🧠
Korean Kws2 model by ryulee2ppi
⭐ 38
🧠
Model

Korean Kws2

by ryulee2ppi hf-model--ryulee2ppi--korean_kws2
Nexus Index
38.0 Top 10%
S: Semantic 50
A: Authority 0
P: Popularity 2
R: Recency 91
Q: Quality 65
Tech Context
0.09B Params
4.096K Ctx
Vital Performance
15 DL / 30D
0.0%
Source →
Audited 38 FNI Score
Tiny 0.09B Params
4k Context
15 Downloads
8G GPU ~2GB Est. VRAM
Dense WAV2VEC2FORSEQUENCECLASSIFICATION Architecture
Model Information Summary
Entity Passport
Registry ID hf-model--ryulee2ppi--korean_kws2
Provider huggingface
💾

Compute Threshold

~1.4GB VRAM

Interactive
Analyze Hardware
â–ŧ

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__ryulee2ppi__korean_kws2,
  author = {ryulee2ppi},
  title = {Korean Kws2 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/ryulee2ppi/korean_kws2}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
ryulee2ppi. (2026). Korean Kws2 [Model]. Free2AITools. https://huggingface.co/ryulee2ppi/korean_kws2

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run korean_kws2
🤗 HF Download
huggingface-cli download ryulee2ppi/korean_kws2
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
38.0
TOP 10% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 2
Recency (R) 91
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Korean Kws2: Semantic (S:50), Authority (A:0), Popularity (P:2), Recency (R:91), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
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🚀 What's Next?

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Deployment Guide

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Technical Deep Dive

korean_kws2

This model is a fine-tuned version of Kkonjeong/wav2vec2-base-korean on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5138
  • Accuracy: 0.9474

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 2 1.9103 0.4737
No log 2.0 4 1.8747 0.2632
No log 3.0 6 1.8417 0.2632
No log 4.0 8 1.7328 0.4211
No log 5.0 10 1.5862 0.6316
No log 6.0 12 1.5029 0.6316
No log 7.0 14 1.3662 0.7368
No log 8.0 16 1.3309 0.7368
No log 9.0 18 1.2793 0.7895
No log 10.0 20 1.2108 0.7895
No log 11.0 22 1.1893 0.8947
No log 12.0 24 1.0773 0.9474
No log 13.0 26 1.0089 0.9474
No log 14.0 28 0.9668 0.8947
No log 15.0 30 0.8962 0.9474
No log 16.0 32 0.8234 0.9474
No log 17.0 34 0.7940 0.9474
No log 18.0 36 0.7593 0.9474
No log 19.0 38 0.7313 0.9474
No log 20.0 40 0.6875 0.9474
No log 21.0 42 0.6449 0.9474
No log 22.0 44 0.6118 0.9474
No log 23.0 46 0.5965 0.9474
No log 24.0 48 0.5809 0.9474
No log 25.0 50 0.5674 0.9474
No log 26.0 52 0.5497 0.9474
No log 27.0 54 0.5331 0.9474
No log 28.0 56 0.5219 0.9474
No log 29.0 58 0.5158 0.9474
No log 30.0 60 0.5138 0.9474

Framework versions

  • Transformers 5.0.0
  • Pytorch 2.10.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.2

⚠ī¸ 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 →

📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.
Top Tier

Social Proof

HuggingFace Hub
15Downloads
Hub Discussions
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--ryulee2ppi--korean_kws2
slug
ryulee2ppi--korean_kws2
source
huggingface
author
ryulee2ppi
license
tags
transformers, safetensors, wav2vec2, audio-classification, generated_from_trainer, base_model:kkonjeong/wav2vec2-base-korean, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
Wav2Vec2ForSequenceClassification
params billions
0.09
context length
4,096
pipeline tag
audio-classification
vram gb
1.4
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
15
stars
0
forks
0

Data indexed from public sources. Updated daily.