🧠
Log Anomaly Detection Model Roberta model by Dumi2025
⭐ 37.1
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Model

Log Anomaly Detection Model Roberta

by Dumi2025 dumi2025/log-anomaly-detection-model-roberta
Free2AITools Nexus Index
37.1
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 44
P: Popularity 3
R: Recency 29
Q: Quality 65
Tech Context
0.12B Params
512 Ctx
Vital Performance
31 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 37.1 FNI Score
Tiny 0.12B Params
1k Context
31 Downloads
8G GPU ~2GB Est. VRAM
Dense ROBERTAFORSEQUENCECLASSIFICATION Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID dumi2025/log-anomaly-detection-model-roberta
License MIT
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.4GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{dumi2025_log_anomaly_detection_model_roberta,
  author = {Dumi2025},
  title = {Log Anomaly Detection Model Roberta Model},
  year = {2025},
  howpublished = {\url{https://huggingface.co/Dumi2025/log-anomaly-detection-model-roberta}},
  note = {Accessed via Free2AITools.}
}
APA Style
Dumi2025. (2025). Log Anomaly Detection Model Roberta [Model]. Free2AITools. https://huggingface.co/Dumi2025/log-anomaly-detection-model-roberta

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run log-anomaly-detection-model-roberta
πŸ€— HF Download
huggingface-cli download dumi2025/log-anomaly-detection-model-roberta
πŸ“¦ Install Lib
pip install -U transformers

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 44
Popularity (P) 3
Recency (R) 29
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Log Anomaly Detection Model Roberta: Authority (A:44), Popularity (P:3), Recency (R:29), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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Technical Deep Dive

⚠️ 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 β†’

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.

Social Proof

HuggingFace Hub
31Downloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--dumi2025--log-anomaly-detection-model-roberta
slug
dumi2025--log-anomaly-detection-model-roberta
source
huggingface
author
Dumi2025
license
MIT
tags
transformers, tensorboard, safetensors, roberta, text-classification, generated_from_trainer, base_model:facebookai/roberta-base, base_model:finetune:facebookai/roberta-base, license:mit, text-embeddings-inference, endpoints_compatible, region:us

βš™οΈ 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 β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
31

Data indexed from public sources. Updated daily.