Paper Link Prediction
Predicts paper-to-paper similarity or graph links.
Model Details
- Artifact type: full fine-tuned model
- Base model:
allenai/scibert_scivocab_uncased - Backbone type:
encoder - Model ID:
M4 - Tier:
T1_metadata - Role in stack: metadata-layer component in the paper understanding stack
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/paper-link-prediction - 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/m4_paper_link_prediction.json - Models directory:
https://github.com/peytontolbert/research_library/tree/main/models
Intended Use
- Primary use: Predicts paper-to-paper similarity or graph links.
- Downstream use: retrieval, ranking, planning, paper understanding, or cross-domain reasoning inside the broader Repository Library system, depending on the model family.
- 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_universe_graph: paper graph features derived from the Repository Library paper-universe graph.
Training Procedure
- Sources:
paper_universe_graph - Input fields:
paper_graph_node - Target fields:
linked_papers - Train/val/test split:
[0.9, 0.1, 0.0] - Max samples:
4000 - Batch size:
8 - Precision:
bf16 - Objective:
link_prediction - Learning rate:
5e-05 - Max source tokens:
512 - Max target tokens:
128 - Fine-tune strategy:
full_finetune - Max steps:
1000
Compute
- Hardware: 4x RTX_3090 (24 GB)
- Distributed strategy:
ddp - Estimated GPU hours in config:
0
Evaluation
- Declared metrics:
accuracy, macro_f1 - Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack.
Usage
from transformers import AutoModelForSequenceClassification, AutoTokenizer
repo_id = "PeytonT/paper-link-prediction"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)
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.
- 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