Model reference · open weights

debertinha-ptbr-xsmall

Embeddings sagui-nlp Embeddings 1 build Open weights 675 dl/mo

debertinha-ptbr-xsmall is an open-weight embedding model from sagui-nlp. debertinha-ptbr-xsmall (FP32) weighs 82 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.

  • Debertinha-ptbr-xsmall is a feature extraction model created by sagui-nlp for Brazilian Portuguese.
  • It has 41M parameters and a context length of 512 tokens.
  • The model is released under the MIT licence.

Summary of the sagui-nlp/debertinha-ptbr-xsmall model card, 2026-11-03

What it is

Released bysagui-nlp
Released2023-09-20
Parameters41M
VRAM82 MB for the weights

What it runs on

Memory and cards for debertinha-ptbr-xsmall (FP32)

82 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 sagui-nlp says about debertinha-ptbr-xsmall

Read the model card

NOTE

We have received feedback of people getting poor results on unbalanced datasets. A more robust training script, like scaling the loss and adding weight decay (1e-3 to 1e-5) seems to fix it.

on unbalanced datasets can be improved.

If you have any problems using the model, please contact us.

Thanks!

Introduction

DeBERTinha is a pretrained DeBERTa model for Brazilian Portuguese.

Available models

ModelArch.#Params
sagui-nlp/debertinha-ptbr-xsmallDeBERTa-V3-Xsmall40M

Usage

from transformers import AutoTokenizer
from transformers import AutoModelForPreTraining
from transformers import AutoModel

model = AutoModelForPreTraining.from_pretrained('sagui-nlp/debertinha-ptbr-xsmall')
tokenizer = AutoTokenizer.from_pretrained('sagui-nlp/debertinha-ptbr-xsmall')

For embeddings

import torch

model = AutoModel.from_pretrained('sagui-nlp/debertinha-ptbr-xsmall')
input_ids = tokenizer.encode('Tinha uma pedra no meio do caminho.', return_tensors='pt')

with torch.no_grad():
    outs = model(input_ids)
    encoded = outs.last_hidden_state[0, 0]  # Take [CLS] special token representation

Citation

If you use our work, please cite:

@misc{campiotti2023debertinha,
      title={DeBERTinha: A Multistep Approach to Adapt DebertaV3 XSmall for Brazilian Portuguese Natural Language Processing Task},
      author={Israel Campiotti and Matheus Rodrigues and Yuri Albuquerque and Rafael Azevedo and Alyson Andrade},
      year={2023},
      eprint={2309.16844},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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

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