Update README.md

README.md

@@ -1,3 +1,49 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+### Generating Questions Given Context and Answers
+
+Traditional BART model is not pre-trained on QG tasks. We fine-tuned `facebook/bart-large` model using 55k human-created question answering pairs with contexts collected by [Demszky et al. (2018)](https://arxiv.org/abs/1809.02922). The dataset includes SQuAD and QA2D question answering pairs associated with contexts.  
+
+### How to use
+Here is how to use this model in PyTorch:
+```python
+from transformers import BartForConditionalGeneration, BartTokenizer
+import torch
+
+tokenizer = BartTokenizer.from_pretrained('uzw/bart-large-question-generation')
+model = BartForConditionalGeneration.from_pretrained('uzw/bart-large-question-generation')
+
+context = "The Thug cult resides at the Pankot Palace."
+answer = "The Thug cult"
+
+inputs = tokenizer.encode_plus(
+    context, 
+    answer, 
+    max_length=512, 
+    padding='max_length', 
+    truncation=True, 
+    return_tensors='pt'
+)
+
+with torch.no_grad():
+    generated_ids = model.generate(
+        input_ids=inputs['input_ids'], 
+        attention_mask=inputs['attention_mask'],
+        max_length=64,  # Maximum length of generated question
+        num_return_sequences=3,  # Generate multiple questions
+        do_sample=True,  # Enable sampling for diversity
+        temperature=0.7  # Control randomness of generation
+    )
+
+generated_questions = tokenizer.batch_decode(
+    generated_ids, 
+    skip_special_tokens=True
+)
+
+for i, question in enumerate(generated_questions, 1):
+    print(f"Generated Question {i}: {question}")
+```
+
+Adjusting parameter `num_return_sequences` to generate multiple questions.