Metadata Category Classifier

Classifies paper metadata from title and abstract text into arXiv-style category labels.

Model Details

Artifact type: full fine-tuned Transformers model
Base model: allenai/scibert_scivocab_uncased
Backbone type: encoder
Model ID: M2
Tier: T1_metadata
Role in stack: metadata and global science-graph 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/metadata-category-classifier
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/m2_metadata_category_classifier.json
Models directory: https://github.com/peytontolbert/research_library/tree/main/models

Intended Use

Primary use: Classifies paper title and abstract metadata into category labels for metadata indexing and filtering.
Downstream use: retrieval, ranking, routing, clustering, paper understanding, and global science-graph construction 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 arxiv_metadata: arXiv metadata records containing titles, abstracts, authors, categories, and update metadata.

The v2 hardening pass restricts the classifier target space to a denser category set:

max_labels: 32
min_per_label: 16
max_per_label: 128

Training Procedure

Sources: arxiv_metadata
Input fields: title, abstract
Target fields: categories
Filters: years [2000, 2025]
Train/val/test split: [0.9, 0.1, 0.0]
Max samples: 4000
Batch size: 8
Precision: bf16
Objective: cross_entropy
Learning rate: 5e-05
Max source tokens: 512
Max target tokens: 128
Fine-tune strategy: full_finetune
Number of labels: 32
Max steps: 1000

Compute

Hardware: 2x RTX_3090 (24 GB)
Distributed strategy: ddp
Estimated GPU hours in config: 0

Evaluation

Declared metrics: accuracy, macro_f1

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

eval_loss: 0.003253802889958024
eval_accuracy: 1.0
eval_macro_f1: 1.0
eval_balanced_accuracy: 1.0
eval_label_count: 31

Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack. The v2 scores are very high and should receive a leakage audit before being treated as a final benchmark.

Usage

from transformers import AutoModelForSequenceClassification, AutoTokenizer

repo_id = "PeytonT/metadata-category-classifier"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)

text = "TITLE: Example paper title
ABSTRACT: This paper studies representation learning for scientific documents."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
outputs = model(**inputs)
predicted_id = outputs.logits.argmax(dim=-1).item()
label = model.config.id2label.get(predicted_id, str(predicted_id))
print(label)

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.
The v2 classifier uses a hardened 32-label target space rather than the full arXiv category universe.
The current v2 eval result is perfect on the tracked split, which is useful as a pipeline check but should be audited for label leakage, split leakage, and class-distribution artifacts.
These models are components of a larger research system and should be validated in their target workflow before deployment.