Bertweet Tb2 Ewt Pos Tagging
The state-of-the-art Twitter POS tagging model with an accuracy of 95.38%, trained on Tweebank V2 NER benchmark and English-EWT corpus.
Downloads 45.75k
Release Time : 5/3/2022
Model Overview
This model is specifically designed for POS tagging tasks on tweet texts, combining training data from Tweebank-NER and English-EWT corpora, excelling in Twitter text processing.
Model Features
High Accuracy
Achieves 95.38% accuracy on the Tweebank V2 NER benchmark, making it the state-of-the-art Twitter POS tagging model.
Multi-Corpus Training
Incorporates training data from both Tweebank-NER and English-EWT corpora, enhancing the model's generalization capabilities.
Twitter-Optimized
Specifically optimized for Twitter text, with TweetTokenizer preprocessing for optimal performance.
Model Capabilities
Twitter text POS tagging
Natural language processing
Use Cases
Social media analysis
Tweet POS analysis
Performs POS tagging on Twitter text for subsequent text analysis and understanding.
Accurately identifies various parts of speech in tweets with 95.38% accuracy.
Natural language processing research
POS tagging benchmark
Serves as a benchmark model for POS tagging tasks to evaluate the performance of other models.
Performs exceptionally well on the Tweebank V2 NER benchmark.
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