🧠

Model

Bert Base Uncased Logic Lrtc

Name: Bert Base Uncased Logic Lrtc
Author: KingTechnician

by KingTechnician hf-model--kingtechnician--bert-base-uncased_logic_lrtc

Free2AITools Nexus Index

40.5 Top 100%

S: Semantic 50

A: Authority 0

P: Popularity 17

R: Recency 97

Q: Quality 65

Tech Context

0.11B Params

512 Ctx

Vital Performance

344 DL / 30D

0.0%

Source →

Audited 40.5 FNI Score

Tiny 0.11B Params

1k Context

344 Downloads

8G GPU ~2GB Est. VRAM

Dense BERTFORSEQUENCECLASSIFICATION Architecture

Commercial APACHE License

Model Information Summary
Entity Passport
Registry ID	hf-model--kingtechnician--bert-base-uncased_logic_lrtc
License	Apache-2.0
Provider	huggingface

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Compute Threshold

~1.4GB VRAM

Interactive

Analyze Hardware

Hardware Compatibility Test

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* Static estimation for 4-Bit Quantization.

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Cite this model

Academic & Research Attribution

BibTeX

@misc{hf_model__kingtechnician__bert_base_uncased_logic_lrtc,
  author = {KingTechnician},
  title = {Bert Base Uncased Logic Lrtc Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/KingTechnician/bert-base-uncased_LOGIC_LRTC}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

KingTechnician. (2026). Bert Base Uncased Logic Lrtc [Model]. Free2AITools. https://huggingface.co/KingTechnician/bert-base-uncased_LOGIC_LRTC

🔬Technical Deep Dive

Full Specifications [+]

Quick Commands

🦙 Ollama Run

ollama run bert-base-uncased_logic_lrtc

🤗 HF Download

huggingface-cli download kingtechnician/bert-base-uncased_logic_lrtc

📦 Install Lib

pip install -U transformers

⚖️ Free2AITools Nexus Index V2.0

Methodology Index Protocol

Semantic (S) 50

Authority (A) 0

Popularity (P) 17

Recency (R) 97

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Bert Base Uncased Logic Lrtc: Semantic (S:50), Authority (A:0), Popularity (P:17), Recency (R:97), 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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Deployment Guide

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Technical Deep Dive

bert-base-uncased_LOGIC_LRTC

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

Loss: 1.7740
Accuracy: 0.6633
Macro Precision: 0.6419
Macro F1: 0.6169

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: 2e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 12

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Macro Precision	Macro F1
No log	1.0	116	1.8896	0.3633	0.3357	0.2975
No log	2.0	232	1.4273	0.5033	0.5179	0.4789
No log	3.0	348	1.2158	0.5967	0.5967	0.5861
No log	4.0	464	1.1976	0.61	0.5779	0.5735
1.2558	5.0	580	1.2373	0.6267	0.5848	0.5809
1.2558	6.0	696	1.3141	0.6533	0.6306	0.6235
1.2558	7.0	812	1.4384	0.6733	0.6411	0.6248
1.2558	8.0	928	1.6433	0.6767	0.6660	0.6263
0.1405	9.0	1044	1.6696	0.67	0.6398	0.6177
0.1405	10.0	1160	1.7243	0.6633	0.6328	0.6152
0.1405	11.0	1276	1.7508	0.6633	0.6401	0.6205
0.1405	12.0	1392	1.7740	0.6633	0.6419	0.6169

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

⚠️ 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

• Benchmark scores may vary based on evaluation methodology and hardware configuration.
• VRAM requirements are estimates; actual usage depends on quantization and batch size.
• 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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344Downloads

Hub Discussions

🤗 Data Source: Hugging Face ↗

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

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: hf-model--kingtechnician--bert-base-uncased_logic_lrtc
slug: kingtechnician--bert-base-uncased_logic_lrtc
source: huggingface
author: KingTechnician
license: Apache-2.0
tags: transformers, safetensors, bert, text-classification, generated_from_trainer, base_model:google-bert/bert-base-uncased, base_model:finetune:google-bert/bert-base-uncased, license:apache-2.0, text-embeddings-inference, endpoints_compatible, region:us

⚙️ 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: 344
stars: 0
forks: 0

Data indexed from public sources. Updated daily.