🧠
Nas Bilingue model by dtorber
⭐ 24.5
🧠
Model

Nas Bilingue

by dtorber hf-model--dtorber--nas-bilingue
Nexus Index
24.5 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 2
R: Recency 12
Q: Quality 50
Tech Context
Vital Performance
14 DL / 30D
0.0%
Source →
Audited 24.5 FNI Score
Tiny - Params
- Context
14 Downloads
Model Information Summary
Entity Passport
Registry ID hf-model--dtorber--nas-bilingue
Provider huggingface
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Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__dtorber__nas_bilingue,
  author = {dtorber},
  title = {Nas Bilingue Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/dtorber/nas-bilingue}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
dtorber. (2026). Nas Bilingue [Model]. Free2AITools. https://huggingface.co/dtorber/nas-bilingue

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download dtorber/nas-bilingue
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
24.5
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 2
Recency (R) 12
Quality (Q) 50

đŸ’Ŧ Index Insight

FNI V2.0 for Nas Bilingue: Semantic (S:50), Authority (A:0), Popularity (P:2), Recency (R:12), Quality (Q:50).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
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Deployment Guide

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Technical Deep Dive

NAS-bilingue

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 3.7187
  • Rougelsum: 0.0922

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: 1.3739167643078955e-06
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rougelsum
No log 1.0 5 4.5936 0.0759
No log 2.0 10 4.4276 0.0759
No log 3.0 15 4.2936 0.0759
No log 4.0 20 4.1820 0.0759
No log 5.0 25 4.0896 0.0881
No log 6.0 30 4.0121 0.0970
No log 7.0 35 3.9451 0.0918
No log 8.0 40 3.8875 0.0922
No log 9.0 45 3.8395 0.0922
No log 10.0 50 3.8011 0.0922
No log 11.0 55 3.7707 0.0922
No log 12.0 60 3.7480 0.0922
No log 13.0 65 3.7320 0.0922
No log 14.0 70 3.7223 0.0922
No log 15.0 75 3.7187 0.0922

Framework versions

  • Transformers 4.26.1
  • Pytorch 1.13.1+cu117
  • Datasets 2.9.0
  • Tokenizers 0.13.2

⚠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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14Downloads
Hub Discussions
🤗 Data Source: Hugging Face ↗
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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--dtorber--nas-bilingue
slug
dtorber--nas-bilingue
source
huggingface
author
dtorber
license
tags
transformers, pytorch, bart, text2text-generation, summarization, generated_from_trainer, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
summarization

📊 Engagement & Metrics

downloads
14
stars
0
forks
0

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