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OCRonos Vintage by PleIAs

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Model Card on HF 🤗: https://huggingface.co/PleIAs/OCRonos-Vintage 

OCRonos Vintage Benchmarks

LLME Score: 0.21579
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
OCRonos Vintage (PleIAs/OCRonos-Vintage)

OCRonos Vintage Parameters and Internals

Model Type 
text-generation, OCR correction
Use Cases 
Areas:
cultural heritage archives
Applications:
OCR correction
Primary Use Cases:
Historical text correction
Limitations:
Not reliable for modern concept correction
Considerations:
Performs well for documents published between the mid-19th century and mid-20th century
Additional Notes 
Can simulate historical text generation. Historical pre-training limits exposure to modern content.
Supported Languages 
en (comparable to GPT-4 or llama for English-speaking archives)
Training Details 
Data Sources:
Library of Congress, Internet Archive, Hathi Trust
Data Volume:
18 billion tokens
Methodology:
Pre-trained with llm.c
Context Length:
1024
Training Time:
2.5 hours
Hardware Used:
4 H100s
Model Architecture:
GPT-2 tokenizer, future custom tokenizer for better performance
Input Output 
Input Format:
### Text ###
Accepted Modalities:
text
Output Format:
### Correction ###
LLM NameOCRonos Vintage
Repository 🤗https://huggingface.co/PleIAs/OCRonos-Vintage 
Model Size124.4m
Required VRAM0.2 GB
Updated2025-05-03
MaintainerPleIAs
Model Typegpt2
Model Files  0.2 GB
Supported Languagesen
Model ArchitectureGPT2LMHeadModel
Licenseapache-2.0
Model Max Length1024
Transformers Version4.43.3
Tokenizer ClassGPT2Tokenizer
Vocabulary Size50257
Torch Data Typebfloat16
Activation Functiongelu_new
Errorsreplace

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Note: green Score (e.g. "73.2") means that the model is better than PleIAs/OCRonos-Vintage.

Rank the OCRonos Vintage Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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