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import torch
import torch.nn as nn
from .configuration_smsbert import SMSBertConfig
from transformers import pipeline, BertModel, AutoTokenizer, PretrainedConfig,PreTrainedModel, Pipeline, AutoModel,AutoModelForSequenceClassification, BertConfig
class SMSBertModel(PreTrainedModel):
config_class = SMSBertConfig
def __init__(self, config):
super().__init__(config)
# self.bert = BertModel.from_pretrained(base_model_path)
# self.config= kwargs
# self.config = config
self.bert = BertModel._from_config(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)
# print(type(outputs))
# sequence_output, pooled_output = outputs.last_hidden_state, outputs.pooler_output
# token_classification_logits = self.token_classifier(self.dropout(sequence_output))
# sequence_logits = self.sequence_classifier(self.dropout(pooled_output))
# return token_classification_logits, sequence_logits
def forward(self, input_ids,attention_mask, token_type_ids):
# out = self.tokenizer(inputs, return_tensors="pt")
# outputs =
outputs = self.bert(input_ids, attention_mask, token_type_ids)
sequence_output, pooled_output = outputs.last_hidden_state, outputs.pooler_output
token_classification_logits = self.token_classifier(self.dropout(sequence_output))
sequence_logits = self.sequence_classifier(self.dropout(pooled_output))
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]
model_out = str({"token_classfier":token_classification_out, "sequence_classfier": seq_classification_out})
return model_out
# return token_classification_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} |