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--- |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- google-bert/bert-large-cased |
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pipeline_tag: token-classification |
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tags: |
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- code |
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--- |
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This model is used to tag the tokens in an input sequence with information about the different signs of |
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syntactic complexity that they contain. For more details, please see Chapters 2 and 3 of my thesis |
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(https://rj3vans.github.io/Evans2020_SentenceSimplificationForTextProcessing.pdf. |
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It was derived using code written by Dr. Le An Ha at the University of Wolverhampton. |
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To use this model, the following code snippet may help: |
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====================================================================== |
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~~~ |
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import torch |
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from transformers import AutoModelForTokenClassification, AutoTokenizer |
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SignTaggingModel = AutoModelForTokenClassification.from_pretrained('RJ3vans/SignTagger') |
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SignTaggingTokenizer = AutoTokenizer.from_pretrained('RJ3vans/SignTagger') |
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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 |
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"M:N_CLQ", "M:N_CLV", "M:N_CMA1", "M:N_CMAdv", "M:N_CMN1", |
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"M:N_CMN2", "M:N_CMN3", "M:N_CMN4", "M:N_CMP", "M:N_CMP2", |
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"M:N_CMV1", "M:N_CMV2", "M:N_CMV3", "M:N_COMBINATORY", "M:N_CPA", |
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"M:N_ESAdvP", "M:N_ESCCV", "M:N_ESCM", "M:N_ESMA", "M:N_ESMAdvP", |
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"M:N_ESMI", "M:N_ESMN", "M:N_ESMP", "M:N_ESMV", "M:N_HELP", |
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"M:N_SPECIAL", "M:N_SSCCV", "M:N_SSCM", "M:N_SSMA", "M:N_SSMAdvP", |
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"M:N_SSMI", "M:N_SSMN", "M:N_SSMP", "M:N_SSMV", "M:N_STQ", |
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"M:N_V", "M:N_nan", "M:Y_CCV", "M:Y_CIN", "M:Y_CLA", "M:Y_CLAdv", |
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"M:Y_CLN", "M:Y_CLP", "M:Y_CLQ", "M:Y_CLV", "M:Y_CMA1", |
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"M:Y_CMAdv", "M:Y_CMN1", "M:Y_CMN2", "M:Y_CMN4", "M:Y_CMP", |
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"M:Y_CMP2", "M:Y_CMV1", "M:Y_CMV2", "M:Y_CMV3", |
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"M:Y_COMBINATORY", "M:Y_CPA", "M:Y_ESAdvP", "M:Y_ESCCV", |
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"M:Y_ESCM", "M:Y_ESMA", "M:Y_ESMAdvP", "M:Y_ESMI", "M:Y_ESMN", |
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"M:Y_ESMP", "M:Y_ESMV", "M:Y_HELP", "M:Y_SPECIAL", "M:Y_SSCCV", |
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"M:Y_SSCM", "M:Y_SSMA", "M:Y_SSMAdvP", "M:Y_SSMI", "M:Y_SSMN", |
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"M:Y_SSMP", "M:Y_SSMV", "M:Y_STQ"] |
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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.' |
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tokens = SignTaggingTokenizer.tokenize(SignTaggingTokenizer.decode(SignTaggingTokenizer.encode(sentence))) |
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inputs = SignTaggingTokenizer.encode(sentence, return_tensors="pt") |
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outputs = SignTaggingModel(inputs)[0] |
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predictions = torch.argmax(outputs, dim=2) |
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print([(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].tolist())]) |
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~~~ |
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====================================================================== |
