Instructions to use ielabgroup/xor-tydi-docTquery-mt5-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ielabgroup/xor-tydi-docTquery-mt5-large with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ielabgroup/xor-tydi-docTquery-mt5-large")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("ielabgroup/xor-tydi-docTquery-mt5-large")
model = AutoModelForSeq2SeqLM.from_pretrained("ielabgroup/xor-tydi-docTquery-mt5-large")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ielabgroup/xor-tydi-docTquery-mt5-large with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ielabgroup/xor-tydi-docTquery-mt5-large"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ielabgroup/xor-tydi-docTquery-mt5-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ielabgroup/xor-tydi-docTquery-mt5-large

SGLang

How to use ielabgroup/xor-tydi-docTquery-mt5-large with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ielabgroup/xor-tydi-docTquery-mt5-large" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ielabgroup/xor-tydi-docTquery-mt5-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ielabgroup/xor-tydi-docTquery-mt5-large" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ielabgroup/xor-tydi-docTquery-mt5-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ielabgroup/xor-tydi-docTquery-mt5-large with Docker Model Runner:
```
docker model run hf.co/ielabgroup/xor-tydi-docTquery-mt5-large
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Model description

mT5-large query generation model that is trained with XOR QA data.

Used in paper Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation

and Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval

How to use

from transformers import pipeline

lang2mT5 = dict(
    ar='Arabic',
    bn='Bengali',
    fi='Finnish',
    ja='Japanese',
    ko='Korean',
    ru='Russian',
    te='Telugu'
)
PROMPT = 'Generate a {lang} question for this passage: {title} {passage}'

title = 'Transformer (machine learning model)'
passage = 'A transformer is a deep learning model that adopts the mechanism of self-attention, differentially ' \
          'weighting the significance of each part of the input (which includes the recursive output) data.'


model_name_or_path = 'ielabgroup/xor-tydi-docTquery-mt5-large'
input_text = PROMPT.format_map({'lang': lang2mT5['ja'],
                                'title': title,
                                'passage': passage})

generator = pipeline(model=model_name_or_path,
                     task='text2text-generation',
                     device="cuda:0",
                     )

results = generator(input_text,
                    do_sample=True,
                    max_length=64,
                    num_return_sequences=10,
                    )

for i, result in enumerate(results):
    print(f'{i + 1}. {result["generated_text"]}')

BibTeX entry and citation info

@article{zhuang2022bridging,
  title={Bridging the gap between indexing and retrieval for differentiable search index with query generation},
  author={Zhuang, Shengyao and Ren, Houxing and Shou, Linjun and Pei, Jian and Gong, Ming and Zuccon, Guido and Jiang, Daxin},
  journal={arXiv preprint arXiv:2206.10128},
  year={2022}
}

@inproceedings{zhuang2023augmenting,
    title={Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval},
    author={Zhuang, Shengyao and Shou, Linjun and Zuccon, Guido},
    booktitle={Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval},
    year={2023}
}

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Safetensors

Model size

1B params

Tensor type

F32

Papers for ielabgroup/xor-tydi-docTquery-mt5-large

Augmenting Passage Representations with Query Generation for Enhanced Cross-Lingual Dense Retrieval

Paper • 2305.03950 • Published May 6, 2023

Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation

Paper • 2206.10128 • Published Jun 21, 2022