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Distilhubert Finetuned Gtzan dataset by nic70
⭐ 25.6
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Dataset

Distilhubert Finetuned Gtzan

by nic70 hf-model--nic70--distilhubert-finetuned-gtzan
Nexus Index
25.6 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 100
Q: Quality 23
Tech Context
Vital Performance
0 DL / 30D
0.0%
Data Integrity 25.6 FNI Score
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- Rows
Parquet Format
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Dataset Information Summary
Entity Passport
Registry ID hf-model--nic70--distilhubert-finetuned-gtzan
Provider huggingface
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Cite this dataset

Academic & Research Attribution

BibTeX 
@misc{hf_model__nic70__distilhubert_finetuned_gtzan,
  author = {nic70},
  title = {Distilhubert Finetuned Gtzan Dataset},
  year = {2026},
  howpublished = {\url{https://free2aitools.com/dataset/hf-model--nic70--distilhubert-finetuned-gtzan}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
nic70. (2026). Distilhubert Finetuned Gtzan [Dataset]. Free2AITools. https://free2aitools.com/dataset/hf-model--nic70--distilhubert-finetuned-gtzan

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
25.6
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 100
Quality (Q) 23

đŸ’Ŧ Index Insight

FNI V2.0 for Distilhubert Finetuned Gtzan: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:100), Quality (Q:23).

Free2AITools Nexus Index

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Dataset Specification

distilhubert-finetuned-gtzan-7.5E-5rate

This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7037
  • Accuracy: 0.83

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: 7.500000000000001e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.6131 1.0 113 1.7407 0.43
1.0878 2.0 226 1.1306 0.68
0.7836 3.0 339 0.8427 0.77
0.5646 4.0 452 0.6842 0.8
0.2202 5.0 565 0.5216 0.84
0.1047 6.0 678 0.5698 0.82
0.0824 7.0 791 0.6976 0.83
0.1118 8.0 904 0.6875 0.81
0.1161 9.0 1017 0.6779 0.84
0.0855 10.0 1130 0.7037 0.83

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3
🤗 Data Source: Hugging Face ↗
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AI Summary: Based on Hugging Face metadata. Not a recommendation.

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Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--nic70--distilhubert-finetuned-gtzan
slug
nic70--distilhubert-finetuned-gtzan
source
huggingface
author
nic70
license
tags

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

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