metadata
base_model: allenai/scibert_scivocab_uncased
library_name: peft
pipeline_tag: feature-extraction
tags:
- embeddings
- m1
- metadata-embedding
- repository-library
- research-library
- t1_metadata
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