cnmoro/inference-free-splade-co-condenser-en-ptbr

# SPLADE (co-condenser-marco) 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-co-condenser-en-ptbr")

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

Base model

• —Luyu/co-condenser-marco

Training date

• —Training completed on April 13, 2026.

Dataset composition

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

1. 1.sentence-transformers/natural-questions
2. 2.cnmoro/GPT4-500k-Augmented-PTBR-Clean
3. 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: 3
• —Per-device batch size: 32
• —Max sequence length: 128
• —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: 5756.8386 s
• —train_steps_per_second: 4.714
• —train_samples_per_second: 150.841
• —train_loss: 0.30475