REPOGEO REPORT · LITE

explosion/curated-transformers

Default branch main · commit e7e7e9da · scanned 6/9/2026, 7:16:54 PM

GitHub: 896 stars · 35 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 explosion/curated-transformers, 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

Reposition README opening to highlight production-readiness and spaCy integration

Why:

CURRENT

State-of-the-art transformers, brick by brick

Curated Transformers is a transformer library for PyTorch. It provides state-of-the-art models that are composed from a set of reusable components.

COPY-PASTE FIX

Curated Transformers is a production-tested PyTorch library for state-of-the-art transformer models, including LLMs, built from reusable components. It's designed for robust NLP applications and will be the default transformer implementation in spaCy 3.7.

mediumhomepage#2
Add the official documentation URL as the homepage
Why:
COPY-PASTE FIX
```
https://curated-transformers.readthedocs.io/en/latest/
```

mediumtopics#3

Add 'spacy' to the repository topics

Why:

CURRENT

albert, bert, camembert, dolly2, falcon, gptneox, llama, llm, llms, nlp, pytorch, roberta, transformer, transformers, xlm-roberta

COPY-PASTE FIX

albert, bert, camembert, dolly2, falcon, gptneox, llama, llm, llms, nlp, pytorch, roberta, spacy, transformer, transformers, xlm-roberta

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 explosion/curated-transformers

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers · recommended 1×
PyTorch-Lightning · recommended 1×
DeepSpeed · recommended 1×
Accelerate · recommended 1×
bitsandbytes · recommended 1×

CATEGORY QUERY
How can I easily integrate various transformer models, including LLMs, into my PyTorch projects?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. PyTorch-Lightning
3. DeepSpeed
4. Accelerate
5. bitsandbytes
6. fairseq
AI recommended 6 alternatives but never named explosion/curated-transformers. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a modular Python library to build custom transformer architectures with reusable components.
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. PyTorch (pytorch/pytorch)
3. Keras (keras-team/keras)
4. JAX (google/jax)
5. Flax (google/flax)
6. Haiku (deepmind/dm-haiku)
7. Trax (google/trax)
AI recommended 7 alternatives but never named explosion/curated-transformers. 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 explosion/curated-transformers?
pass
AI named explosion/curated-transformers explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts explosion/curated-transformers in production, what risks or prerequisites should they evaluate first?
pass
AI named explosion/curated-transformers 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 explosion/curated-transformers solve, and who is the primary audience?
pass
AI named explosion/curated-transformers 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 explosion/curated-transformers. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/explosion/curated-transformers.svg)](https://repogeo.com/en/r/explosion/curated-transformers)

HTML

<a href="https://repogeo.com/en/r/explosion/curated-transformers"><img src="https://repogeo.com/badge/explosion/curated-transformers.svg" alt="RepoGEO" /></a>

Pro

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explosion/curated-transformers — 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