RJ3vans/SignTagger

This model is used to tag the tokens in an input sequence with information about the different signs of syntactic complexity that they contain. For more details, please see Chapters 2 and 3 of my thesis (https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf.

It was derived using code written by Dr. Le An Ha at the University of Wolverhampton.

To use this model, the following code snippet may help:

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import torch from transformers import AutoModelForTokenClassification, AutoTokenizer

SignTaggingModel = AutoModelForTokenClassification.from_pretrained('RJ3vans/SignTagger') SignTaggingTokenizer = AutoTokenizer.from_pretrained('RJ3vans/SignTagger')

label_list = ["M:N_CCV", "M:N_CIN", "M:N_CLA", "M:N_CLAdv", "M:N_CLN", "M:N_CLP", # This could be obtained from the config file "M:N_CLQ", "M:N_CLV", "M:N_CMA1", "M:N_CMAdv", "M:N_CMN1", "M:N_CMN2", "M:N_CMN3", "M:N_CMN4", "M:N_CMP", "M:N_CMP2", "M:N_CMV1", "M:N_CMV2", "M:N_CMV3", "M:N_COMBINATORY", "M:N_CPA", "M:N_ESAdvP", "M:N_ESCCV", "M:N_ESCM", "M:N_ESMA", "M:N_ESMAdvP", "M:N_ESMI", "M:N_ESMN", "M:N_ESMP", "M:N_ESMV", "M:N_HELP", "M:N_SPECIAL", "M:N_SSCCV", "M:N_SSCM", "M:N_SSMA", "M:N_SSMAdvP", "M:N_SSMI", "M:N_SSMN", "M:N_SSMP", "M:N_SSMV", "M:N_STQ", "M:N_V", "M:N_nan", "M:Y_CCV", "M:Y_CIN", "M:Y_CLA", "M:Y_CLAdv", "M:Y_CLN", "M:Y_CLP", "M:Y_CLQ", "M:Y_CLV", "M:Y_CMA1", "M:Y_CMAdv", "M:Y_CMN1", "M:Y_CMN2", "M:Y_CMN4", "M:Y_CMP", "M:Y_CMP2", "M:Y_CMV1", "M:Y_CMV2", "M:Y_CMV3", "M:Y_COMBINATORY", "M:Y_CPA", "M:Y_ESAdvP", "M:Y_ESCCV", "M:Y_ESCM", "M:Y_ESMA", "M:Y_ESMAdvP", "M:Y_ESMI", "M:Y_ESMN", "M:Y_ESMP", "M:Y_ESMV", "M:Y_HELP", "M:Y_SPECIAL", "M:Y_SSCCV", "M:Y_SSCM", "M:Y_SSMA", "M:Y_SSMAdvP", "M:Y_SSMI", "M:Y_SSMN", "M:Y_SSMP", "M:Y_SSMV", "M:Y_STQ"]

sentence = 'The County Court in Nottingham heard that Roger Gedge, 30, had his leg amputated following the incident outside a rock festival in Wollaton Park, Nottingham, five years ago.'

tokens = SignTaggingTokenizer.tokenize(SignTaggingTokenizer.decode(SignTaggingTokenizer.encode(sentence))) inputs = SignTaggingTokenizer.encode(sentence, return_tensors="pt")

outputs = SignTaggingModel(inputs)[0] predictions = torch.argmax(outputs, dim=2)

print([(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].tolist())])

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