🧠
Xlmr Enis Finetuned Ner model by eliasbe
⭐ 23.1
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Model

Xlmr Enis Finetuned Ner

by eliasbe eliasbe/xlmr-enis-finetuned-ner
Free2AITools Nexus Index
23.1
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 2
R: Recency 3
Q: Quality 50
Tech Context
512 Ctx
Vital Performance
17 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 23.1 FNI Score
Tiny - Params
1k Context
17 Downloads
Dense ROBERTAFORTOKENCLASSIFICATION Architecture
Restricted AGPL License
Model Information Summary
Entity Passport
Registry ID eliasbe/xlmr-enis-finetuned-ner
License agpl-3.0
Provider huggingface
πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{eliasbe_xlmr_enis_finetuned_ner,
  author = {eliasbe},
  title = {Xlmr Enis Finetuned Ner Model},
  year = {2022},
  howpublished = {\url{https://huggingface.co/eliasbe/XLMR-ENIS-finetuned-ner}},
  note = {Accessed via Free2AITools.}
}
APA Style
eliasbe. (2022). Xlmr Enis Finetuned Ner [Model]. Free2AITools. https://huggingface.co/eliasbe/XLMR-ENIS-finetuned-ner

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ€— HF Download
huggingface-cli download eliasbe/xlmr-enis-finetuned-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) 2
Recency (R) 3
Quality (Q) 50

πŸ’¬ Index Insight

FNI V2.0 for Xlmr Enis Finetuned Ner: Authority (A:0), Popularity (P:2), Recency (R:3), Quality (Q:50). 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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⚠️ 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.

Social Proof

HuggingFace Hub
17Downloads
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--eliasbe--xlmr-enis-finetuned-ner
slug
eliasbe--xlmr-enis-finetuned-ner
source
huggingface
author
eliasbe
license
agpl-3.0
tags
transformers, pytorch, tensorboard, roberta, token-classification, generated_from_trainer, dataset:mim_gold_ner, license:agpl-3.0, model-index, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
RobertaForTokenClassification
context length
512
pipeline tag
token-classification

πŸ“Š Engagement & Metrics

downloads
17

Data indexed from public sources. Updated daily.