Instructions to use Kelnux/seo-embedding-uczciweseo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kelnux/seo-embedding-uczciweseo with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Kelnux/seo-embedding-uczciweseo")

sentences = [
    "The weather is lovely today.",
    "It's so sunny outside!",
    "He drove to the stadium."
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

llama-cpp-python

How to use Kelnux/seo-embedding-uczciweseo with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Kelnux/seo-embedding-uczciweseo",
	filename="seo-embedding-uczciweseo.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Kelnux/seo-embedding-uczciweseo with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kelnux/seo-embedding-uczciweseo
# Run inference directly in the terminal:
llama-cli -hf Kelnux/seo-embedding-uczciweseo

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Kelnux/seo-embedding-uczciweseo
# Run inference directly in the terminal:
llama-cli -hf Kelnux/seo-embedding-uczciweseo

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Kelnux/seo-embedding-uczciweseo
# Run inference directly in the terminal:
./llama-cli -hf Kelnux/seo-embedding-uczciweseo

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Kelnux/seo-embedding-uczciweseo
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Kelnux/seo-embedding-uczciweseo

Use Docker

docker model run hf.co/Kelnux/seo-embedding-uczciweseo

LM Studio
Jan
Ollama
How to use Kelnux/seo-embedding-uczciweseo with Ollama:
```
ollama run hf.co/Kelnux/seo-embedding-uczciweseo
```

Unsloth Studio new

How to use Kelnux/seo-embedding-uczciweseo with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Kelnux/seo-embedding-uczciweseo to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Kelnux/seo-embedding-uczciweseo to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Kelnux/seo-embedding-uczciweseo to start chatting

Docker Model Runner
How to use Kelnux/seo-embedding-uczciweseo with Docker Model Runner:
```
docker model run hf.co/Kelnux/seo-embedding-uczciweseo
```

Lemonade

How to use Kelnux/seo-embedding-uczciweseo with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Kelnux/seo-embedding-uczciweseo

Run and chat with the model

lemonade run user.seo-embedding-uczciweseo-{{QUANT_TAG}}

List all available models

lemonade list

SEO Embedding Model - UczciweSEO.pl v2

Model embeddingowy fine-tuned dla polskiego rynku SEO, wytrenowany na:

cyberandy/seo-grpo-reasoning-dataset-1000 - dataset SEO reasoning
UczciweSEO.pl - polskie dane SEO
Terminologia seovoc/schema.org

Wyniki

Metryka	v1	v2	Zmiana
Pary podobne	0.7762	0.8229	+0.047
Pary różne	0.3309	0.2982	-0.033
Gap (separacja)	0.4453	0.5247	+0.079

Użycie

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Kelnux/seo-embedding-uczciweseo")

# Generuj embeddingi
sentences = ["technical SEO audit", "audyt techniczny strony"]
embeddings = model.encode(sentences)

# Oblicz podobieństwo
from sklearn.metrics.pairwise import cosine_similarity
similarity = cosine_similarity([embeddings[0]], [embeddings[1]])[0][0]
print(f"Podobieństwo: {similarity:.4f}")

Przykłady

Fraza 1	Fraza 2	Podobieństwo
technical SEO audit steps	crawl errors in Google Search Console	0.79
structured data	dane strukturalne schema.org	0.93
organic traffic	ruch organiczny	0.91
keyword difficulty	long-tail keywords	0.88

Specyfikacja

Wymiar: 384
Base model: paraphrase-multilingual-MiniLM-L12-v2
Dane treningowe: 298 par (SEO-GRPO + UczciweSEO + terminologia)
Języki: polski, angielski

Kategorie treningowe

Technical SEO (crawl issues, indexing)
Content SEO (keywords, optimization)
Link building (internal linking, anchor text)
seovoc/schema.org concepts
UczciweSEO terminology (uczciwe praktyki SEO)

Citation

@misc{seo-embedding-uczciweseo,
  title={SEO Embedding Model for UczciweSEO.pl},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/Kelnux/seo-embedding-uczciweseo}
}

Downloads last month: 36

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for Kelnux/seo-embedding-uczciweseo

Base model

sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

Quantized

(17)

this model

Kelnux
/

seo-embedding-uczciweseo