---
library_name: transformers
base_model: sentence-transformers/all-MiniLM-L6-v2
tags:
- research-library
- repository-library
- repo-paper-alignment
- c2
- t5_cross
- feature-extraction
- v2
---

# Repo-Paper Alignment Model v2

Scores and embeds paper/repository pairs for mapping paper methods and descriptions to repository functions, files, or code spans.

## Model Details

- Artifact type: full fine-tuned Transformers model
- Base model: `sentence-transformers/all-MiniLM-L6-v2`
- Backbone type: encoder
- Model ID: `C2`
- Tier: `T5_cross`
- Role in stack: paper-code alignment and cross-domain fusion component
- Version: `v2`

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

## Model Sources

- Hugging Face repo: https://huggingface.co/PeytonT/research-library-c2-v2
- Hugging Face collection: https://huggingface.co/collections/PeytonT/research-library-6a49c589ef4d763f7539b50d
- GitHub repository: https://github.com/peytontolbert/research_library
- Experiment config: https://github.com/peytontolbert/research_library/blob/main/models/experiments/c2_repo_paper_alignment.json
- Models directory: https://github.com/peytontolbert/research_library/tree/main/models

## Intended Use

- Primary use: Scores paper/repository text pairs for cross-domain alignment between papers and implementation artifacts.
- Downstream use: reranking alignment candidates, retrieving repository evidence for papers, constructing paper-code links, planning repository-grounded explanations, and supporting cross-domain reasoning inside the broader Repository Library system.
- Out of scope: production safety claims, benchmark claims beyond the tracked experiment config, or deployment without task-specific validation.

## Training Data

The training inputs for this package were assembled from the following Repository Library data sources:

- Source `paper_repo_pairs`: paper/repository pair records from the Repository Library alignment pipeline.
- Alignment path: `exports/paper_repo_span_align.jsonl`

The v2 hardening pass applies:

- `dedupe`: true
- `balance_binary`: true
- Pair quality filters: `pairs_min_chars=64`, `pairs_max_chars=131072`

## Training Procedure

- Sources: `paper_repo_pairs`
- Input fields: paper text and repository text pair
- Target fields: binary alignment label
- Train/val/test split: `[0.9, 0.1, 0.0]`
- Max samples: 1000
- Eval split: 0.1
- Eval max samples: 256
- Batch size: 8
- Precision: `bf16`
- Objective: `contrastive`
- Contrastive loss: `auto`
- Learning rate: `5e-05`
- Max source tokens: 256
- Max target tokens: 256
- Fine-tune strategy: `full_finetune`
- Max steps: 1000
- Best checkpoint metric: `eval_roc_auc`

## Compute

- Hardware: 4x RTX_3090 (24 GB)
- Distributed strategy: `ddp`
- Estimated GPU hours in config: 0

## Evaluation

Declared metrics: `recall_at_10`, `ndcg_at_10`

Tracked v2 eval metrics on the current held-out split:

- `eval_loss`: `0.4323210120201111`
- `eval_pair_accuracy`: `0.4838709677419355`
- `eval_positive_score_mean`: `0.42978216651827095`
- `eval_negative_score_mean`: `0.3917496839421801`
- `eval_score_margin_mean`: `0.03803248257609082`
- `eval_roc_auc`: `0.5416666666666666`
- `eval_best_pair_accuracy`: `0.5967741935483871`
- `eval_best_threshold`: `0.2927139103412628`

Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack. The v2 checkpoint loads from the trainer-selected best checkpoint. Current quality is modest and indicates that further pair curation, stronger positives, and cleaner hard negatives are still needed.

## Usage

```python
import torch
from transformers import AutoModel, AutoTokenizer

repo_id = "PeytonT/repo-paper-alignment"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModel.from_pretrained(repo_id)
model.eval()

def mean_pool(last_hidden_state, attention_mask):
    mask = attention_mask.unsqueeze(-1).type_as(last_hidden_state)
    return (last_hidden_state * mask).sum(dim=1) / mask.sum(dim=1).clamp_min(1.0)

def encode(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
    return torch.nn.functional.normalize(mean_pool(outputs.last_hidden_state, inputs["attention_mask"]), dim=-1)

paper = "PAPER: We introduce a contrastive method for aligning scientific methods to implementation spans."
repo = "REPOSITORY: This module builds pairwise paper-code alignment examples and trains a contrastive encoder."
score = torch.nn.functional.cosine_similarity(encode(paper), encode(repo)).item()
print(score)
```

## Limitations

- These cards are generated from tracked experiment metadata and packaged artifacts, not from a separate benchmark report or external audit.
- Several training sources are pipeline outputs from the Repository Library codebase rather than standalone public datasets.
- Current v2 alignment quality is only modest (`eval_roc_auc` around `0.542`), so this checkpoint should be treated as a research component and not a final alignment benchmark.
- The model depends heavily on the quality of paper/repository pair construction and negative sampling.
- These models are components of a larger research system and should be validated in their target workflow before deployment.

## Project Context

- GitHub repository: https://github.com/peytontolbert/research_library
- Model collection: https://huggingface.co/collections/PeytonT/research-library-6a49c589ef4d763f7539b50d
- Publisher: PeytonT