Instructions to use RJ3vans/SSCCVspanTagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RJ3vans/SSCCVspanTagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="RJ3vans/SSCCVspanTagger")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("RJ3vans/SSCCVspanTagger") model = AutoModelForTokenClassification.from_pretrained("RJ3vans/SSCCVspanTagger") - Notebooks
- Google Colab
- Kaggle
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Try the test sentences:
My name is Sarah and I live in London[, which] is the largest city in the UK.
John thought [that] that was a strange idea.
It was on Tuesdays [when] Peter took Tess for a walk.
John was so large [that] he had to crouch to fit through the front door.
Please note that it is necessary for you to highlight the left clause boundary 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 particular types of syntactic constituents. 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)
There you will find more information about the tagging scheme.
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