Edit model card


This is a doc2query model based on mT5 (also known as docT5query).

It can be used for:

  • 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 paper we showed that BM25+docT5query is a powerful search engine. In the BEIR repository we have an example how to use docT5query with Pyserini.
  • Domain Specific Training Data Generation: It can be used to generate training data to learn an embedding model. In our GPL-Paper / GPL Example on SBERT.net 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.


from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_name = 'doc2query/msmarco-spanish-mt5-base-v1'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

text = "Python es un lenguaje de alto nivel de programación interpretado cuya filosofía hace hincapié en la legibilidad de su código, se utiliza para desarrollar aplicaciones de todo tipo, ejemplos: Instagram, Netflix, Panda 3D, entre otros.2​ Se trata de un lenguaje de programación multiparadigma, ya que soporta parcialmente la orientación a objetos, programación imperativa y, en menor medida, programación funcional. Es un lenguaje interpretado, dinámico y multiplataforma."

def create_queries(para):
    input_ids = tokenizer.encode(para, return_tensors='pt')
    with torch.no_grad():
        # Here we use top_k / top_k random sampling. It generates more diverse queries, but of lower quality
        sampling_outputs = model.generate(
        # Here we use Beam-search. It generates better quality queries, but with less diversity
        beam_outputs = model.generate(

    print("\nBeam Outputs:")
    for i in range(len(beam_outputs)):
        query = tokenizer.decode(beam_outputs[i], skip_special_tokens=True)
        print(f'{i + 1}: {query}')

    print("\nSampling Outputs:")
    for i in range(len(sampling_outputs)):
        query = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True)
        print(f'{i + 1}: {query}')


Note: model.generate() is non-deterministic for top_k/top_n sampling. It produces different queries each time you run it.


This model fine-tuned google/mt5-base for 66k training steps (4 epochs on the 500k training pairs from MS MARCO). For the training script, see the train_script.py in this repository.

The input-text was truncated to 320 word pieces. Output text was generated up to 64 word pieces.

This model was trained on a (query, passage) from the mMARCO dataset.

Downloads last month
Hosted inference API
Text2Text Generation
This model can be loaded on the Inference API on-demand.

Dataset used to train doc2query/msmarco-spanish-mt5-base-v1