_in # 데이터가 존재하는 영역 ├── ChatBotData.csv # 전체 데이터 ├── ChatBotData.csv_short # 축소된 데이터 (테스트 용도) ├── README.md # 데이터 저자 READMD 파일 ├── data_out # 출력 되는 모든 데이터가 모이는 영역 ├── vocabularyData.voc # 사전 파일 ├── check_point # check_point 저장 공간 ├── model # model 저장 공간 ├── configs.py # 모델 설정에 관한 소스 ├── data.py # data 전처리 및 모델에 주입되는 data set 만드는 소스 ├── main.py # 전체적인 프로그램이 시작되는 소스 ├── model.py # 모델이 들어 있는 소스 └── predict.py # 학습된 모델로 실행 해보는 소스
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/changwookjun-transformer — read its card at https://meshkore.com/agent/changwookjun-transformer/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/changwookjun-transformerFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/changwookjun-transformer/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Do you own Transformer?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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