Instructions to use RJ3vans/SSMNspanTagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RJ3vans/SSMNspanTagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="RJ3vans/SSMNspanTagger")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("RJ3vans/SSMNspanTagger") model = AutoModelForTokenClassification.from_pretrained("RJ3vans/SSMNspanTagger", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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Try the test sentence:
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My name is Sarah and I live in London[,] the capital of England.
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Note that accuracy is greatly improved if you place square brackets around the left boundary of the non-finite nominal clause.
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This model identifies complex NPs modified by non-finite nominal clauses ("appositives") in the input sentence.
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Try the test sentence:
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My name is Sarah and I live in London[,] the capital of England.
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Note that accuracy is greatly improved if you place square brackets around the left boundary of the non-finite nominal clause.
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The model was derived using code adapted from an original program written by Dr. Le An Ha at the University of Wolverhampton.
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