🧠
Qa Completeness Regressor model by egcortes
⭐ 39.4
🧠
Model

Qa Completeness Regressor

by egcortes egcortes/qa-completeness-regressor
Free2AITools Nexus Index
39.4
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 6
R: Recency 89
Q: Quality 65
Tech Context
0.11B Params
512 Ctx
Vital Performance
68 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 39.4 FNI Score
Tiny 0.11B Params
1k Context
68 Downloads
8G GPU ~2GB Est. VRAM
Dense BERTCOMPLETENESSREGRESSOR Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID egcortes/qa-completeness-regressor
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{egcortes_qa_completeness_regressor,
  author = {egcortes},
  title = {Qa Completeness Regressor Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/egcortes/qa-completeness-regressor}},
  note = {Accessed via Free2AITools.}
}
APA Style
egcortes. (2026). Qa Completeness Regressor [Model]. Free2AITools. https://huggingface.co/egcortes/qa-completeness-regressor

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run qa-completeness-regressor
πŸ€— HF Download
huggingface-cli download egcortes/qa-completeness-regressor
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) 6
Recency (R) 89
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Qa Completeness Regressor: Authority (A:0), Popularity (P:6), Recency (R:89), 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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Deployment reference

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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
68Downloads
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--egcortes--qa-completeness-regressor
slug
egcortes--qa-completeness-regressor
source
huggingface
author
egcortes
license
MIT
tags
transformers, safetensors, bert, feature-extraction, answer-evaluation, evaluation-metrics, completeness, long-form-qa, question-answering, regression, text-classification, custom_code, en, dataset:egcortes/qa-completeness-relevance, base_model:google-bert/bert-base-uncased, base_model:finetune:google-bert/bert-base-uncased, license:mit, text-embeddings-inference, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
BertCompletenessRegressor
params billions
0.11
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
68

Data indexed from public sources. Updated daily.