A LoRA adapter for ibm-granite/granite-embedding-reranker-english-r2. It ranks text spans by their relevance to a query, encoding each (query, span) pair jointly and returning one logit. Higher logits indicate stronger evidence that the span should be kept.

This is a ranking model, not a standalone compressor. Use it with an allocator that separately preserves required content and enforces the token budget. Do not use its scores as a safety filter.

Intended use

Use the model to rank pre-segmented passages, log lines, code blocks, or other text spans before assembling a smaller context. Scores are most useful for ordering spans from the same document; they are not calibrated probabilities or a substitute for hard retention rules.

How to use

python

from pathlib import Path

import torch
from huggingface_hub import snapshot_download
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from peft import PeftModel

base_id = "ibm-granite/granite-embedding-reranker-english-r2"
repo_dir = Path(snapshot_download("OWNER/tokenfold-select"))  # replace with this repository ID
adapter_dir = repo_dir / "adapter"
tok = AutoTokenizer.from_pretrained(adapter_dir)
base = AutoModelForSequenceClassification.from_pretrained(base_id, dtype=torch.float32)
model = PeftModel.from_pretrained(base, adapter_dir).eval()

def score(query: str, spans: list[str]) -> list[float]:
    if not spans:
        return []
    enc = tok([query] * len(spans), spans, padding=True, truncation=True,
              max_length=8192, return_tensors="pt")
    with torch.no_grad():
        out = model(input_ids=enc["input_ids"], attention_mask=enc["attention_mask"])
        return out.logits.view(-1).float().tolist()

The repository contains a PEFT adapter, not the base-model weights. Loading therefore also downloads ibm-granite/granite-embedding-reranker-english-r2.

Training data

97,449 source/query fixtures spanning code, logs, diffs, JSON and tool calls, agentic tool use, and long-context QA. Sources include project-authored synthetic examples, SWE-bench Verified, publicly available tool-output benchmark samples, and samples derived from HotpotQA, NarrativeQA, SQuAD, TriviaQA, and MS MARCO. Each fixture pairs a source document, a query, a gold answer span, and optional required spans that the downstream allocator must preserve regardless of model score.

Training procedure

LoRA (r=8, alpha=16, dropout=0.05, target_modules="all-linear") via peft, applied for two epochs to ibm-granite/granite-embedding-reranker-english-r2 with BCEWithLogitsLoss and class-weighted positives. The released adapter was trained on the full corpus; the results below come from separately trained held-out evaluation runs.

Evaluation

Mean task success over three stratified repeated-subsampling runs (about 73,000 training and 24,000 held-out fixtures per run). Each run fine-tuned a fresh adapter, evaluated every method on the same held-out fixtures, and used the same required-span and token-budget allocator. Task success means that the literal gold-answer span survived compression.

target token ratio	this model	Kompress-v2 (native)	Kompress-v2 relevance scorer	BM25
0.5	0.863	0.665	0.805	0.794
0.25	0.703	0.472	0.615	0.607
0.1	0.399	0.304	0.377	0.377

At matched forced budgets, this model outperformed each baseline listed above at all three ratios.

Kompress-v2 and BM25 are named here as benchmark baselines the model is compared against, not as an influence on this model's design.

Limitations

Training labels are weak per-unit signals (does this span contain the gold answer?), not judged per-token labels — treat results as directional.
Task success measures literal answer-span retention, not downstream answer quality.
Fixtures are English-centric despite the multilingual base model.
Inputs longer than 8,192 tokens are truncated.
Logits are uncalibrated and should be used for ranking, not as probabilities.
The model cannot guarantee preservation of required or safety-critical text; the downstream allocator must enforce those guarantees.

License

The adapter is released under Apache 2.0. The base model is also Apache 2.0; source datasets remain subject to their own terms.

snchimata/tokenfold-select

tokenfold-select