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| from transformers import pipeline | |
| class FillInSummary: | |
| """Organizing Summarization and Subsequent Fill-In-Task.""" | |
| def __init__(self): | |
| """Initialize Class with Summarizer and NER-Model.""" | |
| # Refer to https://huggingface.co/docs/transformers/v4.18.0/en/main_classes/pipelines#transformers.SummarizationPipeline | |
| # for further information about configuration. | |
| self.summarizer = pipeline("summarization", model="facebook/bart-large-cnn") | |
| # Using default model: https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english | |
| self.ner = ner= pipeline("ner", aggregation_strategy='simple') | |
| def summarize(self, text: str) -> str: | |
| """Summarize given Text. | |
| Parameter | |
| --------- | |
| text : str | |
| Text to be summarized. Must not exceeds BART's maximal input length. | |
| Returns | |
| ------- | |
| str | |
| Summary | |
| """ | |
| # Refer to https://huggingface.co/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig | |
| # for further configuration of of the | |
| output: list = self.summarizer( | |
| text, | |
| max_length=400, | |
| min_length=100, | |
| do_sample=False) | |
| return output[0]['summary_text'] | |
| def blank_ne_out(self, text: str) -> dict(): | |
| """Blank out named entities. | |
| Transforms 'X did this.' to { | |
| 'text': '_ did this', | |
| 'ner': [{ | |
| 'end': 1, | |
| 'entity_group': 'ORG', | |
| 'score': 0.73085225, | |
| 'start': 0, | |
| 'word': 'X' | |
| }]} | |
| Parameter | |
| --------- | |
| text : str | |
| Summarized text. | |
| Returns | |
| ------- | |
| dict | |
| Entails blanked out text and recognized named entity list. | |
| """ | |
| ner_list: list = self.ner(text) | |
| output_str: str = text | |
| for start, end in map(lambda e : (e['start'], e['end']), ner_list): | |
| length: int = end - start | |
| output_str = output_str[0:start] + ("_" * length) + output_str[end::] | |
| return { | |
| 'text': output_str, | |
| 'ner': ner_list | |
| } | |