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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"
) |