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--- |
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license: apache-2.0 |
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datasets: |
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- squad |
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language: |
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- en |
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--- |
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# Question Generator |
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This model should be used to generate questions based on a given string. |
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### Out-of-Scope Use |
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English language support only. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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import torch |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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def question_parser(question: str) -> str: |
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return " ".join(question.split(":")[1].split()) |
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def generate_questions_v2(context: str, answer: str, n_questions: int = 1): |
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model = T5ForConditionalGeneration.from_pretrained( |
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"pipesanma/chasquilla-question-generator" |
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) |
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tokenizer = T5Tokenizer.from_pretrained("pipesanma/chasquilla-question-generator") |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = model.to(device) |
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text = "context: " + context + " " + "answer: " + answer + " </s>" |
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encoding = tokenizer.encode_plus( |
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text, max_length=512, padding=True, return_tensors="pt" |
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) |
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input_ids, attention_mask = encoding["input_ids"].to(device), encoding[ |
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"attention_mask" |
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].to(device) |
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model.eval() |
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beam_outputs = model.generate( |
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input_ids=input_ids, |
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attention_mask=attention_mask, |
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max_length=72, |
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early_stopping=True, |
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num_beams=5, |
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num_return_sequences=n_questions, |
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) |
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questions = [] |
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for beam_output in beam_outputs: |
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sent = tokenizer.decode( |
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beam_output, skip_special_tokens=True, clean_up_tokenization_spaces=True |
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) |
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print(sent) |
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questions.append(question_parser(sent)) |
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return questions |
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context = "President Donald Trump said and predicted that some states would reopen this month." |
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answer = "Donald Trump" |
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questions = generate_questions_v2(context, answer, 1) |
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print(questions) |
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``` |
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## Training Details |
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### Dataset generation |
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The dataset is "squad" from datasets library. |
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Check the [utils/dataset_gen.py](utils/dataset_gen.py) file for the dataset generation. |
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### Training model |
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Check the [utils/t5_train_model.py](utils/t5_train_model.py) file for the training process |
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### Model and Tokenizer versions |
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(v1.0) Model and Tokenizer V1: trained with 1000 rows |
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(v1.1) Model and Tokenizer V2: trained with 3000 rows |
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(v1.2) Model and Tokenizer V3: trained with all rows from datasets (78664 rows-train, 9652 rows-validation) |
