RRepoGEO

REPOGEO REPORT · LITE

OpenNMT/OpenNMT-py

Default branch master · commit 97111d97 · scanned 5/21/2026, 12:16:44 AM

GitHub: 7,000 stars · 2,249 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
68 /100
Needs work
Category recall
1 / 2
Avg rank #3.0 when recommended
Rule findings
2 pass · 0 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 OpenNMT/OpenNMT-py, 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
  • highreadme#1
    Clarify project status in README's opening announcement

    Why:

    CURRENT
    # Announcement: OpenNMT-py is no longer actively supported.
    COPY-PASTE FIX
    # Announcement: OpenNMT-py is now in a maintenance-only phase, focusing on stability for existing users and projects. For new development and advanced features, please consider our successor project, Eole, available on Github.
  • mediumreadme#2
    Emphasize NLP research and flexibility in the main description

    Why:

    CURRENT
    OpenNMT-py is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation (and beyond!) framework. It is designed to be research friendly to try out new ideas in translation, language modeling, summarization, and many other NLP tasks.
    COPY-PASTE FIX
    OpenNMT-py is a highly flexible and research-friendly PyTorch framework for neural machine translation, language modeling, summarization, and a wide range of other NLP tasks. It provides granular control over NMT architectures, making it ideal for experimenting with new ideas and building custom models.
  • lowtopics#3
    Add specific NLP research and summarization topics

    Why:

    CURRENT
    deep-learning, language-model, llms, machine-translation, neural-machine-translation, pytorch
    COPY-PASTE FIX
    deep-learning, language-model, llms, machine-translation, neural-machine-translation, nlp-research, pytorch, summarization

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
1 / 2
50% of queries surface OpenNMT/OpenNMT-py
Avg rank
#3.0
Lower is better. #1 = top recommendation.
Share of voice
10%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. facebookresearch/fairseq · recommended 2×
  3. microsoft/DeepSpeed · recommended 1×
  4. huggingface/accelerate · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    What are good open-source PyTorch frameworks for neural machine translation and large language models?
    you: #3
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Fairseq (facebookresearch/fairseq)
    3. OpenNMT-py (OpenNMT/OpenNMT-py) ← you
    4. DeepSpeed (microsoft/DeepSpeed)
    5. Accelerate (huggingface/accelerate)
    Show full AI answer
  • CATEGORY QUERY
    Seeking a flexible deep learning toolkit for NLP research, especially machine translation and summarization.
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. Hugging Face Transformers (huggingface/transformers)
    3. TensorFlow (tensorflow/tensorflow)
    4. JAX (google/jax)
    5. fairseq (facebookresearch/fairseq)

    AI recommended 5 alternatives but never named OpenNMT/OpenNMT-py. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 OpenNMT/OpenNMT-py?
    pass
    AI named OpenNMT/OpenNMT-py explicitly

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

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

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

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OpenNMT/OpenNMT-py — 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