REPOGEO REPORT · LITE

mlfoundations/dclm

Default branch main · commit 361714bd · scanned 5/9/2026, 4:22:46 AM

GitHub: 1,439 stars · 131 forks

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 mlfoundations/dclm, 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

hightopics#1
Add relevant topics to improve categorization
Why:
COPY-PASTE FIX
```
llm-evaluation, language-models, dataset, benchmarking, machine-learning, nlp
```

highreadme#2

Reposition the README's opening to clearly state DCLM's purpose

Why:

CURRENT

# DataComp-LM (DCLM)

## ⚠️ Updates to centered CORE and EXTENDED calculations (9/5/2025)

COPY-PASTE FIX

# DataComp-LM (DCLM)

A comprehensive, open-source framework and dataset for robustly benchmarking and evaluating large language models (LLMs) across diverse tasks. DCLM provides standardized evaluation metrics and baselines to enable transparent and reproducible comparisons of LLM performance.

## ⚠️ Updates to centered CORE and EXTENDED calculations (9/5/2025)

mediumhomepage#3
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
[Insert URL to project page, paper, or main documentation here]
```

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 mlfoundations/dclm

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Hugging Face 🫡 Evaluate

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Hugging Face 🫡 Evaluate · recommended 1×
Hugging Face 🫡 Datasets · recommended 1×
EleutherAI's LM Evaluation Harness · recommended 1×
OpenAI Evals · recommended 1×
LangChain · recommended 1×

CATEGORY QUERY
How to reliably benchmark and evaluate large language models for performance comparison?
you: not recommended
AI recommended (in order):
1. Hugging Face 🫡 Evaluate
2. Hugging Face 🫡 Datasets
3. EleutherAI's LM Evaluation Harness
4. OpenAI Evals
5. LangChain
6. Weights & Biases (W&B) Prompts
7. Langfuse
8. Ragas
AI recommended 8 alternatives but never named mlfoundations/dclm. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a robust framework to compare language model performance using standardized evaluation metrics.
you: not recommended
AI recommended (in order):
1. EleutherAI's LM Evaluation Harness (lm-eval) (EleutherAI/lm-evaluation-harness)
2. Hugging Face Evaluate (huggingface/evaluate)
3. OpenAI Evals (openai/evals)
4. BigCode's BigCode-Evaluation-Harness (bigcode-project/bigcode-evaluation-harness)
5. LightEval (by Salesforce AI Research) (salesforce/LightEval)
6. Seqeval (chakki-works/seqeval)
AI recommended 6 alternatives but never named mlfoundations/dclm. 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 mlfoundations/dclm?
pass
AI did not name mlfoundations/dclm — 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 mlfoundations/dclm in production, what risks or prerequisites should they evaluate first?
pass
AI named mlfoundations/dclm 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 mlfoundations/dclm solve, and who is the primary audience?
pass
AI named mlfoundations/dclm explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/mlfoundations/dclm.svg)](https://repogeo.com/en/r/mlfoundations/dclm)

HTML

<a href="https://repogeo.com/en/r/mlfoundations/dclm"><img src="https://repogeo.com/badge/mlfoundations/dclm.svg" alt="RepoGEO" /></a>

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mlfoundations/dclm — 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