REPOGEO REPORT · LITE

deepset-ai/FARM

Default branch master · commit 5919538f · scanned 7/1/2026, 8:27:05 PM

GitHub: 1,752 stars · 246 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 README's opening to highlight specialization for production NLP and QA

Why:

CURRENT

Framework for Adapting Representation Models

COPY-PASTE FIX

FARM (Framework for Adapting Representation Models) is a high-level, opinionated framework built on Hugging Face Transformers and PyTorch, designed for fast and easy transfer learning to streamline the fine-tuning and deployment of language models for production-ready NLP applications, with a strong focus on tasks like Question Answering.

mediumtopics#2

Add 'hugging-face-transformers' to 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, hugging-face-transformers, language-models, ner, nlp, nlp-framework, nlp-library, pretrained-models, pytorch, question-answering, roberta, transfer-learning, xlnet-pytorch

mediumreadme#3

Add a 'Why FARM?' or 'Comparison' section to the README

Why:

COPY-PASTE FIX

## Why FARM? 

FARM is a higher-level, opinionated framework built on top of Hugging Face Transformers and PyTorch. While Hugging Face Transformers provides the foundational models, FARM offers a streamlined, production-ready training and fine-tuning pipeline specifically designed for NLP applications, making it easier to adapt and deploy language models for tasks like Question Answering compared to general-purpose deep learning frameworks.

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

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 1×
ludwig-ai/ludwig · recommended 1×
keras-team/keras · recommended 1×
Lightning-AI/lightning · recommended 1×
fastai/fastai · recommended 1×

CATEGORY QUERY
How can I quickly fine-tune pre-trained language models for question answering in NLP?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. Ludwig (ludwig-ai/ludwig)
3. Keras (keras-team/keras)
4. PyTorch Lightning (Lightning-AI/lightning)
5. Fast.ai (fastai/fastai)
AI recommended 5 alternatives but never named deepset-ai/FARM. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Which deep learning framework simplifies adapting large language models for various NLP tasks?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. PyTorch Lightning
3. Keras
4. JAX
5. Flax
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?

Embed your GEO score

Drop this badge into the README of deepset-ai/FARM. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/deepset-ai/FARM.svg)](https://repogeo.com/en/r/deepset-ai/FARM)

HTML

<a href="https://repogeo.com/en/r/deepset-ai/FARM"><img src="https://repogeo.com/badge/deepset-ai/FARM.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

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