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

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metadata

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 for question generation task on the lmqg/qg_squad (dataset_name: default) via lmqg. 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
Language: en
Training data: lmqg/qg_squad (default)
Online Demo: https://autoqg.net/
Repository: https://github.com/asahi417/lm-question-generation
Paper: https://arxiv.org/abs/2210.03992

Usage

With lmqg

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

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

	Score	Type	Dataset
BERTScore	90.41	default	lmqg/qg_squad
Bleu_1	56.44	default	lmqg/qg_squad
Bleu_2	40.29	default	lmqg/qg_squad
Bleu_3	30.87	default	lmqg/qg_squad
Bleu_4	24.27	default	lmqg/qg_squad
METEOR	25.67	default	lmqg/qg_squad
MoverScore	63.97	default	lmqg/qg_squad
ROUGE_L	51.3	default	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.

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",
}