🧠
Mobilebert Uncased Mnli model by typeform
⭐ 25.1
🧠
Model

Mobilebert Uncased Mnli

by typeform typeform/mobilebert-uncased-mnli
Free2AITools Nexus Index
25.1
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 46
P: Popularity 34
R: Recency 8
Q: Quality 65
Tech Context
0.02B Params
4.096K Ctx
Vital Performance
2.8K DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 25.1 FNI Score
Tiny 0.02B Params
4k Context
2.8K Downloads
8G GPU ~2GB Est. VRAM
Dense MOBILEBERTFORSEQUENCECLASSIFICATION Architecture
Model Information Summary
Entity Passport
Registry ID typeform/mobilebert-uncased-mnli
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.3GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{typeform_mobilebert_uncased_mnli,
  author = {typeform},
  title = {Mobilebert Uncased Mnli Model},
  year = {2022},
  howpublished = {\url{https://huggingface.co/typeform/mobilebert-uncased-mnli}},
  note = {Accessed via Free2AITools.}
}
APA Style
typeform. (2022). Mobilebert Uncased Mnli [Model]. Free2AITools. https://huggingface.co/typeform/mobilebert-uncased-mnli

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run mobilebert-uncased-mnli
πŸ€— HF Download
huggingface-cli download typeform/mobilebert-uncased-mnli
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) 46
Popularity (P) 34
Recency (R) 8
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Mobilebert Uncased Mnli: Authority (A:46), Popularity (P:34), Recency (R:8), 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.

View Original Source β†’

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
2.8KDownloads
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--typeform--mobilebert-uncased-mnli
slug
typeform--mobilebert-uncased-mnli
source
huggingface
author
typeform
tags
transformers, pytorch, safetensors, mobilebert, text-classification, zero-shot-classification, en, dataset:multi_nli, arxiv:1910.09700, endpoints_compatible, deploy:azure, region:us

βš™οΈ Technical Specs

architecture
MobileBertForSequenceClassification
params billions
0.02
context length
4,096
pipeline tag
zero-shot-classification
vram gb
1.3
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
2,835
stars
0
forks
0

Data indexed from public sources. Updated daily.