🧠
T5 Neutralisation model by carmengoar
⭐ 38.1
🧠
Model

T5 Neutralisation

by carmengoar hf-model--carmengoar--t5-neutralisation
Free2AITools Nexus Index
38.1 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 16
R: Recency 90
Q: Quality 45
Tech Context
0.06B Params
512 Ctx
Vital Performance
293 DL / 30D
0.0%
Source →
Audited 38.1 FNI Score
Tiny 0.06B Params
1k Context
293 Downloads
8G GPU ~2GB Est. VRAM
Dense T5FORCONDITIONALGENERATION Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID hf-model--carmengoar--t5-neutralisation
License Apache-2.0
Provider huggingface
💾

Compute Threshold

~1.3GB VRAM

Interactive
Analyze Hardware
â–ŧ

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__carmengoar__t5_neutralisation,
  author = {carmengoar},
  title = {T5 Neutralisation Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/carmengoar/t5-neutralisation}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
carmengoar. (2026). T5 Neutralisation [Model]. Free2AITools. https://huggingface.co/carmengoar/t5-neutralisation

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run t5-neutralisation
🤗 HF Download
huggingface-cli download carmengoar/t5-neutralisation
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Free2AITools Nexus Index V2.0

Methodology Index Protocol
Semantic (S) 50
Authority (A) 0
Popularity (P) 16
Recency (R) 90
Quality (Q) 45

đŸ’Ŧ Index Insight

FNI V2.0 for T5 Neutralisation: Semantic (S:50), Authority (A:0), Popularity (P:16), Recency (R:90), Quality (Q:45).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
---

🚀 What's Next?

📊

Find Training Datasets

Discover datasets compatible with this model

📈

Compare Benchmarks

See how this model ranks on standard tests

⚡

Deployment Guide

Understand deployment options

Technical Deep Dive

t5-neutralisation

This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0593
  • Bleu: 54.7416
  • Gen Len: 18.7292

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5.6e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 6

Training results

Training Loss Epoch Step Validation Loss Bleu Gen Len
No log 1.0 440 0.0837 53.9225 18.6042
0.0369 2.0 880 0.0739 54.1449 18.6354
0.034 3.0 1320 0.0690 54.4631 18.6562
0.0346 4.0 1760 0.0625 54.7416 18.7292
0.0423 5.0 2200 0.0599 54.7416 18.7292
0.0406 6.0 2640 0.0593 54.7416 18.7292

Framework versions

  • Transformers 4.51.2
  • Pytorch 2.10.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.21.4

⚠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
293Downloads
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--carmengoar--t5-neutralisation
slug
carmengoar--t5-neutralisation
source
huggingface
author
carmengoar
license
Apache-2.0
tags
transformers, safetensors, t5, text2text-generation, simplification, generated_from_trainer, base_model:google-t5/t5-small, base_model:finetune:google-t5/t5-small, license:apache-2.0, text-generation-inference, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
T5ForConditionalGeneration
params billions
0.06
context length
512
pipeline tag
vram gb
1.3
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
293
stars
0
forks
0

Data indexed from public sources. Updated daily.