Instructions to use research-backup/t5-large-squad-qg-no-answer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use research-backup/t5-large-squad-qg-no-answer with Transformers:

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

pipe = pipeline("text-generation", model="research-backup/t5-large-squad-qg-no-answer")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("research-backup/t5-large-squad-qg-no-answer")
model = AutoModelForSeq2SeqLM.from_pretrained("research-backup/t5-large-squad-qg-no-answer")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use research-backup/t5-large-squad-qg-no-answer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "research-backup/t5-large-squad-qg-no-answer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "research-backup/t5-large-squad-qg-no-answer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/research-backup/t5-large-squad-qg-no-answer

SGLang

How to use research-backup/t5-large-squad-qg-no-answer 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 "research-backup/t5-large-squad-qg-no-answer" \
    --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": "research-backup/t5-large-squad-qg-no-answer",
		"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 "research-backup/t5-large-squad-qg-no-answer" \
        --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": "research-backup/t5-large-squad-qg-no-answer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use research-backup/t5-large-squad-qg-no-answer with Docker Model Runner:
```
docker model run hf.co/research-backup/t5-large-squad-qg-no-answer
```

t5-large-squad-qg-no-answer / README.md

asahi417

model update

6cd7022 over 3 years ago

preview code

raw

history blame contribute delete

5.74 kB


	---
	license: cc-by-4.0
	metrics:
	- bleu4
	- meteor
	- rouge-l
	- bertscore
	- moverscore
	language: en
	datasets:
	- lmqg/qg_squad
	pipeline_tag: text2text-generation
	tags:
	- question generation
	widget:
	- text: "generate question: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>"
	example_title: "Question Generation Example 1"
	- text: "generate question: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>"
	example_title: "Question Generation Example 2"
	- text: "generate question: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records . <hl>"
	example_title: "Question Generation Example 3"
	model-index:
	- name: research-backup/t5-large-squad-qg-no-answer
	results:
	- task:
	name: Text2text Generation
	type: text2text-generation
	dataset:
	name: lmqg/qg_squad
	type: default
	args: default
	metrics:
	- name: BLEU4 (Question Generation)
	type: bleu4_question_generation
	value: 24.27
	- name: ROUGE-L (Question Generation)
	type: rouge_l_question_generation
	value: 51.3
	- name: METEOR (Question Generation)
	type: meteor_question_generation
	value: 25.67
	- name: BERTScore (Question Generation)
	type: bertscore_question_generation
	value: 90.41
	- name: MoverScore (Question Generation)
	type: moverscore_question_generation
	value: 63.97
	---

	# Model Card of `research-backup/t5-large-squad-qg-no-answer`
	This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
	This model is fine-tuned without answer information, i.e. generate a question only given a paragraph (note that normal model is fine-tuned to generate a question given a pargraph and an associated answer in the paragraph).

	### Overview
	- Language model: [t5-large](https://huggingface.co/t5-large)
	- Language: en
	- Training data: [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
	- Online Demo: [https://autoqg.net/](https://autoqg.net/)
	- Repository: [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
	- Paper: [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)

	### Usage
	- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
	```python
	from lmqg import TransformersQG

	# initialize model
	model = TransformersQG(language="en", model="research-backup/t5-large-squad-qg-no-answer")

	# model prediction
	questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")

	```

	- With `transformers`
	```python
	from transformers import pipeline

	pipe = pipeline("text2text-generation", "research-backup/t5-large-squad-qg-no-answer")
	output = pipe("generate question: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>")

	```

	## Evaluation


	- *Metric (Question Generation)*: [raw metric file](https://huggingface.co/research-backup/t5-large-squad-qg-no-answer/raw/main/eval/metric.first.sentence.paragraph_sentence.question.lmqg_qg_squad.default.json)

	\| \| Score \| Type \| Dataset \|
	\|:-----------\|--------:\|:--------\|:---------------------------------------------------------------\|
	\| BERTScore \| 90.41 \| default \| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) \|
	\| Bleu_1 \| 56.44 \| default \| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) \|
	\| Bleu_2 \| 40.29 \| default \| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) \|
	\| Bleu_3 \| 30.87 \| default \| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) \|
	\| Bleu_4 \| 24.27 \| default \| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) \|
	\| METEOR \| 25.67 \| default \| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) \|
	\| MoverScore \| 63.97 \| default \| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) \|
	\| ROUGE_L \| 51.3 \| default \| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) \|



	## Training hyperparameters

	The following hyperparameters were used during fine-tuning:
	- dataset_path: lmqg/qg_squad
	- dataset_name: default
	- input_types: ['paragraph_sentence']
	- output_types: ['question']
	- prefix_types: ['qg']
	- model: t5-large
	- max_length: 512
	- max_length_output: 32
	- epoch: 7
	- batch: 16
	- lr: 5e-05
	- fp16: False
	- random_seed: 1
	- gradient_accumulation_steps: 4
	- label_smoothing: 0.15

	The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/t5-large-squad-qg-no-answer/raw/main/trainer_config.json).

	## Citation
	```
	@inproceedings{ushio-etal-2022-generative,
	title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
	author = "Ushio, Asahi and
	Alva-Manchego, Fernando and
	Camacho-Collados, Jose",
	booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
	month = dec,
	year = "2022",
	address = "Abu Dhabi, U.A.E.",
	publisher = "Association for Computational Linguistics",
	}

	```