RRepoGEO

REPOGEO REPORT · LITE

HarderThenHarder/transformers_tasks

Default branch main · commit 5464a2c1 · scanned 6/19/2026, 4:13:02 PM

GitHub: 2,421 stars · 401 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
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 HarderThenHarder/transformers_tasks, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root. Choose a standard open-source license (e.g., MIT, Apache-2.0, GPL-3.0) and add its full text to this file.
  • highreadme#2
    Add a concise English positioning statement to the README

    Why:

    CURRENT
    该项目集成了基于 transformers 库实现的多种 NLP 任务。
    COPY-PASTE FIX
    Add the following English summary at the very top of the README, before any existing Chinese text or decorative elements:

"This repository provides ready-to-use, fine-tuned implementations for various NLP tasks (Text Classification, Generation, Information Extraction, Text Matching, RLHF, SFT) built *on top of* the Hugging Face Transformers library. It serves as a practical toolkit for practitioners and researchers to quickly adapt and deploy transformer models for specific tasks."
  • mediumtopics#3
    Add 'llm' and 'fine-tuning' to repository topics

    Why:

    CURRENT
    information-extraction, nlp, reinforcement-learning, text-classification, text-generation, text-matching, transformers
    COPY-PASTE FIX
    information-extraction, llm, nlp, reinforcement-learning, text-classification, text-generation, text-matching, transformers, fine-tuning

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 HarderThenHarder/transformers_tasks
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. spaCy · recommended 1×
  3. Keras · recommended 1×
  4. PyTorch Lightning · recommended 1×
  5. Flair · recommended 1×
  • CATEGORY QUERY
    How to implement various NLP tasks such as text classification and information extraction with transformer models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. spaCy
    3. Keras
    4. PyTorch Lightning
    5. Flair
    6. AllenNLP

    AI recommended 6 alternatives but never named HarderThenHarder/transformers_tasks. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a toolkit to fine-tune large language models for text generation and human feedback reinforcement learning.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face PEFT (huggingface/peft)
    3. trl (huggingface/trl)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. PyTorch FSDP
    6. OpenAI Baselines (openai/baselines)
    7. RLlib (ray-project/ray)

    AI recommended 7 alternatives but never named HarderThenHarder/transformers_tasks. 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 HarderThenHarder/transformers_tasks?
    pass
    AI did not name HarderThenHarder/transformers_tasks — 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 HarderThenHarder/transformers_tasks in production, what risks or prerequisites should they evaluate first?
    pass
    AI named HarderThenHarder/transformers_tasks 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 HarderThenHarder/transformers_tasks solve, and who is the primary audience?
    pass
    AI named HarderThenHarder/transformers_tasks explicitly

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

Embed your GEO score

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HarderThenHarder/transformers_tasks — 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