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token-classification

Token Classification generic

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token-classification / pipeline.py

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osanseviero HF staff

Update pipeline.py

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	from typing import Dict, List, Any

	class PreTrainedPipeline():
	def __init__(self, path=""):
	# IMPLEMENT_THIS
	# Preload all the elements you are going to need at inference.
	# For instance your model, processors, tokenizer that might be needed.
	# This function is only called once, so do all the heavy processing I/O here"""
	raise NotImplementedError(
	"Please implement PreTrainedPipeline __init__ function"
	)

	def __call__(self, inputs: str) -> List[Dict[str, Any]]:
	"""
	Args:
	inputs (:obj:`str`):
	a string containing some text
	Return:
	A :obj:`list`:. The object returned should be like [{"entity_group": "XXX", "word": "some word", "start": 3, "end": 6, "score": 0.82}] containing :
	- "entity_group": A string representing what the entity is.
	- "word": A substring of the original string that was detected as an entity.
	- "start": the offset within `input` leading to `answer`. context[start:stop] == word
	- "end": the ending offset within `input` leading to `answer`. context[start:stop] === word
	- "score": A score between 0 and 1 describing how confident the model is for this entity.
	"""
	# IMPLEMENT_THIS
	raise NotImplementedError(
	"Please implement PreTrainedPipeline __call__ function"
	)