🧠
T5 Small Korean Summarization model by eenzeenee
⭐ 29.1
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Model

T5 Small Korean Summarization

by eenzeenee eenzeenee/t5-small-korean-summarization
Free2AITools Nexus Index
29.1
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 39
P: Popularity 22
R: Recency 9
Q: Quality 65
Tech Context
0.1B Params
512 Ctx
Vital Performance
601 DL / 30D
Source β†’

Task categories from upstream metadata

πŸ“Content Summary

Technical Constraints

Experimental / High Latency
Low FNI signal 29.1 FNI Score
Tiny 0.1B Params
1k Context
601 Downloads
8G GPU ~2GB Est. VRAM
Dense T5FORCONDITIONALGENERATION Architecture
Model Information Summary
Entity Passport
Registry ID eenzeenee/t5-small-korean-summarization
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.4GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{eenzeenee_t5_small_korean_summarization,
  author = {eenzeenee},
  title = {T5 Small Korean Summarization Model},
  year = {2023},
  howpublished = {\url{https://huggingface.co/eenzeenee/t5-small-korean-summarization}},
  note = {Accessed via Free2AITools.}
}
APA Style
eenzeenee. (2023). T5 Small Korean Summarization [Model]. Free2AITools. https://huggingface.co/eenzeenee/t5-small-korean-summarization

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run t5-small-korean-summarization
πŸ€— HF Download
huggingface-cli download eenzeenee/t5-small-korean-summarization
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) 39
Popularity (P) 22
Recency (R) 9
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for T5 Small Korean Summarization: Authority (A:39), Popularity (P:22), Recency (R:9), Quality (Q:65). 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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Technical Deep Dive

⚠️ 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.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub
601Downloads
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--eenzeenee--t5-small-korean-summarization
slug
eenzeenee--t5-small-korean-summarization
source
huggingface
author
eenzeenee
tags
transformers, pytorch, safetensors, t5, text2text-generation, summarization, ko, text-generation-inference, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
T5ForConditionalGeneration
params billions
0.1
context length
512
pipeline tag
summarization
vram gb
1.4
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
601
stars
0
forks
0

Data indexed from public sources. Updated daily.