🧠
Ai Content Detector Roberta V1 model by adhwiraj
⭐ 39.5
🧠
Model

Ai Content Detector Roberta V1

by adhwiraj adhwiraj/ai-content-detector-roberta-v1
Free2AITools Nexus Index
39.5
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 4
R: Recency 88
Q: Quality 65
Tech Context
0.12B Params
512 Ctx
Vital Performance
39 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 39.5 FNI Score
Tiny 0.12B Params
1k Context
39 Downloads
8G GPU ~2GB Est. VRAM
Dense ROBERTAFORSEQUENCECLASSIFICATION Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID adhwiraj/ai-content-detector-roberta-v1
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{adhwiraj_ai_content_detector_roberta_v1,
  author = {adhwiraj},
  title = {Ai Content Detector Roberta V1 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/adhwiraj/ai-content-detector-roberta-v1}},
  note = {Accessed via Free2AITools.}
}
APA Style
adhwiraj. (2026). Ai Content Detector Roberta V1 [Model]. Free2AITools. https://huggingface.co/adhwiraj/ai-content-detector-roberta-v1

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run ai-content-detector-roberta-v1
πŸ€— HF Download
huggingface-cli download adhwiraj/ai-content-detector-roberta-v1
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) 0
Popularity (P) 4
Recency (R) 88
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Ai Content Detector Roberta V1: Authority (A:0), Popularity (P:4), Recency (R:88), 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.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub
39Downloads
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--adhwiraj--ai-content-detector-roberta-v1
slug
adhwiraj--ai-content-detector-roberta-v1
source
huggingface
author
adhwiraj
license
MIT
tags
transformers, safetensors, roberta, text-classification, ai-detection, fine-tuned, ai-content-detector, en, base_model:facebookai/roberta-base, base_model:finetune:facebookai/roberta-base, license:mit, model-index, 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
39
stars
null
forks
null

Data indexed from public sources. Updated daily.