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Roberta Base Japanese Aozora model by KoichiYasuoka
⭐ 24.1
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Model

Roberta Base Japanese Aozora

by KoichiYasuoka koichiyasuoka/roberta-base-japanese-aozora
Free2AITools Nexus Index
24.1
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 8
R: Recency 82
Q: Quality 50
Tech Context
512 Ctx
Vital Performance
90 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 24.1 FNI Score
Tiny - Params
1k Context
90 Downloads
Dense ROBERTAFORMASKEDLM Architecture
Restricted CC License
Model Information Summary
Entity Passport
Registry ID koichiyasuoka/roberta-base-japanese-aozora
License CC-BY-SA-4.0
Provider huggingface
πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{koichiyasuoka_roberta_base_japanese_aozora,
  author = {KoichiYasuoka},
  title = {Roberta Base Japanese Aozora Model},
  year = {2022},
  howpublished = {\url{https://huggingface.co/KoichiYasuoka/roberta-base-japanese-aozora}},
  note = {Accessed via Free2AITools.}
}
APA Style
KoichiYasuoka. (2022). Roberta Base Japanese Aozora [Model]. Free2AITools. https://huggingface.co/KoichiYasuoka/roberta-base-japanese-aozora

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ€— HF Download
huggingface-cli download koichiyasuoka/roberta-base-japanese-aozora
πŸ“¦ Install Lib
pip install -U transformers

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0
Popularity (P) 8
Recency (R) 82
Quality (Q) 50

πŸ’¬ Index Insight

FNI V2.0 for Roberta Base Japanese Aozora: Authority (A:0), Popularity (P:8), Recency (R:82), Quality (Q:50). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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Deployment reference

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⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source β†’

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.

Social Proof

HuggingFace Hub
90Downloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--koichiyasuoka--roberta-base-japanese-aozora
slug
koichiyasuoka--roberta-base-japanese-aozora
source
huggingface
author
KoichiYasuoka
license
CC-BY-SA-4.0
tags
transformers, pytorch, roberta, fill-mask, japanese, masked-lm, ja, license:cc-by-sa-4.0, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
RobertaForMaskedLM
context length
512
pipeline tag
fill-mask

πŸ“Š Engagement & Metrics

downloads
90
stars
0
forks
0

Data indexed from public sources. Updated daily.