Flair NER English Model
| Property | Value |
|---|---|
| Downloads | 344,335 |
| Framework | PyTorch + Flair |
| Dataset | CoNLL-2003 |
| Performance | 93.06% F1-Score |
What is ner-english?
The ner-english model is Flair's default Named Entity Recognition (NER) model for English text, designed to identify and classify named entities in text. It represents a state-of-the-art approach to NER, combining Flair embeddings with an LSTM-CRF architecture to achieve high accuracy in entity detection.
Implementation Details
The model employs a sophisticated architecture combining GloVe embeddings with contextual string embeddings (both forward and backward) using Flair. It uses a hidden size of 256 in its LSTM layer and was trained for 150 epochs on the CoNLL-2003 dataset.
- Utilizes stacked embeddings (GloVe + Flair forward/backward)
- LSTM-CRF architecture for sequence labeling
- Trained on the standard CoNLL-2003 dataset
- Optimized for production use with PyTorch backend
Core Capabilities
- Identifies four entity types: Person (PER), Location (LOC), Organization (ORG), and Miscellaneous (MISC)
- Processes raw text input and returns labeled entities with confidence scores
- Handles complex entity recognition scenarios with contextual understanding
- Achieves 93.06% F1-score on the CoNLL-03 benchmark
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its use of Flair's contextual string embeddings combined with traditional word embeddings, providing superior context understanding for entity recognition. It's the default choice in the Flair framework due to its balance of accuracy and efficiency.
Q: What are the recommended use cases?
The model is ideal for applications requiring English named entity recognition, such as information extraction, document processing, and automated content analysis. It's particularly effective for identifying people, locations, organizations, and miscellaneous named entities in natural text.
