ner-english-ontonotes-fast
| Property | Value |
|---|---|
| Framework | Flair/PyTorch |
| Task | Named Entity Recognition |
| Performance | 89.3% F1-Score on Ontonotes |
| Downloads | 24,156 |
What is ner-english-ontonotes-fast?
This is a specialized Named Entity Recognition (NER) model built using the Flair framework, designed for fast and accurate entity detection in English text. It's capable of identifying 18 different types of entities including persons, organizations, dates, and monetary values. The model leverages Flair embeddings combined with an LSTM-CRF architecture to achieve high accuracy while maintaining computational efficiency.
Implementation Details
The model utilizes a sophisticated architecture combining GloVe embeddings with bidirectional Flair embeddings (news-forward-fast and news-backward-fast), processed through a hidden layer of 256 units. The implementation uses a stacked embedding approach, allowing it to capture both contextual and static word representations.
- Utilizes LSTM-CRF architecture for sequence labeling
- Implements stacked embeddings (GloVe + Flair)
- 256-dimensional hidden layer
- Trained on the Ontonotes dataset
Core Capabilities
- Identifies 18 distinct entity types including PERSON, ORG, DATE, MONEY, etc.
- Processes both simple and complex entity mentions
- Handles contextual entity disambiguation
- Provides confidence scores for predictions
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its balance of speed and accuracy, achieving an 89.3% F1-score while maintaining faster inference times compared to larger alternatives. It's specifically optimized for production environments where computational efficiency is crucial.
Q: What are the recommended use cases?
This model is ideal for applications requiring fast and accurate entity recognition in English text, such as information extraction systems, automated content analysis, and real-time text processing pipelines. It's particularly suitable for scenarios where processing speed is as important as accuracy.
