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anonymoussubmission111/
mpe-checkpoints

Name: mpe-checkpoints
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Checkpoints for "Improving Long-Context Retrieval with Multi-Prefix Embedding"

This repository contains model checkpoints and pre-computed embeddings for the anonymous submission Improving Long-Context Retrieval with Multi-Prefix Embedding.

Repository Structure

models/
  fixed-64-epoch1/          # Ablation: fixed 64-token prefix length
  maxp-train-epoch1/        # Baseline: MaxP trained model
  nochunk-epoch1/           # Baseline: single-vector (no chunking)
  prand-32to1024-epoch1/    # Proposed: random prefix lengths (32-1024 tokens)

encode/
  browsecomp-plus/          # Pre-computed embeddings - BrowseComp-Plus
  longembed/                # Pre-computed embeddings - LongEmbed (2WikiMQA,
  |                         #   NarrativeQA, QMSum, SummScreenFD)
  mldr-en/                  # Pre-computed embeddings - MLDR (English)

Each model folder contains a LoRA adapter (rank 16, alpha 64) fine-tuned from Qwen/Qwen3-Embedding-0.6B for feature extraction, along with tokenizer files and a checkpoint-625/ subfolder with the intermediate checkpoint at the end of epoch 1 (including optimizer state).

Usage

Load a LoRA adapter with PEFT:

python

from peft import PeftModel
from transformers import AutoModel

base = AutoModel.from_pretrained("Qwen/Qwen3-Embedding-0.6B")
model = PeftModel.from_pretrained(
    base,
    "anonymoussubmission111/mpe-checkpoints",
    subfolder="models/prand-32to1024-epoch1",
)

Pre-computed embeddings in encode/ are stored as .pkl files (pickled numpy arrays) and can be loaded directly to reproduce retrieval results without re-encoding.