🧠
Scenescope Roberta V3 model by RedMinder56
⭐ 37.4
🧠
Model

Scenescope Roberta V3

by RedMinder56 hf-model--redminder56--scenescope_roberta_v3
Free2AITools Nexus Index
37.4 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 1
R: Recency 96
Q: Quality 65
Tech Context
0.12B Params
512 Ctx
Vital Performance
12 DL / 30D
0.0%
Source →
Audited 37.4 FNI Score
Tiny 0.12B Params
1k Context
12 Downloads
8G GPU ~2GB Est. VRAM
Dense ROBERTAFORSEQUENCECLASSIFICATION Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID hf-model--redminder56--scenescope_roberta_v3
License MIT
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__redminder56__scenescope_roberta_v3,
  author = {RedMinder56},
  title = {Scenescope Roberta V3 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/RedMinder56/scenescope_roberta_v3}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
RedMinder56. (2026). Scenescope Roberta V3 [Model]. Free2AITools. https://huggingface.co/RedMinder56/scenescope_roberta_v3

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run scenescope_roberta_v3
🤗 HF Download
huggingface-cli download redminder56/scenescope_roberta_v3
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Free2AITools Nexus Index V2.0

Methodology Index Protocol
Semantic (S) 50
Authority (A) 0
Popularity (P) 1
Recency (R) 96
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Scenescope Roberta V3: Semantic (S:50), Authority (A:0), Popularity (P:1), Recency (R:96), 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

scenescope_roberta_v3

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

  • Loss: 1.1025
  • Accuracy: 0.5155
  • F1 Macro: 0.5164
  • F1 Weighted: 0.5162

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • 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: 4

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Macro F1 Weighted
1.3835 1.0 76 1.3524 0.4109 0.3679 0.3682
1.2281 2.0 152 1.1090 0.5155 0.5188 0.5185
1.0200 3.0 228 1.0982 0.5504 0.5019 0.5008
0.8586 4.0 304 1.0808 0.5233 0.5123 0.5115

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.

Social Proof

HuggingFace Hub
12Downloads
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--redminder56--scenescope_roberta_v3
slug
redminder56--scenescope_roberta_v3
source
huggingface
author
RedMinder56
license
MIT
tags
transformers, safetensors, roberta, text-classification, generated_from_trainer, base_model:facebookai/roberta-base, base_model:finetune:facebookai/roberta-base, license:mit, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
RobertaForSequenceClassification
params billions
0.12
context length
512
pipeline tag
text-classification
vram gb
1.4
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
12
stars
0
forks
0

Data indexed from public sources. Updated daily.