{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "2022hpsk-speakermemr1--run-speakermem-socialmembench-with-local-writer",
 "repo": "2022hpsk/SpeakerMemR1",
 "repo_url": "https://github.com/2022hpsk/SpeakerMemR1",
 "repo_slug": "2022hpsk-speakermemr1",
 "title": "Evaluate SpeakerMem on SocialMemBench with a local Writer",
 "page_title": "How to evaluate SpeakerMem on SocialMemBench with a local Writer with Speaker Mem R1",
 "summary": "Run the main SpeakerMem evaluation with a locally served Writer model instead of an external LLM.",
 "who": "A researcher reproducing the paper's main SocialMemBench result who wants the memory-writing step to run on their own hardware.",
 "problem": "The full dual-track pipeline needs a writer and answerer, and the paper's main numbers depend on configuration choices like top-k and no-ASK settings.",
 "outcome": "A local results folder with SpeakerMem question-level outputs for SocialMemBench using the default main configuration.",
 "steps": [
  "Place the SocialMemBench payload as described in DATA_SOURCES.md under code/ and set credentials from code/.env.example.",
  "Change into the code directory with `cd code`.",
  "Run `PYTHONPATH=.:speakermem_pkg/src python speakermem_pkg/examples/socialmembench.py --benchmark socialmem --domain all --top-k 10 --s2-k 2 --s2-source-k 1 --results-dir ../results/local_run`. (inferred, check the README)",
  "Add `--writer-vllm-url` and `--writer-vllm-model` to point the run at your locally served Writer, and use `--ingest-only` first if you want to build the memory store before QA.",
  "Run `python speakermem_pkg/examples/socialmembench.py --help` to check any other options you need."
 ],
 "agent_prompt": "In the SpeakerMem-R1 repo (2022hpsk/SpeakerMemR1), run speakermem_pkg/examples/socialmembench.py on SocialMemBench with --top-k 10 --s2-k 2 --s2-source-k 1 and a local Writer URL given as <writer-url>. Expected output is results written to the results directory you choose. Verify that the summary reports accuracy for the socialmem domains and that no ASK route was enabled, since the README says the principal comparison uses the no-ASK configuration.",
 "agents": [
  "any"
 ],
 "difficulty": "Advanced",
 "time": "2 hours",
 "basis": "readme",
 "caveat": "The README gives no full reproduction time or hardware needs, and the full-LLM sensitivity study also needs an external held-out memory store that the repo does not include.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "Python with the ability to create a virtual environment",
  "A CPU environment is enough for the quickstart and offline checks",
  "Model service credentials (kept outside version control) for full benchmark runs",
  "Benchmark payloads placed as described in DATA_SOURCES.md for full evaluations"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [
  {
   "tag": "credentials",
   "notice": "The answerer and judge calls need API credentials, which must stay in a local .env file outside version control."
  }
 ],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/2022hpsk-speakermemr1/run-speakermem-socialmembench-with-local-writer/",
 "md": "https://dedupedai.com/use-cases/2022hpsk-speakermemr1/run-speakermem-socialmembench-with-local-writer/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/2022hpsk-speakermemr1--run-speakermem-socialmembench-with-local-writer.json",
 "api_version": "v1"
}