Davlan/Xlm Roberta Base Ner Hrl
XLM-RoBERTa model for Named Entity Recognition in multiple languages.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
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
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The model heavily relies on Python-specific libraries and patterns, which may be unfamiliar to developers with other primary languages.
While basic usage is covered, detailed explanations of the internal workings and advanced configuration options are sparse.
The model can be slow when processing very large volumes of text due to its resource-intensive nature.
Any breaking changes or deprecations in the Hugging Face transformers library could affect this model's functionality and require code adjustments.
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
Available
Open source — free to use
Starts at
$0
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.