REPOGEO REPORT · LITE

NVIDIA/OpenSeq2Seq

Default branch master · commit 8681d381 · scanned 6/19/2026, 9:47:59 PM

GitHub: 1,560 stars · 370 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

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

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 NVIDIA/OpenSeq2Seq, 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

mediumreadme#1

Strengthen the README's opening to highlight NVIDIA optimization

Why:

CURRENT

OpenSeq2Seq main goal is to allow researchers to most effectively explore various sequence-to-sequence models. The efficiency is achieved by fully supporting distributed and mixed-precision training. OpenSeq2Seq is built using TensorFlow and provides all the necessary building blocks for training encoder-decoder models for neural machine translation, automatic speech recognition, speech synthesis, and language modeling.

COPY-PASTE FIX

OpenSeq2Seq is a powerful toolkit designed for researchers to effectively explore various sequence-to-sequence models, with a strong focus on efficiency through fully supported distributed and mixed-precision training, especially optimized for NVIDIA Volta/Turing GPUs. Built using TensorFlow, it provides all the necessary building blocks for training encoder-decoder models for neural machine translation, automatic speech recognition, speech synthesis, and language modeling.

lowtopics#2

Add 'archived' to the repository topics

Why:

CURRENT

deep-learning, float16, language-model, mixed-precision, multi-gpu, multi-node, neural-machine-translation, seq2seq, sequence-to-sequence, speech-recognition, speech-synthesis, speech-to-text, tensorflow, text-to-speech

COPY-PASTE FIX

deep-learning, float16, language-model, mixed-precision, multi-gpu, multi-node, neural-machine-translation, seq2seq, sequence-to-sequence, speech-recognition, speech-synthesis, speech-to-text, tensorflow, text-to-speech, archived

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 NVIDIA/OpenSeq2Seq

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

fairseq

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

fairseq · recommended 1×
ESPnet · recommended 1×
Hugging Face Transformers · recommended 1×
NeMo · recommended 1×
TensorFlow TTS · recommended 1×

CATEGORY QUERY
How to efficiently train sequence-to-sequence models for speech recognition and text synthesis?
you: not recommended
AI recommended (in order):
1. fairseq
2. ESPnet
3. Hugging Face Transformers
4. NeMo
5. TensorFlow TTS
AI recommended 5 alternatives but never named NVIDIA/OpenSeq2Seq. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What toolkit provides distributed and mixed-precision training for neural machine translation?
you: not recommended
AI recommended (in order):
1. Fairseq (facebookresearch/fairseq)
2. Hugging Face Transformers (huggingface/transformers)
3. OpenNMT-py (OpenNMT/OpenNMT-py)
4. Tensor2Tensor (T2T) (tensorflow/tensor2tensor)
5. NVIDIA NeMo (NVIDIA/NeMo)
AI recommended 5 alternatives but never named NVIDIA/OpenSeq2Seq. 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 NVIDIA/OpenSeq2Seq?
pass
AI named NVIDIA/OpenSeq2Seq explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts NVIDIA/OpenSeq2Seq in production, what risks or prerequisites should they evaluate first?
pass
AI named NVIDIA/OpenSeq2Seq 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 NVIDIA/OpenSeq2Seq solve, and who is the primary audience?
pass
AI named NVIDIA/OpenSeq2Seq explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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<a href="https://repogeo.com/en/r/NVIDIA/OpenSeq2Seq"><img src="https://repogeo.com/badge/NVIDIA/OpenSeq2Seq.svg" alt="RepoGEO" /></a>

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NVIDIA/OpenSeq2Seq — 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