🧠
Distilroberta Thunderbird Anomaly Baseline model by EgilKarlsen
⭐ 24.9
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Model

Distilroberta Thunderbird Anomaly Baseline

by EgilKarlsen egilkarlsen/distilroberta_thunderbird-anomaly_baseline
Free2AITools Nexus Index
24.9
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 33
P: Popularity 2
R: Recency 11
Q: Quality 50
Tech Context
512 Ctx
Vital Performance
17 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 24.9 FNI Score
Tiny - Params
1k Context
17 Downloads
Dense ROBERTAFORSEQUENCECLASSIFICATION Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID egilkarlsen/distilroberta_thunderbird-anomaly_baseline
License Apache-2.0
Provider huggingface
πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{egilkarlsen_distilroberta_thunderbird_anomaly_baseline,
  author = {EgilKarlsen},
  title = {Distilroberta Thunderbird Anomaly Baseline Model},
  year = {2023},
  howpublished = {\url{https://huggingface.co/EgilKarlsen/DistilRoBERTa_Thunderbird-Anomaly_Baseline}},
  note = {Accessed via Free2AITools.}
}
APA Style
EgilKarlsen. (2023). Distilroberta Thunderbird Anomaly Baseline [Model]. Free2AITools. https://huggingface.co/EgilKarlsen/DistilRoBERTa_Thunderbird-Anomaly_Baseline

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ€— HF Download
huggingface-cli download egilkarlsen/distilroberta_thunderbird-anomaly_baseline
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) 33
Popularity (P) 2
Recency (R) 11
Quality (Q) 50

πŸ’¬ Index Insight

FNI V2.0 for Distilroberta Thunderbird Anomaly Baseline: Authority (A:33), Popularity (P:2), Recency (R:11), Quality (Q:50). 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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⚠️ 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
17Downloads
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--egilkarlsen--distilroberta_thunderbird-anomaly_baseline
slug
egilkarlsen--distilroberta_thunderbird-anomaly_baseline
source
huggingface
author
EgilKarlsen
license
Apache-2.0
tags
transformers, pytorch, tensorboard, roberta, text-classification, generated_from_trainer, base_model:distilbert/distilroberta-base, base_model:finetune:distilbert/distilroberta-base, license:apache-2.0, text-embeddings-inference, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
RobertaForSequenceClassification
context length
512
pipeline tag
text-classification

πŸ“Š Engagement & Metrics

downloads
17
stars
0
forks
0

Data indexed from public sources. Updated daily.