layoutlm_doc_classification_example.py

from transformers import LayoutLMTokenizer, LayoutLMForSequenceClassification
import torch

tokenizer = LayoutLMTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
model = LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased")

words = ["Hello", "world"]
normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

token_boxes = []
for word, box in zip(words, normalized_word_boxes):
    word_tokens = tokenizer.tokenize(word)
    token_boxes.extend([box] * len(word_tokens))
# add bounding boxes of cls + sep tokens
token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

encoding = tokenizer(" ".join(words), return_tensors="pt")
input_ids = encoding["input_ids"]
attention_mask = encoding["attention_mask"]
token_type_ids = encoding["token_type_ids"]
bbox = torch.tensor([token_boxes])
sequence_label = torch.tensor([1])

outputs = model(
    input_ids=input_ids,
    bbox=bbox,
    attention_mask=attention_mask,
    token_type_ids=token_type_ids,
    labels=sequence_label,
)

loss = outputs.loss
logits = outputs.logits