DLF / trains /subNets /BertTextEncoder.py

peter-wang321

Initial DLF commit

9157432 almost 2 years ago

2.04 kB

import torch
import torch.nn as nn
from transformers import BertModel, BertTokenizer, RobertaModel, RobertaTokenizer

__all__ = ['BertTextEncoder']

TRANSFORMERS_MAP = {
    'bert': (BertModel, BertTokenizer),
    'roberta': (RobertaModel, RobertaTokenizer),
}

class BertTextEncoder(nn.Module):
    def __init__(self, use_finetune=False, transformers='bert', pretrained='bert-base-uncased'):
        super().__init__()

        tokenizer_class = TRANSFORMERS_MAP[transformers][1]
        model_class = TRANSFORMERS_MAP[transformers][0]
        self.tokenizer = tokenizer_class.from_pretrained(pretrained)
        self.model = model_class.from_pretrained(pretrained)
        self.use_finetune = use_finetune
    
    def get_tokenizer(self):
        return self.tokenizer
    
    # def from_text(self, text):
    #     """
    #     text: raw data
    #     """
    #     input_ids = self.get_id(text)
    #     with torch.no_grad():
    #         last_hidden_states = self.model(input_ids)[0]  # Models outputs are now tuples
    #     return last_hidden_states.squeeze()
    
    def forward(self, text):
        """
        text: (batch_size, 3, seq_len)
        3: input_ids, input_mask, segment_ids
        input_ids: input_ids,
        input_mask: attention_mask,
        segment_ids: token_type_ids
        """
        input_ids, input_mask, segment_ids = text[:,0,:].long(), text[:,1,:].float(), text[:,2,:].long()
        if self.use_finetune:
            last_hidden_states = self.model(input_ids=input_ids,
                                            attention_mask=input_mask,
                                            token_type_ids=segment_ids)[0]  # Models outputs are now tuples
        else:
            with torch.no_grad():
                last_hidden_states = self.model(input_ids=input_ids,
                                                attention_mask=input_mask,
                                                token_type_ids=segment_ids)[0]  # Models outputs are now tuples
        return last_hidden_states

	import torch
	import torch.nn as nn
	from transformers import BertModel, BertTokenizer, RobertaModel, RobertaTokenizer

	__all__ = ['BertTextEncoder']

	TRANSFORMERS_MAP = {
	'bert': (BertModel, BertTokenizer),
	'roberta': (RobertaModel, RobertaTokenizer),
	}

	class BertTextEncoder(nn.Module):
	def __init__(self, use_finetune=False, transformers='bert', pretrained='bert-base-uncased'):
	super().__init__()

	tokenizer_class = TRANSFORMERS_MAP[transformers][1]
	model_class = TRANSFORMERS_MAP[transformers][0]
	self.tokenizer = tokenizer_class.from_pretrained(pretrained)
	self.model = model_class.from_pretrained(pretrained)
	self.use_finetune = use_finetune

	def get_tokenizer(self):
	return self.tokenizer

	# def from_text(self, text):
	# """
	# text: raw data
	# """
	# input_ids = self.get_id(text)
	# with torch.no_grad():
	# last_hidden_states = self.model(input_ids)[0] # Models outputs are now tuples
	# return last_hidden_states.squeeze()

	def forward(self, text):
	"""
	text: (batch_size, 3, seq_len)
	3: input_ids, input_mask, segment_ids
	input_ids: input_ids,
	input_mask: attention_mask,
	segment_ids: token_type_ids
	"""
	input_ids, input_mask, segment_ids = text[:,0,:].long(), text[:,1,:].float(), text[:,2,:].long()
	if self.use_finetune:
	last_hidden_states = self.model(input_ids=input_ids,
	attention_mask=input_mask,
	token_type_ids=segment_ids)[0] # Models outputs are now tuples
	else:
	with torch.no_grad():
	last_hidden_states = self.model(input_ids=input_ids,
	attention_mask=input_mask,
	token_type_ids=segment_ids)[0] # Models outputs are now tuples
	return last_hidden_states