File size: 1,547 Bytes
c7f5132 |
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 32 33 34 35 36 37 38 39 40 |
import torch.nn as nn
import torch
import numpy as np
from huggingface_hub import PyTorchModelHubMixin
from transformers import BertModel, AutoTokenizer
class IndoBertLSTMEcommerceReview(nn.Module, PyTorchModelHubMixin):
def __init__(self, bert):
super().__init__()
self.bert = bert
self.lstm = nn.LSTM(bert.config.hidden_size, 128)
self.linear = nn.Linear(128, 3)
self.sigmoid = nn.Sigmoid()
def forward(self, input_ids, attention_mask):
outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
# print(outputs.keys())
last_hidden_state = outputs.last_hidden_state
lstm_out, _ = self.lstm(last_hidden_state)
pooled = lstm_out[:, -1, :]
logits = self.linear(pooled)
probabilities = self.sigmoid(logits)
return probabilities
bert = BertModel.from_pretrained("indobenchmark/indobert-base-p1")
tokenizer = AutoTokenizer.from_pretrained("fahrendrakhoirul/indobert-finetuned-ecommerce-reviews")
indobertlstm_model = IndoBertLSTMEcommerceReview.from_pretrained("fahrendrakhoirul/indobert-lstm-finetuned-ecommerce-reviews", bert=bert).to('cpu')
# run modell
res_token = tokenizer("hahahah", return_tensors="pt").to('cpu')
input_ids = res_token['input_ids'] # Unpack dictionary
attention_mask = res_token['attention_mask']# Unpack dictionary
print(res_token)
with torch.no_grad():
logits = indobertlstm_model(input_ids=input_ids, attention_mask=attention_mask)
preds = torch.sigmoid(logits).detach().cpu().numpy()[0]
print(preds) |