metadata
base_model: google/flan-t5-base
library_name: peft
pipeline_tag: text2text-generation
tags:
- p0
- pdf-tokenization
- repository-library
- research-library
- t3_pdf
PDF Tokenization
Normalizes and models structured PDF-derived paper content for downstream paper tasks.
Model Details
- Artifact type: LoRA adapter
- Base model:
google/flan-t5-base - Backbone type:
encoder_decoder - Model ID:
P0 - Tier:
T3_pdf - Role in stack: full-paper or structured-PDF component
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/pdf-tokenization - 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/p0_pdf_tokenization.json - Models directory:
https://github.com/peytontolbert/research_library/tree/main/models
Intended Use
- Primary use: Normalizes and models structured PDF-derived paper content for downstream paper tasks.
- 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
arxiv_pdfs: raw arXiv PDF documents routed through the Repository Library paper pipeline.
Training Procedure
- Sources:
arxiv_pdfs - Input fields:
raw_pdf - Target fields:
structured_tokens - Train/val/test split:
[0.9, 0.1, 0.0] - Max samples:
4000 - Batch size:
2 - Precision:
bf16 - Objective:
cross_entropy - Learning rate:
5e-05 - Max source tokens:
512 - Max target tokens:
256 - Fine-tune strategy:
peft_lora - Max steps:
1000
Compute
- Hardware: 4x RTX_3090 (24 GB)
- Distributed strategy:
ddp - Estimated GPU hours in config:
0
Evaluation
- Declared metrics:
perplexity - Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack.
Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from peft import PeftModel
repo_id = "PeytonT/pdf-tokenization"
base_id = "google/flan-t5-base"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
base = AutoModelForSeq2SeqLM.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