chunk-english
| Property | Value |
|---|---|
| Framework | PyTorch + Flair |
| Task | Token Classification (Chunking) |
| Dataset | CoNLL-2000 |
| Performance | 96.48% F1-Score |
| Downloads | 910 |
What is chunk-english?
chunk-english is a state-of-the-art phrase chunking model developed using the Flair framework. It's designed to identify and classify different types of phrases in English text, including noun phrases (NP), verb phrases (VP), and other syntactic constituents. The model leverages advanced Flair embeddings combined with an LSTM-CRF architecture to achieve impressive accuracy in phrase identification.
Implementation Details
The model employs a sophisticated neural architecture combining contextual string embeddings (Flair embeddings) with a bidirectional LSTM and CRF layer. It's trained on the CoNLL-2000 dataset and utilizes both forward and backward news embeddings to capture rich contextual information.
- Architecture: LSTM-CRF with Flair embeddings
- Hidden Size: 256 dimensions
- Training Duration: Up to 150 epochs
- Embedding Types: news-forward and news-backward Flair embeddings
Core Capabilities
- Identifies 10 different phrase types (ADJP, ADVP, CONJP, INTJ, LST, NP, PP, PRT, SBAR, VP)
- High-accuracy phrase boundary detection
- Real-time text processing capability
- Simple integration with the Flair framework
- Support for complex sentence structures
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its high F1-score of 96.48% on the CoNLL-2000 dataset and its use of sophisticated Flair embeddings, making it particularly effective for English phrase chunking tasks. It's also the default chunking model in the Flair framework, ensuring robust support and maintenance.
Q: What are the recommended use cases?
The model is ideal for natural language processing tasks requiring phrase identification, such as syntactic parsing, information extraction, and text analysis. It's particularly useful in applications needing accurate identification of noun phrases, verb phrases, and other syntactic constituents in English text.
