| # Query Generation | |
| This model is the t5-base model from [docTTTTTquery](https://github.com/castorini/docTTTTTquery). | |
| The T5-base model was trained on the [MS MARCO Passage Dataset](https://github.com/microsoft/MSMARCO-Passage-Ranking), which consists of about 500k real search queries from Bing together with the relevant passage. | |
| The model can be used for query generation to learn semantic search models without requiring annotated training data: [Synthetic Query Generation](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/query_generation). | |
| ## Usage | |
| ```python | |
| from transformers import T5Tokenizer, T5ForConditionalGeneration | |
| tokenizer = T5Tokenizer.from_pretrained('model-name') | |
| model = T5ForConditionalGeneration.from_pretrained('model-name') | |
| para = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." | |
| input_ids = tokenizer.encode(para, return_tensors='pt') | |
| outputs = model.generate( | |
| input_ids=input_ids, | |
| max_length=64, | |
| do_sample=True, | |
| top_p=0.95, | |
| num_return_sequences=3) | |
| print("Paragraph:") | |
| print(para) | |
| print("\nGenerated Queries:") | |
| for i in range(len(outputs)): | |
| query = tokenizer.decode(outputs[i], skip_special_tokens=True) | |
| print(f'{i + 1}: {query}') | |
| ``` |