IT General Question Generation by mou3az

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  Adapter Base model:adapter:facebook/ba...   Base model:facebook/bart-base   Dataset:drop   Dataset:mou3az/it qa-qg   Dataset:squad v2   En   Finetuned   General purpose   It purpose   Lora   Peft   Region:us   Safetensors   Text2text generation

IT General Question Generation Benchmarks

nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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IT General Question Generation Parameters and Internals

Model Type 
Text2text Generation
Supported Languages 
en (English)
Training Details 
Data Sources:
squad_v2, drop, mou3az/IT_QA-QG
Methodology:
Fine-tuned using PEFT-LoRa
LLM NameIT General Question Generation
Repository ๐Ÿค—https://huggingface.co/mou3az/IT-General_Question-Generation 
Base Model(s)  Bart Base   facebook/bart-base
Required VRAM0 GB
Updated2025-06-09
Maintainermou3az
Model Files  0.0 GB
Supported Languagesen
Model ArchitectureAdapter
Licenseapache-2.0
Is Biasednone
PEFT TypeLORA
LoRA ModelYes
PEFT Target Modulesq_proj|v_proj
LoRA Alpha8
LoRA Dropout0.05
R Param18
IT General Question Generation (mou3az/IT-General_Question-Generation)

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Note: green Score (e.g. "73.2") means that the model is better than mou3az/IT-General_Question-Generation.

Rank the IT General Question Generation Capabilities

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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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124