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README.md

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+---
+license: apache-2.0
+base_model: ibm-granite/granite-embedding-reranker-english-r2
+library_name: peft
+tags:
+  - cross-encoder
+  - lora
+  - peft
+  - context-compression
+  - text-compression
+  - english
+  - relevance-scoring
+pipeline_tag: text-classification
+---
+
+# tokenfold-select
+
+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.

adapter/README.md

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+---
+base_model: ibm-granite/granite-embedding-reranker-english-r2
+library_name: peft
+tags:
+- base_model:adapter:ibm-granite/granite-embedding-reranker-english-r2
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.19.1

adapter/adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "ModernBertForSequenceClassification",
+    "parent_library": "transformers.models.modernbert.modeling_modernbert"
+  },
+  "base_model_name_or_path": "ibm-granite/granite-embedding-reranker-english-r2",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "lora_ga_config": null,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": [
+    "head",
+    "classifier"
+  ],
+  "peft_type": "LORA",
+  "peft_version": "0.19.1",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "dense",
+    "Wi",
+    "Wo",
+    "classifier",
+    "Wqkv"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_bdlora": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter/adapter_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2b407ee63caf3ad133b1bf351f45346dba9b8852c7fd899e7683246d574e16b9
+size 9145724

adapter/tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "is_local": false,
+  "mask_token": "[MASK]",
+  "max_length": 1124,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1124,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "tokenizer_class": "TokenizersBackend",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}