|
---
|
|
language: fr
|
|
datasets:
|
|
- unicamp-dl/mmarco
|
|
widget:
|
|
- text: "Python (prononcé /pi.tɔ̃/) est un langage de programmation interprété, multi-paradigme et multiplateformes. Il favorise la programmation impérative structurée, fonctionnelle et orientée objet. Il est doté d'un typage dynamique fort, d'une gestion automatique de la mémoire par ramasse-miettes et d'un système de gestion d'exceptions ; il est ainsi similaire à Perl, Ruby, Scheme, Smalltalk et Tcl."
|
|
|
|
license: apache-2.0
|
|
---
|
|
|
|
# doc2query/msmarco-french-mt5-base-v1
|
|
|
|
This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on mT5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)).
|
|
|
|
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](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/beir-cellar/beir) 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](https://arxiv.org/abs/2112.07577) / [GPL Example on SBERT.net](https://www.sbert.net/examples/domain_adaptation/README.html#gpl-generative-pseudo-labeling) 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.
|
|
|
|
## Usage
|
|
```python
|
|
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
|
|
import torch
|
|
|
|
model_name = 'doc2query/msmarco-french-mt5-base-v1'
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
|
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
|
|
|
|
text = "Python (prononcé /pi.tɔ̃/) est un langage de programmation interprété, multi-paradigme et multiplateformes. Il favorise la programmation impérative structurée, fonctionnelle et orientée objet. Il est doté d'un typage dynamique fort, d'une gestion automatique de la mémoire par ramasse-miettes et d'un système de gestion d'exceptions ; il est ainsi similaire à Perl, Ruby, Scheme, Smalltalk et Tcl."
|
|
|
|
|
|
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(
|
|
input_ids=input_ids,
|
|
max_length=64,
|
|
do_sample=True,
|
|
top_p=0.95,
|
|
top_k=10,
|
|
num_return_sequences=5
|
|
)
|
|
|
|
# Here we use Beam-search. It generates better quality queries, but with less diversity
|
|
beam_outputs = model.generate(
|
|
input_ids=input_ids,
|
|
max_length=64,
|
|
num_beams=5,
|
|
no_repeat_ngram_size=2,
|
|
num_return_sequences=5,
|
|
early_stopping=True
|
|
)
|
|
|
|
|
|
print("Paragraph:")
|
|
print(para)
|
|
|
|
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}')
|
|
|
|
create_queries(text)
|
|
|
|
```
|
|
|
|
**Note:** `model.generate()` is non-deterministic for top_k/top_n sampling. It produces different queries each time you run it.
|
|
|
|
## Training
|
|
This model fine-tuned [google/mt5-base](https://huggingface.co/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](https://github.com/unicamp-dl/mMARCO).
|
|
|
|
|
|
|
|
|
