from layoutlm import FunsdDataset, LayoutlmConfig, LayoutlmForTokenClassification
from transformers import BertTokenizer
import torch
MODEL_CLASSES = { "layoutlm": (LayoutlmConfig, LayoutlmForTokenClassification, BertTokenizer), }
def main():
if torch.cuda.is_available():
device = torch.device("cuda")
print("GPU is available")
else:
device = torch.device("cpu")
print("GPU is not available, using CPU instead")
labels = get_labels(labels) # in our case labels will be x-axis,y-axis,title
num_labels = len(labels) # Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later
pad_token_label_id = CrossEntropyLoss().ignore_index
config = config_class.from_pretrained( "layoutlm-base-uncased/", num_labels=num_labels, force_download = True, ignore_mismatched_sizes=True, cache_dir= cache_dir_path else None, )
tokenizer = tokenizer_class.from_pretrained( "microsoft/layoutlm-base-uncased", do_lower_case=True, force_download = True, ignore_mismatched_sizes=True, cache_dir= cache_dir_path else None, )
model = model_class.from_pretrained( "layoutlm-base-uncased/", config=config, )
model.to(args.device)
train_dataset = FunsdDataset( args, tokenizer, labels, pad_token_label_id, mode="train" )
global_step, tr_loss = train( args, train_dataset, model, tokenizer, labels, pad_token_label_id )
tokenizer = tokenizer_class.from_pretrained( "microsoft/layoutlm-base-uncased",force_download = True, do_lower_case=args.do_lower_case,ignore_mismatched_sizes=True)
model = model_class.from_pretrained(args.output_dir)
model.to(args.device)
result, predictions = evaluate( args, model, tokenizer, labels, pad_token_label_id, mode="test" )
return result,predictions
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