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from torch import nn
from transformers import AutoConfig, AutoModel, AutoTokenizer
import torch
from torch.utils.data import Dataset
from utils import read_yaml
class BanglaHSDataset(Dataset):
def __init__(self, tokenizer, max_length):
self.tokenizer = tokenizer
self.max_length = max_length
def __len__(self): return 0
def __getitem__(self, text):
inputs = self.tokenizer(
text,
max_length=self.max_length, padding='max_length',
truncation=True,
return_offsets_mapping=False
)
for k, v in inputs.items(): inputs[k] = torch.tensor(v, dtype=torch.long).unsqueeze(dim=0)
label = torch.tensor(0, dtype=torch.float)
return inputs, label
def get_class(index):
ind2cat = [
'Geopolitical',
'Personal',
'Political',
'Religious',
]
return ind2cat[index]
if __name__ == '__main__':
cfg = read_yaml('./baseline.yaml')
# cfg.Model.target_size = 6
# model = BanglaHS_Model(cfg.Model)
# #model.load_state_dict(torch.load('./model_fold-0_best.pt', map_location=torch.device('cpu')))
# model.eval()
# ds = BanglaHSDataset(cfg.Dataset, model)
# x = ds['Hello hi'][0]
# with torch.no_grad():
# y = model(x)
# print('y:', y)
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