File size: 1,328 Bytes
3625af8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 |
from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoConfig, AutoModelForSequenceClassification
import torch
class EndpointHandler:
def __init__(self, path=""):
# load model and processor from path
guider_config = AutoConfig.from_pretrained(path)
self.model = AutoModelForSequenceClassification.from_pretrained(path, config=guider_config)
self.tokenizer = AutoTokenizer.from_pretrained(path)
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""
Args:
data (:dict:):
The payload with the text prompt.
"""
# process input
gen_outputs_no_input_decoded = data.pop("gen_outputs_no_input_decoded", data)
# Guiding the model with his ranking,
guider_inputs = self.tokenizer([gen_output_no_input_decoded for gen_output_no_input_decoded in gen_outputs_no_input_decoded],
return_tensors='pt', padding=True, truncation=True)
guider_outputs = self.model(**guider_inputs)
# the slicing at the end [:,x]: x=0 for negative, x=1 for neutral, x=2 for positive
guider_predictions = torch.nn.functional.softmax(guider_outputs.logits, dim=-1)[:, 0].tolist()
return {"guider_predictions": guider_predictions} |