🧠
Model

Biomedical Ner Pubmedbert

by Nikita2605 hf-model--nikita2605--biomedical-ner-pubmedbert
Free2AITools Nexus Index
39.0 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 3
R: Recency 96
Q: Quality 65
Tech Context
0.11B Params
512 Ctx
Vital Performance
28 DL / 30D
0.0%
Audited 39 FNI Score
Tiny 0.11B Params
1k Context
28 Downloads
8G GPU ~2GB Est. VRAM
Dense BERTFORTOKENCLASSIFICATION Architecture
Model Information Summary
Entity Passport
Registry ID hf-model--nikita2605--biomedical-ner-pubmedbert
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__nikita2605__biomedical_ner_pubmedbert,
  author = {Nikita2605},
  title = {Biomedical Ner Pubmedbert Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/Nikita2605/biomedical-ner-pubmedbert}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Nikita2605. (2026). Biomedical Ner Pubmedbert [Model]. Free2AITools. https://huggingface.co/Nikita2605/biomedical-ner-pubmedbert

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run biomedical-ner-pubmedbert
🤗 HF Download
huggingface-cli download nikita2605/biomedical-ner-pubmedbert
đŸ“Ļ Install Lib
pip install -U transformers

âš–ī¸ Free2AITools Nexus Index V2.0

Semantic (S) 50
Authority (A) 0
Popularity (P) 3
Recency (R) 96
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Biomedical Ner Pubmedbert: Semantic (S:50), Authority (A:0), Popularity (P:3), Recency (R:96), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

Unbiased Data Node Refresh: VFS Live
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🚀 What's Next?

Technical Deep Dive

âš 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
28Downloads
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseâ„šī¸ Verify with original source

đŸ›Ąī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--nikita2605--biomedical-ner-pubmedbert
slug
nikita2605--biomedical-ner-pubmedbert
source
huggingface
author
Nikita2605
license
tags
transformers, safetensors, bert, token-classification, arxiv:1910.09700, endpoints_compatible, region:us

âš™ī¸ Technical Specs

architecture
BertForTokenClassification
params billions
0.11
context length
512
pipeline tag
token-classification
vram gb
1.4
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
28
stars
0
forks
0

Data indexed from public sources. Updated daily.