🧠
Korean Kws2 model by allzero0102
⭐ 38
🧠
Model

Korean Kws2

by allzero0102 hf-model--allzero0102--korean_kws2
Nexus Index
38.0 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 2
R: Recency 91
Q: Quality 65
Tech Context
Vital Performance
17 DL / 30D
0.0%
Source →
Audited 38 FNI Score
Tiny - Params
- Context
17 Downloads
Model Information Summary
Entity Passport
Registry ID hf-model--allzero0102--korean_kws2
Provider huggingface
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Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__allzero0102__korean_kws2,
  author = {allzero0102},
  title = {Korean Kws2 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/allzero0102/korean_kws2}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
allzero0102. (2026). Korean Kws2 [Model]. Free2AITools. https://huggingface.co/allzero0102/korean_kws2

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download allzero0102/korean_kws2
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
38.0
TOP 100% 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

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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: 1.0681
  • Accuracy: 1.0

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: 3e-05
  • 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 1.9243 0.2105
No log 2.0 4 1.9090 0.2105
No log 3.0 6 1.8979 0.2105
No log 4.0 8 1.8672 0.4211
No log 5.0 10 1.8672 0.4211
No log 6.0 12 1.8260 0.4211
No log 7.0 14 1.7997 0.3158
No log 8.0 16 1.7597 0.4211
No log 9.0 18 1.7091 0.5789
No log 10.0 20 1.6596 0.6316
No log 11.0 22 1.6187 0.6316
No log 12.0 24 1.5555 0.6316
No log 13.0 26 1.5052 0.7895
No log 14.0 28 1.4639 0.8421
No log 15.0 30 1.4112 0.8947
No log 16.0 32 1.3621 0.8421
No log 17.0 34 1.3069 1.0
No log 18.0 36 1.2652 1.0
No log 19.0 38 1.2375 1.0
No log 20.0 40 1.2167 0.9474
No log 21.0 42 1.1725 1.0
No log 22.0 44 1.1414 1.0
No log 23.0 46 1.1228 1.0
No log 24.0 48 1.1166 1.0
No log 25.0 50 1.0997 1.0
No log 26.0 52 1.0868 1.0
No log 27.0 54 1.0792 1.0
No log 28.0 56 1.0740 1.0
No log 29.0 58 1.0700 1.0
No log 30.0 60 1.0681 1.0

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.

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📝 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.

Social Proof

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

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🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--allzero0102--korean_kws2
slug
allzero0102--korean_kws2
source
huggingface
author
allzero0102
license
tags
transformers, safetensors, wav2vec2, audio-classification, generated_from_trainer, base_model:kkonjeong/wav2vec2-base-korean, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
audio-classification

📊 Engagement & Metrics

downloads
17
stars
0
forks
0

Data indexed from public sources. Updated daily.