python generate questions for a given context
# for a given context, generate a question using an ai model # https://pythonprogrammingsnippets.tumblr.com import torch device = torch.device("cpu") from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("voidful/context-only-question-generator") model = AutoModelForSeq2SeqLM.from_pretrained("voidful/context-only-question-generator").to(device) def get_questions_for_context(context, model, tokenizer, num_count=5): inputs = tokenizer(context, return_tensors="pt") with torch.no_grad(): outputs = model.generate(**inputs, num_beams=num_count, num_return_sequences=num_count) return [tokenizer.decode(output, skip_special_tokens=True) for output in outputs] def get_question_for_context(context, model, tokenizer): return get_questions_for_context(context, model, tokenizer)[0] # send array of sentences, and the function will return an array of questions def context_sentences_to_questions(context, model, tokenizer): questions = [] for sentence in context.split("."): if len(sentence) < 1: continue # skip blanks question = get_question_for_context(sentence, model, tokenizer) questions.append(question) return questions
example 1 (split a string by "." and process):
context = "The capital of France is Paris." context += "The capital of Germany is Berlin." context += "The capital of Spain is Madrid." context += "He is a dog named Robert." if len(context.split(".")) > 2: questions = [] for sentence in context.split("."): if len(sentence) < 1: continue # skip blanks question = get_question_for_context(sentence, model, tokenizer) questions.append(question) print(questions) else: question = get_question_for_context(context, model, tokenizer) print(question)
output:
['What is the capital of France?', 'What is the capital of Germany?', 'What is the capital of Spain?', 'Who is Robert?']
example 2 (generate multiple questions for a given context):
print("\r\n\r\n") context = "She walked to the store to buy a jug of milk." print("Context:\r\n", context) print("") questions = get_questions_for_context(context, model, tokenizer, num_count=15) # pretty print all the questions print("Generated Questions:") for question in questions: print(question) print("\r\n\r\n")
output:
Generated Questions: Where did she go to buy milk? What did she walk to the store to buy? Why did she walk to the store to buy milk? Why did she go to the store? Why did she go to the grocery store? What did she go to the store to buy? Where did the woman go to buy milk? Why did she go to the store to buy milk? What did she buy at the grocery store? Why did she walk to the store? What kind of milk did she buy at the store? Where did she walk to buy milk? What kind of milk did she buy? Where did she go to get milk? What did she buy at the store?
and if we wanted to answer those questions (ez pz):
# now generate an answer for a given question from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("deepset/tinyroberta-squad2") model = AutoModelForQuestionAnswering.from_pretrained("deepset/tinyroberta-squad2") def get_answer_for_question(question, context, model, tokenizer): inputs = tokenizer(question, context, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) answer_start_index = outputs.start_logits.argmax() answer_end_index = outputs.end_logits.argmax() predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] tokenizer.decode(predict_answer_tokens, skip_special_tokens=True) target_start_index = torch.tensor([14]) target_end_index = torch.tensor([15]) outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index) loss = outputs.loss answer = tokenizer.decode(predict_answer_tokens, skip_special_tokens=True) return answer print("Context:\r\n", context, "\r\n") for question in questions: # right pad the question to 60 characters question_text = question.ljust(50) answer = get_answer_for_question(question, context, model, tokenizer) print("Question: ", question_text, "Answer: ", answer)