Model reference · open weights

simcse-retromae-small-cs

Embeddings Seznam Embeddings 1 build Open weights 517 dl/mo

simcse-retromae-small-cs is an open-weight embedding model from Seznam. simcse-retromae-small-cs (BF16) weighs 97 MB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • simcse-retromae-small-cs is a Czech sentence-similarity model developed by Seznam.
  • It is a fine-tuned version of the retromae-small-cs model designed for tasks such as similarity search, retrieval, clustering, and classification.
  • The model supports a context length of 512 tokens and is released under the cc-by-4.0 licence.

Summary of the Seznam/simcse-retromae-small-cs model card, 2026-10-01

What it is

Released bySeznam
Released2023-11-02
VRAM97 MB for the weights

What it runs on

Memory and cards for simcse-retromae-small-cs (BF16)

97 MBweights, file size
1.1 GBruntime overhead
CardRunsMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What Seznam says about simcse-retromae-small-cs

Read the model card

SimCSE

SimCSE-RetroMAE-Small is the Seznam/retromae-small-cs model fine-tuned with the SimCSE objective.

This model was created at Seznam.cz as part of a project to create high-quality small Czech semantic embedding models. These models perform well across various natural language processing tasks, including similarity search, retrieval, clustering, and classification. For further details or evaluation results, please visit the associated paper or GitHub repository.

How to Use

You can load and use the model like this:

import torch
from transformers import AutoModel, AutoTokenizer

model_name = "Seznam/retromae-small-cs"  # Hugging Face link
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

input_texts = [
    "Dnes je výborné počasí na procházku po parku.",
    "Večer si oblíbím dobrý film a uvařím si čaj."
]

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = outputs.last_hidden_state[:, 0]  # Extract CLS token embeddings

similarity = torch.nn.functional.cosine_similarity(embeddings[0], embeddings[1], dim=0)

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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