Cdn13/splade-multi-static-doc

SPLADEX — best_proxy checkpoint

This is the best_proxy checkpoint from an inference-free SPLADE-v3-doc training run on a combined BEIR-style documentation dataset (NumPy, Pandas, Pybind11, etc.).

Model description

• Architecture: SPLADE-v3-doc (MLM head, log1p(ReLU(logits)).max() document pooling)
• Query encoder: Inference-free — static per-token weights (static_query_weights.pt) initialized from IDF and learned during training.
• Document encoder: Full SPLADE document side.
• Selection criterion: Best window-average selection proxy during training (top-1 ranking score − sparsity budget penalty).

Files

File	Description
config.json	Model config (HF format)
model.safetensors / pytorch_model.bin	Document-encoder weights
tokenizer*	Tokenizer files
static_query_weights.pt	Learned static query token weights
trainer_state.pt	Optimizer / scheduler state + training metrics at best step

Usage

import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM

repo = "Cdn13/splade-multi-static-doc"
tokenizer = AutoTokenizer.from_pretrained(repo)
model     = AutoModelForMaskedLM.from_pretrained(repo)

# Load static query weights
sqw = torch.load("static_query_weights.pt", map_location="cpu")
query_weights = sqw["query_weights"]  # shape: [vocab_size]

Note: The query representation is presence(token) * query_weights[token], computed without any forward pass through the model.