RRepoGEO

REPOGEO REPORT · LITE

deepset-ai/FARM

Default branch master · commit 5919538f · scanned 5/20/2026, 6:11:45 AM

GitHub: 1,753 stars · 247 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
40 /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
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 deepset-ai/FARM, 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 the README's opening statement to clearly state its purpose

    Why:

    CURRENT
    FARM (Framework for Adapting Representation Models)
    COPY-PASTE FIX
    FARM (Framework for Adapting Representation Models) is a PyTorch-based NLP framework for fast and easy transfer learning with large language models, specifically designed for tasks like Question Answering, NER, and document classification.
  • mediumreadme#2
    Add a dedicated 'Why FARM?' or 'Key Differentiators' section to the README

    Why:

    COPY-PASTE FIX
    ## Why FARM?
FARM provides a higher-level, opinionated API and a complete workflow for fine-tuning transformer models (like BERT, RoBERTa, etc.) for various NLP tasks. While it builds on top of Hugging Face's Transformers, FARM streamlines the entire process from data preparation to deployment, making it ideal for industry applications requiring robust and reproducible NLP pipelines.
  • lowtopics#3
    Add 'transformer-models' to the repository topics

    Why:

    CURRENT
    bert, deep-learning, germanbert, language-models, ner, nlp, nlp-framework, nlp-library, pretrained-models, pytorch, question-answering, roberta, transfer-learning, xlnet-pytorch
    COPY-PASTE FIX
    bert, deep-learning, germanbert, language-models, ner, nlp, nlp-framework, nlp-library, pretrained-models, pytorch, question-answering, roberta, transfer-learning, transformer-models, xlnet-pytorch

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 deepset-ai/FARM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. PyTorch Lightning · recommended 1×
  3. Keras · recommended 1×
  4. TensorFlow Hub · recommended 1×
  5. fast.ai · recommended 1×
  • CATEGORY QUERY
    What's a good NLP library for fast transfer learning with large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. Keras
    4. TensorFlow Hub
    5. fast.ai

    AI recommended 5 alternatives but never named deepset-ai/FARM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a PyTorch-based NLP framework for building robust question answering systems easily.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Haystack
    3. AllenNLP
    4. PyTorch-Lightning
    5. spaCy

    AI recommended 5 alternatives but never named deepset-ai/FARM. 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 deepset-ai/FARM?
    pass
    AI named deepset-ai/FARM explicitly

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

  • If a team adopts deepset-ai/FARM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named deepset-ai/FARM 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 deepset-ai/FARM solve, and who is the primary audience?
    pass
    AI named deepset-ai/FARM explicitly

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

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deepset-ai/FARM — 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