README.md

metadata

license: apache-2.0
datasets:
  - cnmoro/GPT4-500k-Augmented-PTBR-Clean
  - cnmoro/WizardVicuna-PTBR-Instruct-Clean
  - sentence-transformers/natural-questions
language:
  - en
  - pt
base_model:
  - prajjwal1/bert-tiny
pipeline_tag: sentence-similarity
library_name: sentence-transformers
tags:
  - splade
  - sparse
  - embeddings
  - bert
  - portuguese
  - pt
  - ptbr

SPLADE (BERT-Tiny) finetuned on merged NQ + PT-BR instruction datasets

This model is a sparse retriever trained with Sentence Transformers' SPLADE stack, using a merged multilingual corpus (English + Portuguese).

Usage

from sentence_transformers import SparseEncoder

sparse_model = SparseEncoder("cnmoro/inference-free-splade-bert-tiny-en-ptbr")

sparse_embeddings = sparse_model.encode(["Hello", "World"], show_progress_bar=True)

Base model

• prajjwal1/bert-tiny

Training date

• Training completed on April 13, 2026.

Dataset composition

The training corpus was built by row-wise concatenation of:

1. sentence-transformers/natural-questions
2. cnmoro/GPT4-500k-Augmented-PTBR-Clean
3. cnmoro/WizardVicuna-PTBR-Instruct-Clean

Final merged size:

• Total rows: 869,365
• Train rows: 868,365
• Eval rows: 1,000
• Split seed: 12

Training objective

• Loss: SpladeLoss(SparseMultipleNegativesRankingLoss)
• Document regularizer weight: 0.03
• Query regularizer weight: 0

Core hyperparameters

• Epochs: 1
• Per-device batch size: 64
• Max sequence length: 256
• SPLADE pooling chunk size: 64
• Learning rate: 2e-5
• Warmup ratio: 0.1
• Mixed precision: fp16=True
• Batch sampler: NO_DUPLICATES
• Router mapping: query -> query, answer -> document

Final training metrics

• train_runtime: 2898.9331 s
• train_steps_per_second: 4.681
• train_samples_per_second: 299.546
• train_loss: 0.81497