ner-english-fast
| Property | Value |
|---|---|
| Downloads | 1,288,130 |
| Model Type | Named Entity Recognition |
| Framework | PyTorch / Flair |
| Dataset | CoNLL-2003 |
| Performance | 92.92% F1-Score |
What is ner-english-fast?
ner-english-fast is a high-performance Named Entity Recognition model designed for quick and accurate entity detection in English text. Built using the Flair framework, it implements a LSTM-CRF architecture combined with Flair embeddings to identify four types of entities: Person (PER), Location (LOC), Organization (ORG), and Miscellaneous (MISC).
Implementation Details
The model utilizes a sophisticated architecture combining GloVe embeddings with contextual string embeddings (both forward and backward) from Flair. It employs a hidden size of 256 units in its LSTM layer and was trained for 150 epochs on the CoNLL-2003 dataset.
- Stacked embeddings combining GloVe and bi-directional Flair embeddings
- LSTM-CRF sequence labeling architecture
- Optimized for speed while maintaining high accuracy
- Trained on the standard CoNLL-2003 NER dataset
Core Capabilities
- Fast and accurate entity recognition with 92.92% F1-score
- Four-class classification: PER, LOC, ORG, MISC
- Efficient processing of both short and long text sequences
- Simple integration with the Flair framework
- Support for batch processing and GPU acceleration
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimal balance between speed and accuracy, making it particularly suitable for production environments where processing time is crucial but high accuracy is still required. The "fast" variant maintains impressive performance while being more efficient than its larger counterparts.
Q: What are the recommended use cases?
The model is ideal for applications requiring real-time entity recognition, such as information extraction systems, document processing pipelines, and content analysis tools. It's particularly effective for scenarios where quick processing of large text volumes is needed while maintaining reliable entity detection.
