🧠
Roberta Bne Sentiment Analysis Es model by edumunozsala
⭐ 26.7
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Model

Roberta Bne Sentiment Analysis Es

by edumunozsala edumunozsala/roberta_bne_sentiment_analysis_es
Free2AITools Nexus Index
26.7
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 44
P: Popularity 6
R: Recency 10
Q: Quality 65
Tech Context
0.12B Params
512 Ctx
Vital Performance
69 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 26.7 FNI Score
Tiny 0.12B Params
1k Context
69 Downloads
8G GPU ~2GB Est. VRAM
Dense ROBERTAFORSEQUENCECLASSIFICATION Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID edumunozsala/roberta_bne_sentiment_analysis_es
License Apache-2.0
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{edumunozsala_roberta_bne_sentiment_analysis_es,
  author = {edumunozsala},
  title = {Roberta Bne Sentiment Analysis Es Model},
  year = {2022},
  howpublished = {\url{https://huggingface.co/edumunozsala/roberta_bne_sentiment_analysis_es}},
  note = {Accessed via Free2AITools.}
}
APA Style
edumunozsala. (2022). Roberta Bne Sentiment Analysis Es [Model]. Free2AITools. https://huggingface.co/edumunozsala/roberta_bne_sentiment_analysis_es

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run roberta_bne_sentiment_analysis_es
πŸ€— HF Download
huggingface-cli download edumunozsala/roberta_bne_sentiment_analysis_es
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) 44
Popularity (P) 6
Recency (R) 10
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Roberta Bne Sentiment Analysis Es: Authority (A:44), Popularity (P:6), Recency (R:10), 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.

Social Proof

HuggingFace Hub
69Downloads
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--edumunozsala--roberta_bne_sentiment_analysis_es
slug
edumunozsala--roberta_bne_sentiment_analysis_es
source
huggingface
author
edumunozsala
license
Apache-2.0
tags
transformers, pytorch, safetensors, roberta, text-classification, sagemaker, roberta-bne, textclassification, sentimentanalysis, es, dataset:imdbreviews_es, arxiv:2107.07253, license:apache-2.0, model-index, text-embeddings-inference, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
RobertaForSequenceClassification
params billions
0.12
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
69
stars
0
forks
0

Data indexed from public sources. Updated daily.