PeytonT/metadata-embedding

Metadata Embedding

Builds dense embeddings over paper metadata fields such as title, abstract, authors, and categories.

Model Details

• Artifact type: LoRA adapter
• Base model: allenai/scibert_scivocab_uncased
• Backbone type: encoder
• Model ID: M1
• 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/metadata-embedding
• 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/m1_metadata_embedding.json
• Models directory: https://github.com/peytontolbert/research_library/tree/main/models

Intended Use

• Primary use: Builds dense embeddings over paper metadata fields such as title, abstract, authors, and categories.
• 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:

• Primary published dataset: PeytonT/1m_papers_text
• Source paper_text_parquet: full-text paper corpus records prepared for model training.

Training Procedure

• Sources: paper_text_parquet
• Input fields: title, abstract, categories, authors
• Target fields: metadata_embedding
• Train/val/test split: [0.8, 0.1, 0.1]
• Max samples: 0
• Batch size: 512
• Precision: bf16
• Objective: contrastive
• Learning rate: 0.0001
• Max source tokens: 256
• Max target tokens: 512
• Fine-tune strategy: peft_lora
• Max steps: -1
• Streaming dataset build: True

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
• Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack.

Usage

from transformers import AutoModel, AutoTokenizer
from peft import PeftModel

repo_id = "PeytonT/metadata-embedding"
base_id = "allenai/scibert_scivocab_uncased"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
base = AutoModel.from_pretrained(base_id)
model = PeftModel.from_pretrained(base, 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