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

PeytonT/research-library-c2-v2

Repo-Paper Alignment Model v2

Model Details