RRepoGEO

REPOGEO REPORT · LITE

yandex/faster-rnnlm

Default branch master · commit c35e481d · scanned 5/10/2026, 2:37:44 AM

GitHub: 565 stars · 137 forks

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
    Emphasize 'toolkit' and 'speed' in the README's opening sentence

    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
    This project is a high-performance toolkit for Recurrent Neural Network Language Modeling (RNNLM), designed for training on massive datasets and very large vocabularies, specifically for real-world ASR and MT applications.
  • mediumreadme#2
    Clarify the existing license(s) in the README

    Why:

    COPY-PASTE FIX
    This project is distributed under [Specify License Name(s) here, e.g., 'a custom license based on Apache 2.0 and MIT']. Please refer to 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. PyTorch Lightning · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. TensorFlow · recommended 1×
  5. DeepSpeed · recommended 1×
  • CATEGORY QUERY
    What are efficient tools for training large-scale recurrent neural network language models quickly?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning
    2. PyTorch
    3. Hugging Face Transformers
    4. TensorFlow
    5. DeepSpeed
    6. Keras
    7. JAX
    8. Flax
    9. Haiku
    10. Horovod

    AI recommended 10 alternatives but never named yandex/faster-rnnlm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a language modeling toolkit supporting NCE or hierarchical softmax for ASR tasks.
    you: not recommended
    AI recommended (in order):
    1. fairseq
    2. ESPnet
    3. OpenNMT-py
    4. TensorFlow/Keras
    5. PyTorch

    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