Args: data_in: a list of dictionaries each dict has several fields including `sentences` and `ner` `ner` data ar

Converts the tag ids to the actual tags. ``output_dict["tags"]`` is a list of lists of tag_ids, so we use an ugly nested list

Split given document into its sentences :param doc_text: Text to tokenize :return: list of spacy sentences

Args: inpath: input file from sciie outpath: output file with relations information with_entity_markers: True/False, if T

Args: inpath: input file from chemprot in xml format outpath: output file with relations information with_entity_markers:

Converts the tag ids to the actual tags. ``output_dict["tags"]`` is a list of lists of tag_ids, so we use an ugly nested list

Parameters ---------- text : Dict[str, torch.LongTensor] From a ``TextField`` label : torch.IntTensor, op

Parameters ---------- text : Dict[str, torch.LongTensor] From a ``TextField`` label : torch.IntTensor, op

Parameters ---------- tokens : ``Dict[str, torch.LongTensor]``, required The output of ``TextField.as_array()``,

Checks if the string is an English sentence. :param sentence: spacy string :return: True / False