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inference-free-splade-co-condenser-en-ptbr – AI Model by cnmoro | AlphaNeural AI

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

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sentence-transformers

sentence-similarity

cnmoro/GPT4-500k-Augmented-PTBR-Clean

cnmoro/WizardVicuna-PTBR-Instruct-Clean

sentence-transformers/natural-questions

Luyu/co-condenser-marco

endpoints_compatible

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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

python

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:

sentence-transformers/natural-questions
cnmoro/GPT4-500k-Augmented-PTBR-Clean
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