bert-base-multilingual-cased-ner-hrl
| Property | Value |
|---|---|
| Parameter Count | 177M |
| License | AFL-3.0 |
| Supported Languages | 10 (Arabic, German, English, Spanish, French, Italian, Latvian, Dutch, Portuguese, Chinese) |
| Downloads | 89,842 |
What is bert-base-multilingual-cased-ner-hrl?
This is a specialized Named Entity Recognition (NER) model built on the mBERT architecture, designed to identify three types of entities (Location, Organization, and Person) across 10 high-resource languages. The model demonstrates sophisticated entity detection capabilities through its ability to distinguish between the beginning and continuation of entities, making it particularly effective for processing complex text with consecutive named entities.
Implementation Details
The model is implemented using the Transformers library and can be easily integrated into NLP pipelines. It utilizes a fine-tuned mBERT base architecture and supports both PyTorch and TensorFlow frameworks.
- Trained on diverse datasets including ANERcorp, CoNLL 2002/2003, Europeana Newspapers, and others
- Supports token classification with 7 distinct classes (O, B-PER, I-PER, B-ORG, I-ORG, B-LOC, I-LOC)
- Trained on NVIDIA V100 GPU with optimized hyperparameters
Core Capabilities
- Multilingual NER processing across 10 major languages
- Precise entity boundary detection with B/I tagging scheme
- Robust performance on news and formal text domains
- Efficient processing with 177M parameters
Frequently Asked Questions
Q: What makes this model unique?
The model's key strength lies in its multilingual capabilities combined with sophisticated entity boundary detection, making it particularly valuable for cross-lingual NER tasks in high-resource languages.
Q: What are the recommended use cases?
This model is ideal for news article analysis, multilingual document processing, and general named entity extraction in formal text. However, it may have limitations when applied to domain-specific content or informal text types.
