🧠
Molecular Oncology Bert model by baptistescancar
⭐ 37.8
🧠
Model

Molecular Oncology Bert

by baptistescancar hf-model--baptistescancar--molecular-oncology-bert
Nexus Index
37.8 Top 10%
S: Semantic 50
A: Authority 0
P: Popularity 4
R: Recency 96
Q: Quality 65
Tech Context
0.11B Params
512 Ctx
Vital Performance
38 DL / 30D
0.0%
Source →
Audited 37.8 FNI Score
Tiny 0.11B Params
1k Context
38 Downloads
8G GPU ~2GB Est. VRAM
Dense BERTFORSEQUENCECLASSIFICATION Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID hf-model--baptistescancar--molecular-oncology-bert
License MIT
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__baptistescancar__molecular_oncology_bert,
  author = {baptistescancar},
  title = {Molecular Oncology Bert Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/baptistescancar/molecular-oncology-bert}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
baptistescancar. (2026). Molecular Oncology Bert [Model]. Free2AITools. https://huggingface.co/baptistescancar/molecular-oncology-bert

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run molecular-oncology-bert
🤗 HF Download
huggingface-cli download baptistescancar/molecular-oncology-bert
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
37.8
TOP 10% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 4
Recency (R) 96
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Molecular Oncology Bert: Semantic (S:50), Authority (A:0), Popularity (P:4), Recency (R:96), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
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🚀 What's Next?

📊

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Compare Benchmarks

See how this model ranks on standard tests

⚡

Deployment Guide

Understand deployment options

Technical Deep Dive

Model Card for Model ID

This model is intended to distinguish molecular oncology papers from other papers.

⚠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.
Top Tier

Social Proof

HuggingFace Hub
38Downloads
Hub Discussions
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

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

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--baptistescancar--molecular-oncology-bert
slug
baptistescancar--molecular-oncology-bert
source
huggingface
author
baptistescancar
license
MIT
tags
transformers, safetensors, bert, text-classification, en, base_model:google-bert/bert-base-uncased, base_model:finetune:google-bert/bert-base-uncased, license:mit, text-embeddings-inference, endpoints_compatible, region:us

⚙ī¸ Technical Specs

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

📊 Engagement & Metrics

downloads
38
stars
0
forks
0

Data indexed from public sources. Updated daily.