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Xlm Roberta Base Multilingual Ner model by Einstien
⭐ 39
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Model

Xlm Roberta Base Multilingual Ner

by Einstien einstien/xlm-roberta-base-multilingual-ner
Free2AITools Nexus Index
39.0
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 5
R: Recency 93
Q: Quality 65
Tech Context
0.28B Params
4.096K Ctx
Vital Performance
46 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 39 FNI Score
Tiny 0.28B Params
4k Context
46 Downloads
8G GPU ~2GB Est. VRAM
Dense XLMROBERTAFORTOKENCLASSIFICATION Architecture
Model Information Summary
Entity Passport
Registry ID einstien/xlm-roberta-base-multilingual-ner
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.5GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{einstien_xlm_roberta_base_multilingual_ner,
  author = {Einstien},
  title = {Xlm Roberta Base Multilingual Ner Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/Einstien/xlm-roberta-base-multilingual-ner}},
  note = {Accessed via Free2AITools.}
}
APA Style
Einstien. (2026). Xlm Roberta Base Multilingual Ner [Model]. Free2AITools. https://huggingface.co/Einstien/xlm-roberta-base-multilingual-ner

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run xlm-roberta-base-multilingual-ner
πŸ€— HF Download
huggingface-cli download einstien/xlm-roberta-base-multilingual-ner
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) 5
Recency (R) 93
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Xlm Roberta Base Multilingual Ner: Authority (A:0), Popularity (P:5), Recency (R:93), 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
46Downloads
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--einstien--xlm-roberta-base-multilingual-ner
slug
einstien--xlm-roberta-base-multilingual-ner
source
huggingface
author
Einstien
tags
transformers, safetensors, xlm-roberta, token-classification, arxiv:1910.09700, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
XLMRobertaForTokenClassification
params billions
0.28
context length
4,096
pipeline tag
token-classification
vram gb
1.5
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
46

Data indexed from public sources. Updated daily.