Update README.md

README.md

@@ -1,3 +1,48 @@
 ---
 license: mit
+language:
+- en
+pipeline_tag: document-question-answering
 ---
+This model trained on DocVQA Dataset on sample of 15000 questions
+
+```
+from transformers import AutoTokenizer, AutoModelForDocumentQuestionAnswering
+from datasets import load_dataset
+
+model_checkpoint = "TusharGoel/LayoutLM-Finetuned-DocVQA"
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, add_prefix_space=True)
+model_predict = AutoModelForDocumentQuestionAnswering.from_pretrained(model_checkpoint)
+
+model_predict.eval()
+dataset = load_dataset("nielsr/funsd", split="train")
+example = dataset[0]
+
+question = "What's Licensee Number?"
+
+words = example["words"]
+boxes = example["bboxes"]
+
+encoding = tokenizer(question.split(), words,
+                            is_split_into_words=True, return_token_type_ids=True, return_tensors="pt")
+
+bbox = []
+for i, s, w in zip(encoding.input_ids[0], encoding.sequence_ids(0), encoding.word_ids(0)):
+    if s == 1:
+        bbox.append(boxes[w])
+    elif i == tokenizer.sep_token_id:
+        bbox.append([1000] * 4)
+    else:
+        bbox.append([0] * 4)
+encoding["bbox"] = torch.tensor([bbox])
+
+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(" ".join(words[start : end + 1]))
+```