Davlan/Xlm Roberta Base Ner Hrl
XLM-RoBERTa model for Named Entity Recognition in multiple languages.
Pricing
See website
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
—Overview
What is Davlan/Xlm Roberta Base Ner Hrl?
This XLM-RoBERTa-based model is designed for named entity recognition tasks across various languages, leveraging the transformers library. It's particularly useful for developers and data scientists working on multilingual text analysis projects.
Key differentiator
“This model stands out due to its multilingual capabilities and high accuracy in named entity recognition, making it a strong choice for developers working on international projects.”
Capability profile
Strength Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
Fit analysis
Who is it for?
✓ Best for
Projects requiring high accuracy in named entity recognition across multiple languages
Developers working on multilingual applications that need to extract specific entities from text
✕ Not a fit for
Real-time processing of large volumes of text data where latency is critical
Applications needing support for extremely rare or custom language variants not covered by the model's training
Cost structure
Pricing
Free Tier
None
Starts at
See website
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Next step
Get Started with Davlan/Xlm Roberta Base Ner Hrl
Step-by-step setup guide with code examples and common gotchas.