RRepoGEO

REPOGEO REPORT · LITE

tensorflow/nmt

Default branch master · commit 0be86425 · scanned 6/30/2026, 4:12:42 PM

GitHub: 6,463 stars · 1,936 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 tensorflow/nmt, 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

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

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    neural-machine-translation, nmt, seq2seq, tensorflow, deep-learning, nlp, tutorial, reference-implementation, attention-mechanism
  • highreadme#2
    Add a clear introductory sentence to the README

    Why:

    CURRENT
    # Neural Machine Translation (seq2seq) Tutorial

*Authors: Thang Luong, Eugene Brevdo, Rui Zhao (Google Research Blogpost, Github)*

*This version of the tutorial requires TensorFlow Nightly.
For using the stable TensorFlow versions, please consider other branches such as
tf-1.4.*
    COPY-PASTE FIX
    # Neural Machine Translation (seq2seq) Tutorial

This repository provides a comprehensive, hands-on tutorial and reference implementation for building Neural Machine Translation (NMT) systems from the ground up using TensorFlow. It guides users through the core concepts of seq2seq models with attention, serving as a practical guide for researchers and practitioners to understand and implement NMT.

*Authors: Thang Luong, Eugene Brevdo, Rui Zhao (Google Research Blogpost, Github)*

*This version of the tutorial requires TensorFlow Nightly.
For using the stable TensorFlow versions, please consider other branches such as
tf-1.4.*
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    [Insert the URL of the associated Google Research Blogpost here]

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 tensorflow/nmt
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. OpenNMT-py · recommended 2×
  3. PyTorch · recommended 2×
  4. Fairseq · recommended 1×
  5. TensorFlow (with Keras) · recommended 1×
  • CATEGORY QUERY
    How to build a custom neural machine translation system using deep learning?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Fairseq
    3. OpenNMT-py
    4. TensorFlow (with Keras)
    5. PyTorch
    6. Marian NMT

    AI recommended 6 alternatives but never named tensorflow/nmt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a practical guide for implementing sequence-to-sequence models with attention for language tasks.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch
    3. TensorFlow
    4. OpenNMT-py
    5. fairseq

    AI recommended 5 alternatives but never named tensorflow/nmt. 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 tensorflow/nmt?
    pass
    AI named tensorflow/nmt explicitly

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

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

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

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tensorflow/nmt — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite