🧠
ClinicalTrialBioBert model by domenicrosati
⭐ 24.2
🧠
Model

ClinicalTrialBioBert

by domenicrosati hf-model--domenicrosati--clinicaltrialbiobert
Nexus Index
24.2 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 3
R: Recency 9
Q: Quality 50
Tech Context
Vital Performance
24 DL / 30D
0.0%
Source →
Audited 24.2 FNI Score
Tiny - Params
- Context
24 Downloads
Model Information Summary
Entity Passport
Registry ID hf-model--domenicrosati--clinicaltrialbiobert
Provider huggingface
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Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__domenicrosati__clinicaltrialbiobert,
  author = {domenicrosati},
  title = {ClinicalTrialBioBert Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/domenicrosati/clinicaltrialbiobert}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
domenicrosati. (2026). ClinicalTrialBioBert [Model]. Free2AITools. https://huggingface.co/domenicrosati/clinicaltrialbiobert

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download domenicrosati/clinicaltrialbiobert
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
24.2
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 3
Recency (R) 9
Quality (Q) 50

đŸ’Ŧ Index Insight

FNI V2.0 for ClinicalTrialBioBert: Semantic (S:50), Authority (A:0), Popularity (P:3), Recency (R:9), Quality (Q:50).

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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Deployment Guide

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Technical Deep Dive

ClinicalTrialBioBERT

This model is a fine-tuned version of dmis-lab/biobert-v1.1 on Clinical Trial Texts Dataset.

Model description

A Clinical Trial Language Model.

Intended uses & limitations

Use when you need domain knowledge from the clinical trial domain.

Training and evaluation data

Trained on 500k steps of Clinical Trial Texts Dataset

Perplexity of BioBERT: Perplexity of ClinicalTrialBioBERT:

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 128
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • training_steps: 500000
  • mixed_precision_training: Native AMP

Training results

10k step training loss: 0.92 500k step training loss: 0.50

Framework versions

  • Transformers 4.20.1
  • Pytorch 1.11.0
  • Datasets 2.7.1
  • Tokenizers 0.12.1

⚠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.

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📝 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

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24Downloads
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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--domenicrosati--clinicaltrialbiobert
slug
domenicrosati--clinicaltrialbiobert
source
huggingface
author
domenicrosati
license
tags
transformers, pytorch, tensorboard, bert, fill-mask, generated_from_trainer, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
fill-mask

📊 Engagement & Metrics

downloads
24
stars
0
forks
0

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