Dslim/Distilbert NER
DistilBERT model for Named Entity Recognition
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
UnverifiedOverview
What is Dslim/Distilbert NER?
A lightweight DistilBERT model fine-tuned for Named Entity Recognition tasks, offering efficient and accurate entity extraction from text.
Key differentiator
“dslim/distilbert-NER offers an efficient and lightweight solution for Named Entity Recognition, making it ideal for applications with limited computational resources.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The model is primarily trained on English datasets, leading to subpar performance with non-English text.
DistilBERT, being a smaller variant of BERT, may not capture nuanced entities in specialized domains such as legal or medical texts.
While lightweight, the model can still consume significant computational resources when processing extensive volumes of text data.
Fit analysis
Who is it for?
✓ Best for
Projects requiring efficient and accurate Named Entity Recognition without heavy computational resources
Developers working on text analysis applications who need a lightweight yet powerful model
✕ Not a fit for
Applications that require real-time entity extraction from extremely large datasets
Scenarios where the use of pre-trained models is not acceptable due to specific domain requirements
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
Alternatives
Works well with
Integrations
Next step
Get Started with Dslim/Distilbert NER
Step-by-step setup guide with code examples and common gotchas.