---
base_model: allenai/scibert_scivocab_uncased
library_name: transformers
pipeline_tag: text-classification
tags:
- bug-localization
- code
- r4
- repositories
- repository-library
- research-library
- t4_repo
---

# Bug Localization

Ranks likely bug locations inside repository content.

## Model Details

- Artifact type: full fine-tuned model
- Base model: `allenai/scibert_scivocab_uncased`
- Model ID: `R4`
- Tier: `T4_repo`
- Local mirror: `/arxiv/models/repository_library/bug-localization`
- Source checkpoint: `models/checkpoints/R4`

This repository is part of the `repository_library` model stack and is mirrored from `/data/repository_library/models/checkpoints` for publication under the `PeytonT` namespace.

## Intended Use

- Primary use: Ranks likely bug locations inside repository content.
- Secondary use: retrieval, ranking, planning, or scientific paper tooling inside the broader Repository Library system, depending on the model family.
- Out of scope: production safety claims, benchmark claims beyond the bundled experiment config, or use outside the model's narrow training objective without task-specific validation.

## Training Data

This package was trained from the following declared datasets or corpus sources:

- `source:github_repos`

## Training Procedure

- Sources: `github_repos`
- Input fields: `repo_context, failure_description`
- Target fields: `bug_location`
- Max samples: `4000`
- Precision: `bf16`
- Objective: `cross_entropy`
- Batch size: `8`
- Learning rate: `5e-05`
- Max source tokens: `512`
- Max target tokens: `128`
- Max steps: `1000`

## Evaluation

- Declared metrics: `accuracy, macro_f1`
- Status: local experiment artifact mirrored for release; external benchmark reporting has not been standardized across the full model family yet.

## Usage

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

repo_id = "PeytonT/bug-localization"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)
```

## Limitations

- These model cards reflect the packaged experiment configs and mirrored checkpoint contents, not an independently audited benchmark sheet.
- Some training datasets are local corpora or exported shards, so reproducibility may require access to the surrounding Repository Library data pipeline.
- Models in this stack are narrow components of a larger paper-and-repository system and should be validated on downstream tasks before deployment.

## Project Context

Repository Library is a research system for indexing, retrieving, aligning, and reasoning over scientific papers, structured paper content, repositories, and cross-domain links between them.

## Contact

Published under `PeytonT` from the local `repository_library` build.