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from typing import Dict, List, Any
import json
import torch
from transformers import BertTokenizerFast, BertForTokenClassification
class EndpointHandler():
def __init__(self, path=""):
# Load the tokenizer and model
self.tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
self.model = BertForTokenClassification.from_pretrained(path)
self.model.eval()
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.model.to(self.device)
# ID to label mapping
self.id2label = {
0: 'O',
1: 'B-STEREO',
2: 'I-STEREO',
3: 'B-GEN',
4: 'I-GEN',
5: 'B-UNFAIR',
6: 'I-UNFAIR'
}
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
Args:
data (Dict[str, Any]): A dictionary containing the input text under 'inputs'.
Returns:
List[Dict[str, Any]]: A list of dictionaries with token labels.
"""
# Extract the input sentence
sentence = data.get("inputs", "")
if not sentence:
return [{"error": "Input 'inputs' is required."}]
# Tokenize the input sentence
inputs = self.tokenizer(sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
input_ids = inputs['input_ids'].to(self.device)
attention_mask = inputs['attention_mask'].to(self.device)
# Run inference
with torch.no_grad():
outputs = self.model(input_ids=input_ids, attention_mask=attention_mask)
logits = outputs.logits
probabilities = torch.sigmoid(logits)
predicted_labels = (probabilities > 0.5).int()
# Prepare the result
result = []
tokens = self.tokenizer.convert_ids_to_tokens(input_ids[0])
for i, token in enumerate(tokens):
if token not in self.tokenizer.all_special_tokens:
label_indices = (predicted_labels[0][i] == 1).nonzero(as_tuple=False).squeeze(-1)
labels = [self.id2label[idx.item()] for idx in label_indices] if label_indices.numel() > 0 else ['O']
result.append({"token": token, "labels": labels})
return result
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