RRepoGEO

REPOGEO REPORT · LITE

The-FinAI/PIXIU

Default branch main · commit da45ac46 · scanned 6/10/2026, 7:18:20 PM

GitHub: 866 stars · 116 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 The-FinAI/PIXIU, 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
    Add a concise project summary to the top of the README

    Why:

    COPY-PASTE FIX
    PIXIU is an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks designed to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    [Your project's dedicated website or paper URL, e.g., https://the-finai.github.io/PIXIU/]
  • mediumtopics#3
    Add more specific topics related to financial LLM evaluation

    Why:

    CURRENT
    aifinance, chatgpt, fintech, gpt-4, large-language-models, llama, machine-learning, named-entity-recognition, natural-language-processing, nlp, pixiu, question-answering, sentiment-analysis, stock-price-prediction, text-classification
    COPY-PASTE FIX
    aifinance, chatgpt, fintech, gpt-4, large-language-models, llama, machine-learning, named-entity-recognition, natural-language-processing, nlp, pixiu, question-answering, sentiment-analysis, stock-price-prediction, text-classification, financial-llm-benchmark, financial-ai-evaluation, financial-nlp-datasets

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 The-FinAI/PIXIU
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Bloomberg Terminal
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Bloomberg Terminal · recommended 1×
  2. Refinitiv Eikon/Workspace · recommended 1×
  3. S&P Global Market Intelligence · recommended 1×
  4. FactSet · recommended 1×
  5. Quandl (Nasdaq Data Link) · recommended 1×
  • CATEGORY QUERY
    How to build and evaluate large language models for financial applications?
    you: not recommended
    AI recommended (in order):
    1. Bloomberg Terminal
    2. Refinitiv Eikon/Workspace
    3. S&P Global Market Intelligence
    4. FactSet
    5. Quandl (Nasdaq Data Link)
    6. Alpha Vantage
    7. Yahoo Finance API
    8. Pandas (pandas-dev/pandas)
    9. NumPy (numpy/numpy)
    10. SpaCy (explosion/spaCy)
    11. NLTK (nltk/nltk)
    12. Hugging Face Transformers Library (huggingface/transformers)
    13. BERT
    14. RoBERTa
    15. GPT-2/3
    16. Llama 2
    17. Mistral
    18. T5
    19. OpenAI API
    20. Google Cloud Vertex AI
    21. Azure OpenAI Service
    22. FinancialBERT / FinBERT (ProsusAI/finbert)
    23. BloombergGPT
    24. PyTorch (pytorch/pytorch)
    25. TensorFlow / Keras (tensorflow/tensorflow)
    26. AWS SageMaker
    27. Google Cloud AI Platform
    28. Microsoft Azure Machine Learning
    29. AWS SageMaker Endpoints
    30. Google Cloud Vertex AI Endpoints
    31. Azure Machine Learning Endpoints
    32. Hugging Face Inference Endpoints
    33. Kubernetes (kubernetes/kubernetes)
    34. OpenShift
    35. Google Kubernetes Engine
    36. Azure Kubernetes Service
    37. Amazon EKS
    38. NVIDIA Triton Inference Server (triton-inference-server/server)
    39. LIME (marcotcr/lime)
    40. SHAP (shap/shap)
    41. Fairlearn (Microsoft) (microsoft/fairlearn)
    42. Aequitas (dssg/aequitas)
    43. MLflow (mlflow/mlflow)
    44. Weights & Biases (wandb/wandb)
    45. TensorBoard (tensorflow/tensorboard)
    46. Prometheus (prometheus/prometheus)
    47. Grafana (grafana/grafana)
    48. Tableau
    49. Power BI
    50. Streamlit (streamlit/streamlit)

    AI recommended 50 alternatives but never named The-FinAI/PIXIU. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source resources are available for financial natural language processing tasks?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Hugging Face Datasets
    3. spaCy
    4. NLTK
    5. Gensim
    6. Flair
    7. OpenNMT
    8. TextBlob

    AI recommended 8 alternatives but never named The-FinAI/PIXIU. 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 The-FinAI/PIXIU?
    pass
    AI named The-FinAI/PIXIU explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts The-FinAI/PIXIU in production, what risks or prerequisites should they evaluate first?
    pass
    AI named The-FinAI/PIXIU 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 The-FinAI/PIXIU solve, and who is the primary audience?
    pass
    AI named The-FinAI/PIXIU 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 The-FinAI/PIXIU. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/The-FinAI/PIXIU.svg)](https://repogeo.com/en/r/The-FinAI/PIXIU)
HTML
<a href="https://repogeo.com/en/r/The-FinAI/PIXIU"><img src="https://repogeo.com/badge/The-FinAI/PIXIU.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

The-FinAI/PIXIU — 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