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Share kit

Use this page when sharing me_fasttext with researchers, list maintainers, benchmarkers, or engineers who need a compact lexical embedding baseline. The goal is discoverability with accurate scope, not hype.

Canonical links

Engram-focused positioning

Chinese long-form article: Engram 之后，高阶 n-gram 记忆还能压缩多少？. The current launch copy and figure links describe the C-readable XOR codec and joint cosine/norm sharing, with scope and tail errors.

Use engram_promotion.md for the current technical post: compact n-gram memory storage and lookup, with a runnable byte-exact row store. Keep the experimental reference separate from the legacy .z exporter and reported paper numbers. The source audit records why legacy approximate merging must not be advertised as lossless.

Short descriptions

One sentence

me_fasttext is a FastText-derived C++ system that keeps word and character
n-gram identities explicit with trie-backed ids, then exports a compact
mmap-friendly lexical embedding artifact.

Directory entry

me_fasttext - Memory-efficient FastText-style lexical embeddings with exact
trie-backed word/subword ids, conservative n-gram row sharing, and compact
mmap serving for OOV-heavy retrieval, ranking, and feature-serving workloads.

Paper-oriented summary

me_fasttext explores an exact-then-compact FastText design: preserve explicit
word and UTF-8 character n-gram ids with trie-backed lookup during training,
then rewrite selected learned rows into a smaller mmap-ready serving artifact.
It is best evaluated with memory, cold-load, latency, OOV coverage, and
downstream quality metrics on a clearly described corpus.

Social posts

Short post

I released me_fasttext, a FastText-derived C++ prototype for memory-efficient
lexical embeddings.

It replaces hash-bucketed subword ids with exact trie-backed ids, then exports
a compact mmap serving artifact for OOV-heavy retrieval, entity-heavy search,
and low-memory feature serving.

Code: https://github.com/initial-d/me_fasttext
Paper: https://arxiv.org/abs/2506.01254

Benchmark ask

If you use FastText-style lexical embeddings, I would love independent
me_fasttext benchmark reports.

Most useful: corpus manifest, original FastText or quantized baseline, .z
artifact size, cold mmap load time, p50/p95 lookup latency, OOV coverage, and a
retrieval/ranking/classification quality metric.

Quickstart: https://github.com/initial-d/me_fasttext/blob/main/docs/evaluator_quickstart.md

Chinese short post

我开源了 me_fasttext：一个 memory-efficient FastText 风格的 C++ 原型。

它把原本 hash bucket 里的 word/subword id 换成 trie-backed exact id，再把
训练后的 n-gram rows 做保守合并和 mark-compact layout，导出可 mmap serving
的紧凑 lexical embedding artifact。

适合 OOV-heavy retrieval、实体检索、中文/多语言长尾词、低内存 feature serving
等场景。

代码: https://github.com/initial-d/me_fasttext
论文: https://arxiv.org/abs/2506.01254

Maintainer note

Hi, thanks for maintaining this list.

Would you consider adding `me_fasttext`?

It is a FastText-derived C++ research prototype for memory-sensitive lexical
embedding serving. Instead of hashing character n-grams into fixed buckets, it
assigns exact trie-backed ids, then exports a compact mmap serving artifact via
conservative row sharing and mark-compact style layout.

Repo: https://github.com/initial-d/me_fasttext
Paper: https://arxiv.org/abs/2506.01254
Artifact card: https://github.com/initial-d/me_fasttext/blob/main/docs/artifact_card.md

Disclosure: I maintain the project. I think it fits best under word embeddings,
retrieval infrastructure, compact inference, or model compression depending on
your taxonomy. Happy to adjust or withdraw if it is out of scope.

Where to share

High-fit places:

  • word embedding and NLP resource lists;
  • retrieval and search engineering lists where lexical prefilters fit;
  • model-compression or efficient-inference lists with non-LLM sections;
  • trie, double-array trie, mmap, or systems discussions about model layout;
  • papers, blog posts, and benchmark reports that need a compact lexical embedding baseline.

Lower-fit places:

  • general LLM-agent directories;
  • vector database lists without lexical or prefilter sections;
  • runtime garbage-collector lists unless they accept applied model-layout work;
  • broad social posts that cannot include benchmark context or scope limits.

Preferred call to action

Ask for one of these:

  • a star or watch if the reader wants to follow benchmark notes;
  • a benchmark result with corpus, commands, hardware, and quality metrics;
  • a showcase issue for citations, list entries, integrations, or use cases;
  • a narrow evaluation question before someone spends time on a full run.

Responsible sharing checklist

  • Say it is a FastText-derived compact lexical embedding system.
  • Mention exact trie-backed word/subword ids and mmap serving.
  • Pair memory or latency claims with corpus and hardware context.
  • Invite negative results and omitted-baseline notes.
  • Do not claim it replaces transformer embeddings.
  • Do not call it a vector database, full RAG framework, LLM memory module, or runtime garbage collector.
  • Do not imply the paper's compression ratio is universal.