XLM-RoBERTa Large (CoNLL03 English)
| Property | Value |
|---|---|
| Parameter Count | 560M |
| Model Type | Token Classification |
| Languages Supported | 94 languages |
| Paper | Unsupervised Cross-lingual Representation Learning at Scale |
| Training Data | CoNLL-2003 Dataset (English) |
What is xlm-roberta-large-finetuned-conll03-english?
This is a powerful multilingual language model based on Facebook's XLM-RoBERTa architecture, specifically fine-tuned for Named Entity Recognition (NER) tasks using the CoNLL-2003 English dataset. The model builds upon the original XLM-RoBERTa large model, which was trained on 2.5TB of filtered CommonCrawl data across 94 languages.
Implementation Details
The model utilizes a transformer-based architecture with 560M parameters, implemented in PyTorch and compatible with ONNX and Safetensors. It operates using F32 tensor types and has been optimized for token classification tasks, particularly excelling at identifying named entities in text.
- Pre-trained on 2.5TB of multilingual data
- Fine-tuned specifically for English NER using CoNLL-2003
- Supports 94 different languages for potential cross-lingual transfer
- Implements state-of-the-art transformer architecture
Core Capabilities
- Named Entity Recognition (NER) in English text
- Token classification for identifying person names, locations, and organizations
- Cross-lingual understanding potential
- High-accuracy entity detection with confidence scoring
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful multilingual capabilities of XLM-RoBERTa with specific optimization for English NER tasks. Its large parameter count (560M) and training on diverse multilingual data make it particularly robust for token classification tasks while maintaining cross-lingual transfer potential.
Q: What are the recommended use cases?
The model is ideal for Named Entity Recognition in English text, particularly in applications requiring identification of persons, locations, and organizations. It can be used in information extraction systems, content analysis, and automated document processing pipelines.
