Model reference · open weights
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.
Summary of the sagui-nlp/debertinha-ptbr-xsmall model card, 2026-11-03
What it is
| Released by | sagui-nlp |
|---|---|
| Released | 2023-09-20 |
| Parameters | 41M |
| VRAM | 82 MB for the weights |
What it runs on
| Card | Runs | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
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!
DeBERTinha is a pretrained DeBERTa model for Brazilian Portuguese.
| Model | Arch. | #Params |
|---|---|---|
sagui-nlp/debertinha-ptbr-xsmall | DeBERTa-V3-Xsmall | 40M |
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')
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
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.