REPOGEO REPORT · LITE

ashishpatel26/Treasure-of-Transformers

Default branch main · commit 7172afd1 · scanned 5/9/2026, 4:02:43 AM

GitHub: 1,138 stars · 232 forks

AI VISIBILITY SCORE

27 /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

1 / 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 ashishpatel26/Treasure-of-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

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

OVERALL DIRECTION

highreadme#1

Clarify README's opening to state it's a resource collection, not a library

Why:

CURRENT

# Awesome Treasure of Transformers Models Collection

### 🧑‍💻👩‍💻Collection of All NLP Deep learning algorithm list with Code 🧑‍💻👩‍💻

COPY-PASTE FIX

# Awesome Treasure of Transformers: A Curated Collection of NLP Model Resources

### 🧑‍💻👩‍💻Collection of All NLP Deep learning algorithm list with Code 🧑‍💻👩‍💻

This repository is a comprehensive index of links to papers, videos, blogs, official repositories, and Colab notebooks for various Transformer models. It is designed as a central hub for learning and research, not as a production-ready code library or an implementation from scratch.

mediumtopics#2

Refine topics to emphasize "resource list" and remove "library"

Why:

CURRENT

awesome, bert, jax, language-model, language-models, model-hub, natural-language-generation, natural-language-processing, natural-language-understanding, nlp, nlp-library, pretrained-models, python, pytorch, pytorch-transformers, seq2seq, speech-recognition, tensorflow, transformer

COPY-PASTE FIX

awesome, awesome-list, bert, jax, language-model, language-models, model-hub, natural-language-generation, natural-language-processing, natural-language-understanding, nlp, pretrained-models, python, pytorch, pytorch-transformers, resource-list, seq2seq, speech-recognition, tensorflow, transformer

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 ashishpatel26/Treasure-of-Transformers

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face Transformers library

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face Transformers library · recommended 1×
TensorFlow Hub · recommended 1×
PyTorch Hub · recommended 1×
OpenAI Models · recommended 1×
Google AI · recommended 1×

CATEGORY QUERY
Where can I find a comprehensive collection of transformer models for natural language processing?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers library
2. TensorFlow Hub
3. PyTorch Hub
4. OpenAI Models
5. Google AI
6. Microsoft Azure AI
AI recommended 6 alternatives but never named ashishpatel26/Treasure-of-Transformers. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for resources and code examples to implement various NLP transformer architectures.
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers Library (huggingface/transformers)
2. Hugging Face Hub
3. PyTorch
4. TensorFlow
5. Keras
6. AllenNLP (allenai/allennlp)
7. JAX
8. Flax (google/flax)
9. Haiku
AI recommended 9 alternatives but never named ashishpatel26/Treasure-of-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
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 ashishpatel26/Treasure-of-Transformers?
pass
AI did not name ashishpatel26/Treasure-of-Transformers — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts ashishpatel26/Treasure-of-Transformers in production, what risks or prerequisites should they evaluate first?
pass
AI named ashishpatel26/Treasure-of-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 ashishpatel26/Treasure-of-Transformers solve, and who is the primary audience?
pass
AI did not name ashishpatel26/Treasure-of-Transformers — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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ashishpatel26/Treasure-of-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