| | --- |
| | license: mit |
| | language: |
| | - en |
| | library_name: transformers |
| | inference: false |
| | pipeline_tag: document-question-answering |
| | --- |
| | |
| | LiLT Model [Read Here](https://arxiv.org/pdf/2202.13669v1.pdf). This model being fine-tuned on English DocVQA |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForQuestionAnswering |
| | from datasets import load_dataset |
| | |
| | model_checkpoint = "TusharGoel/LiLT-Document-QA" |
| | tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, add_prefix_space=True) |
| | model_predict = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint) |
| | |
| | model_predict.eval() |
| | dataset = load_dataset("nielsr/funsd", split="train") |
| | example = dataset[0] |
| | print(example) |
| | |
| | question = "What is the Licensee Number?" |
| | print(question) |
| | |
| | words = example["words"] |
| | boxes = example["bboxes"] |
| | |
| | encoding = tokenizer(question, words, boxes = boxes, return_token_type_ids=True, return_tensors="pt") |
| | |
| | word_ids = encoding.word_ids(0) |
| | outputs = model_predict(**encoding) |
| | |
| | loss = outputs.loss |
| | start_scores = outputs.start_logits |
| | end_scores = outputs.end_logits |
| | |
| | start, end = word_ids[start_scores.argmax(-1).item()], word_ids[end_scores.argmax(-1).item()] |
| | # print(start, end) |
| | print(" ".join(words[start : end + 1])) |
| | ``` |
