REPOGEO REPORT · LITE

jessevig/bertviz

Default branch master · commit 79dbaebf · scanned 6/26/2026, 5:26:49 AM

GitHub: 8,097 stars · 883 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

28 /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

2 / 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 jessevig/bertviz, 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

Strengthen README's opening to assert specific niche and differentiation

Why:

CURRENT

BertViz is an interactive tool for visualizing attention in Transformer language models. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models. BertViz extends the Tensor2Tensor visualization tool by Llion Jones, providing multiple views that each offer a unique lens into the attention mechanism.

COPY-PASTE FIX

BertViz is the leading interactive tool specifically designed for visualizing and interpreting attention mechanisms in Transformer language models (like BERT, GPT-2, and RoBERTa). Unlike general interpretability libraries, BertViz offers multiple dedicated, interactive views to deeply explore how attention works across layers and heads, making it an essential tool for NLP researchers and practitioners.

mediumhomepage#2
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://colab.research.google.com/drive/1hXIQ77A4TYS4y3UthWF-Ci7V7vVUoxmQ?usp=sharing
```

mediumreadme#3

Add a 'Why BertViz?' or 'Differentiation' section to the README

Why:

COPY-PASTE FIX

Add a new section to the README, e.g., 'Why BertViz?' or 'Scope and Differentiation', with content like: 'Unlike general interpretability libraries such as LIME or Captum, or broad visualization platforms like TensorBoard, BertViz is hyper-focused on providing deep, interactive insights specifically into the attention mechanisms of Transformer models. Our multi-view approach offers unparalleled granularity for understanding complex attention patterns.'

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 jessevig/bertviz

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LIME

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LIME · recommended 2×
Captum · recommended 2×
Ecco · recommended 2×
TensorBoard · recommended 2×
Transformers Interpret · recommended 1×

CATEGORY QUERY
What are good tools to visualize and interpret attention mechanisms in transformer models?
you: not recommended
AI recommended (in order):
1. LIME
2. Captum
3. Ecco
4. Transformers Interpret
5. exBERT
6. TensorBoard
7. Matplotlib
8. Seaborn
AI recommended 8 alternatives but never named jessevig/bertviz. This is the gap to close.
Show full AI answer
CATEGORY QUERY
How can I interactively visualize attention patterns in NLP deep learning models within a notebook?
you: not recommended
AI recommended (in order):
1. LIME
2. Captum
3. Ecco
4. AllenNLP Interpret
5. TensorBoard
6. Hugging Face Transformers
7. ExBERT
AI recommended 7 alternatives but never named jessevig/bertviz. 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 jessevig/bertviz?
pass
AI did not name jessevig/bertviz — 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 jessevig/bertviz in production, what risks or prerequisites should they evaluate first?
pass
AI named jessevig/bertviz 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 jessevig/bertviz solve, and who is the primary audience?
pass
AI named jessevig/bertviz 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 jessevig/bertviz. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

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HTML

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

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite