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from typing import Dict, List, Any
import torch
import torch.nn as nn
import json
from transformers import pipeline, BertModel, AutoTokenizer, PretrainedConfig
class EndpointHandler():
def __init__(self, path=""):
# self.pipeline = pipeline("text-classification",model=path)
self.model = CustomModel("test_bert_config.json")
self.model.load_state_dict(torch.load("model3.pth"))
def __call__(self, data: Dict[str, Any])-> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str`)
date (:obj: `str`)
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# get inputs
inputs = data.pop("inputs",data)
# date = data.pop("date", None)
# check if date exists and if it is a holiday
# if date is not None and date in self.holidays:
# return [{"label": "happy", "score": 1}]
# run normal prediction
prediction = self.model.classify(inputs)
prediction = json.dumps(prediction)
return prediction
class CustomModel(nn.Module):
def __init__(self, bert_config):
super(CustomModel, self).__init__()
# self.bert = BertModel.from_pretrained(base_model_path)
self.bert = BertModel._from_config(PretrainedConfig.from_json_file(bert_config))
self.dropout = nn.Dropout(0.2)
self.token_classifier = nn.Linear(self.bert.config.hidden_size, 16)
self.sequence_classifier = nn.Linear(self.bert.config.hidden_size, 7)
# Initialize weights
nn.init.kaiming_normal_(self.token_classifier.weight, mode='fan_in', nonlinearity='linear')
nn.init.kaiming_normal_(self.sequence_classifier.weight, mode='fan_in', nonlinearity='linear')
self.seq_labels = [
"Transaction",
"Courier",
"OTP",
"Expiry",
"Misc",
"Tele Marketing",
"Spam",
]
self.token_class_labels = [
'O',
'Courier Service',
'Credit',
'Date',
'Debit',
'Email',
'Expiry',
'Item',
'Order ID',
'Organization',
'OTP',
'Phone Number',
'Refund',
'Time',
'Tracking ID',
'URL',
]
base_model_path = '.'
self.tokenizer = AutoTokenizer.from_pretrained(base_model_path)
def forward(self, input_ids : torch.Tensor, attention_mask: torch.Tensor, token_type_ids: torch.Tensor):
outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids)
sequence_output, pooled_output = outputs.last_hidden_state, outputs.pooler_output
token_logits = self.token_classifier(self.dropout(sequence_output))
sequence_logits = self.sequence_classifier(self.dropout(pooled_output))
return token_logits, sequence_logits
def classify(self, inputs):
out = self.tokenizer(inputs, return_tensors="pt")
token_classification_logits, sequence_logits = self.forward(**out)
token_classification_logits = token_classification_logits.argmax(2)[0]
sequence_logits = sequence_logits.argmax(1)[0]
token_classification_out = [self.token_class_labels[i] for i in token_classification_logits.tolist()]
seq_classification_out = self.seq_labels[sequence_logits]
# return token_classification_out, seq_classification_out
return {"token_classfier":token_classification_out, "sequence_classfier": seq_classification_out} |