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from typing import Dict, List, Any
import torch
from transformers import pipeline, XLMRobertaTokenizerFast, XLMRobertaForSequenceClassification
class EndpointHandler:
def __init__(self, path=""):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# load the optimized model
model = XLMRobertaForSequenceClassification.from_pretrained(path)
tokenizer = XLMRobertaTokenizerFast.from_pretrained(path)
model.eval()
# create inference pipeline
self.pipline = pipeline("text-classification", tokenizer=tokenizer, model=model, device=self.device)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
Return:
A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
- "label": A string representing what the label/class is. There can be multiple labels.
- "score": A score between 0 and 1 describing how confident the model is for this label/class.
"""
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
# pass inputs with all kwargs in data
if parameters is not None:
prediction = self.pipline(inputs, **parameters)
else:
prediction = self.pipline(inputs)
# postprocess the prediction
return [{"label": p["label"]} for p in prediction]
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