REPOGEO REPORT · LITE

datawhalechina/learn-nlp-with-transformers

Default branch main · commit 37564224 · scanned 5/21/2026, 12:38:07 PM

GitHub: 3,236 stars · 511 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

22 /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

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 datawhalechina/learn-nlp-with-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's English subtitle and About description to emphasize 'learning curriculum for Chinese NLP'

Why:

CURRENT

Natural Language Processing with transformers.

COPY-PASTE FIX

A community-driven, open-source learning curriculum for Natural Language Processing with Transformers, primarily presented in Chinese.

highlicense#2

Add a LICENSE file to the repository

Why:

CURRENT

(no LICENSE file detected — the repo has no recognizable license)

COPY-PASTE FIX

Create a LICENSE file (e.g., MIT or Apache-2.0) in the repository root to clearly state the terms of use.

mediumtopics#3
Add more specific topics to highlight 'learning' and 'Chinese' aspects
Why:
CURRENT
```
bert, nlp, transformer
```
COPY-PASTE FIX
```
bert, nlp, transformer, chinese-nlp, nlp-tutorial, deep-learning-course, machine-learning-education
```

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 datawhalechina/learn-nlp-with-transformers

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 1×
PaddlePaddle/PaddleNLP · recommended 1×
stanfordnlp/stanza · recommended 1×
pytorch/pytorch · recommended 1×
tensorflow/tensorflow · recommended 1×

CATEGORY QUERY
How can I learn natural language processing using transformer models for Chinese text?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers Library (huggingface/transformers)
2. PaddleNLP (PaddlePaddle/PaddleNLP)
3. StanfordNLP (Stanza) (stanfordnlp/stanza)
4. PyTorch (pytorch/pytorch)
5. TensorFlow (tensorflow/tensorflow)
6. Jieba (fxsjy/jieba)
7. Datasets (Hugging Face) (huggingface/datasets)
AI recommended 7 alternatives but never named datawhalechina/learn-nlp-with-transformers. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are good resources for implementing BERT-based models for Chinese NLP tasks?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. PaddleNLP
3. Keras/TensorFlow
4. PyTorch
5. THU-KEG/Chinese-BERT-wwm (THU-KEG/Chinese-BERT-wwm)
AI recommended 5 alternatives but never named datawhalechina/learn-nlp-with-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 datawhalechina/learn-nlp-with-transformers?
pass
AI did not name datawhalechina/learn-nlp-with-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 datawhalechina/learn-nlp-with-transformers in production, what risks or prerequisites should they evaluate first?
pass
AI named datawhalechina/learn-nlp-with-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 datawhalechina/learn-nlp-with-transformers solve, and who is the primary audience?
pass
AI did not name datawhalechina/learn-nlp-with-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?

Embed your GEO score

Drop this badge into the README of datawhalechina/learn-nlp-with-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.

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MARKDOWN (README)

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datawhalechina/learn-nlp-with-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