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") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
This model identifies complex NPs modified by non-finite nominal clauses ("appositives") in the input sentence.
Try the test sentence:
My name is Sarah and I live in London[,] the capital of England.
Please note that it is necessary for you to highlight the left boundary of the non-finite nominal clause "," using square brackets. When deployed in a text simplification method, this sign tagging step can be performed using the model at https://huggingface.co/RJ3vans/SignTagger.
The model should tag the tokens in the sentence with information about whether or not they are contained within a non-finite nominal "appositive" clause. If you find the model useful, please cite my thesis which presents the dataset used for finetuning:
Evans, R. (2020) Sentence Simplification for Text Processing. Doctoral thesis. University of Wolverhampton. Wolverhampton, UK. (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf)
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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