🧠
Bank Distil Bert 10k V2 model by BakkaNikki
⭐ 37.8
🧠
Model

Bank Distil Bert 10k V2

by BakkaNikki bakkanikki/bank_distil_bert_10k_v2
Free2AITools Nexus Index
37.8
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 2
R: Recency 72
Q: Quality 65
Tech Context
0.07B Params
512 Ctx
Vital Performance
15 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 37.8 FNI Score
Tiny 0.07B Params
1k Context
15 Downloads
8G GPU ~2GB Est. VRAM
Dense DISTILBERTFORSEQUENCECLASSIFICATION Architecture
Model Information Summary
Entity Passport
Registry ID bakkanikki/bank_distil_bert_10k_v2
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.4GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{bakkanikki_bank_distil_bert_10k_v2,
  author = {BakkaNikki},
  title = {Bank Distil Bert 10k V2 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/BakkaNikki/Bank_distil_bert_10K_v2}},
  note = {Accessed via Free2AITools.}
}
APA Style
BakkaNikki. (2026). Bank Distil Bert 10k V2 [Model]. Free2AITools. https://huggingface.co/BakkaNikki/Bank_distil_bert_10K_v2

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run bank_distil_bert_10k_v2
πŸ€— HF Download
huggingface-cli download bakkanikki/bank_distil_bert_10k_v2
πŸ“¦ Install Lib
pip install -U transformers

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0
Popularity (P) 2
Recency (R) 72
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Bank Distil Bert 10k V2: Authority (A:0), Popularity (P:2), Recency (R:72), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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Technical Deep Dive

⚠️ 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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πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ 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
15Downloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--bakkanikki--bank_distil_bert_10k_v2
slug
bakkanikki--bank_distil_bert_10k_v2
source
huggingface
author
BakkaNikki
tags
transformers, safetensors, distilbert, text-classification, arxiv:1910.09700, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
DistilBertForSequenceClassification
params billions
0.07
context length
512
pipeline tag
text-classification
vram gb
1.4
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
15
stars
0
forks
0

Data indexed from public sources. Updated daily.