RRepoGEO

REPOGEO REPORT · LITE

yandex/faster-rnnlm

Default branch master · commit c35e481d · scanned 6/20/2026, 12:03:21 AM

GitHub: 564 stars · 137 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

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 yandex/faster-rnnlm, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening to emphasize 'C++ toolkit'

    Why:

    CURRENT
    In a nutshell, the goal of this project is to create an rnnlm implementation that can be trained on huge datasets (several billions of words) and very large vocabularies (several hundred thousands) and used in real-world ASR and MT problems.
    COPY-PASTE FIX
    **Faster RNNLM (HS/NCE) toolkit** is a highly optimized C++ toolkit for Recurrent Neural Network Language Models (RNNLM). Its primary goal is to enable training on massive datasets (billions of words) and very large vocabularies (hundreds of thousands) for real-world Automatic Speech Recognition (ASR) and Machine Translation (MT) problems.
  • mediumlicense#2
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under [Specify License Name(s) here, e.g., Apache 2.0 and MIT]. Please see the `LICENSE` file for full details.

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.

Recall
0 / 2
0% of queries surface yandex/faster-rnnlm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. TensorFlow · recommended 2×
  3. Keras · recommended 2×
  4. Hugging Face Transformers · recommended 2×
  5. PyTorch Lightning · recommended 1×
  • CATEGORY QUERY
    How to train large-scale recurrent neural network language models efficiently on massive text datasets?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. PyTorch Lightning
    3. DeepSpeed
    4. FairScale
    5. TensorFlow
    6. Keras
    7. Horovod
    8. tf.distribute.MirroredStrategy
    9. tf.distribute.MultiWorkerMirroredStrategy
    10. NVIDIA Apex
    11. Hugging Face Transformers
    12. JAX
    13. Flax
    14. Haiku

    AI recommended 14 alternatives but never named yandex/faster-rnnlm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best options for recurrent neural language modeling using NCE or hierarchical softmax?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch
    3. TensorFlow
    4. Gensim
    5. Keras

    AI recommended 5 alternatives but never named yandex/faster-rnnlm. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

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 yandex/faster-rnnlm?
    pass
    AI named yandex/faster-rnnlm explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts yandex/faster-rnnlm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named yandex/faster-rnnlm 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 yandex/faster-rnnlm solve, and who is the primary audience?
    pass
    AI named yandex/faster-rnnlm explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of yandex/faster-rnnlm. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/yandex/faster-rnnlm.svg)](https://repogeo.com/en/r/yandex/faster-rnnlm)
HTML
<a href="https://repogeo.com/en/r/yandex/faster-rnnlm"><img src="https://repogeo.com/badge/yandex/faster-rnnlm.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

yandex/faster-rnnlm — 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