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---
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language: en
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datasets:
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- flax-sentence-embeddings/stackexchange_title_body_jsonl
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widget:
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- text: "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."
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license: apache-2.0
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---
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# doc2query/stackexchange-title-body-t5-base-v1
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This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
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It can be used for:
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- **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini.
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- **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models.
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## Usage
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```python
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from transformers import T5Tokenizer, T5ForConditionalGeneration
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model_name = 'doc2query/stackexchange-title-body-t5-base-v1'
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tokenizer = T5Tokenizer.from_pretrained(model_name)
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model = T5ForConditionalGeneration.from_pretrained(model_name)
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text = "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."
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input_ids = tokenizer.encode(text, max_length=320, truncation=True, return_tensors='pt')
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outputs = model.generate(
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input_ids=input_ids,
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max_length=64,
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do_sample=True,
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top_p=0.95,
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num_return_sequences=5)
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print("Text:")
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print(text)
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print("\nGenerated Queries:")
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for i in range(len(outputs)):
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query = tokenizer.decode(outputs[i], skip_special_tokens=True)
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print(f'{i + 1}: {query}')
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```
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**Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it.
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## Training
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This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 550k training steps. For the training script, see the `train_script.py` in this repository.
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The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces.
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This model was trained on a (title, question_body) from StackExchange.
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