xlm-roberta-large-ner-spanish
| Property | Value |
|---|---|
| Model Type | Named Entity Recognition |
| Base Architecture | XLM-RoBERTa Large |
| Training Dataset | CoNLL-2002 (Spanish) |
| Performance | 89.17 F1-score |
| Author | MMG |
| Model URL | Hugging Face |
What is xlm-roberta-large-ner-spanish?
xlm-roberta-large-ner-spanish is a state-of-the-art Named Entity Recognition model specifically optimized for Spanish language processing. Built upon the powerful XLM-RoBERTa large architecture, this model has been fine-tuned on the Spanish portion of the CoNLL-2002 dataset to achieve exceptional performance in identifying and classifying named entities in Spanish text.
Implementation Details
The model leverages the multilingual capabilities of XLM-RoBERTa large as its foundation and has been specifically optimized for Spanish NER tasks. Its architecture enables it to understand complex contextual relationships and identify named entities with high accuracy, as demonstrated by its impressive 89.17 F1-score on the CoNLL-2002 test dataset.
- Based on XLM-RoBERTa large architecture
- Fine-tuned specifically for Spanish NER
- Trained on CoNLL-2002 Spanish dataset
- Achieves state-of-the-art performance
Core Capabilities
- High-accuracy named entity recognition in Spanish text
- Robust performance across various Spanish text types
- Effective identification and classification of named entities
- State-of-the-art performance with 89.17 F1-score
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional performance in Spanish NER tasks, achieving one of the highest F1-scores (89.17) among available Spanish NER models. It combines the robust multilingual capabilities of XLM-RoBERTa large with specialized training for Spanish named entity recognition.
Q: What are the recommended use cases?
The model is ideal for applications requiring named entity recognition in Spanish text, such as information extraction, content analysis, automated document processing, and text analytics systems focused on Spanish language content.
