Instructions to use research-backup/t5-large-tweetqa-qag-np with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use research-backup/t5-large-tweetqa-qag-np with Transformers:

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

pipe = pipeline("text-generation", model="research-backup/t5-large-tweetqa-qag-np")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("research-backup/t5-large-tweetqa-qag-np")
model = AutoModelForSeq2SeqLM.from_pretrained("research-backup/t5-large-tweetqa-qag-np")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use research-backup/t5-large-tweetqa-qag-np 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-tweetqa-qag-np"
# 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-tweetqa-qag-np",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/research-backup/t5-large-tweetqa-qag-np

SGLang

How to use research-backup/t5-large-tweetqa-qag-np 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-tweetqa-qag-np" \
    --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-tweetqa-qag-np",
		"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-tweetqa-qag-np" \
        --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-tweetqa-qag-np",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use research-backup/t5-large-tweetqa-qag-np with Docker Model Runner:
```
docker model run hf.co/research-backup/t5-large-tweetqa-qag-np
```

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 Card of `research-backup/t5-large-tweetqa-qag-np`

This model is fine-tuned version of t5-large for question & answer pair generation task on the lmqg/qag_tweetqa (dataset_name: default) via lmqg. This model is fine-tuned without a task prefix.

Overview

Language model: t5-large
Language: en
Training data: lmqg/qag_tweetqa (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-tweetqa-qag-np")

# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")

With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "research-backup/t5-large-tweetqa-qag-np")
output = pipe("Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

Evaluation

Metric (Question & Answer Generation): raw metric file

	Score	Type	Dataset
BERTScore	90.95	default	lmqg/qag_tweetqa
Bleu_1	40.9	default	lmqg/qag_tweetqa
Bleu_2	28.27	default	lmqg/qag_tweetqa
Bleu_3	19.84	default	lmqg/qag_tweetqa
Bleu_4	14.14	default	lmqg/qag_tweetqa
METEOR	31.49	default	lmqg/qag_tweetqa
MoverScore	62.62	default	lmqg/qag_tweetqa
QAAlignedF1Score (BERTScore)	92.64	default	lmqg/qag_tweetqa
QAAlignedF1Score (MoverScore)	65.47	default	lmqg/qag_tweetqa
QAAlignedPrecision (BERTScore)	93.03	default	lmqg/qag_tweetqa
QAAlignedPrecision (MoverScore)	66.36	default	lmqg/qag_tweetqa
QAAlignedRecall (BERTScore)	92.27	default	lmqg/qag_tweetqa
QAAlignedRecall (MoverScore)	64.68	default	lmqg/qag_tweetqa
ROUGE_L	37.45	default	lmqg/qag_tweetqa

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qag_tweetqa
dataset_name: default
input_types: ['paragraph']
output_types: ['questions_answers']
prefix_types: None
model: t5-large
max_length: 256
max_length_output: 128
epoch: 16
batch: 16
lr: 0.0001
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",
}

Downloads last month: 20

Dataset used to train research-backup/t5-large-tweetqa-qag-np

Paper for research-backup/t5-large-tweetqa-qag-np

Generative Language Models for Paragraph-Level Question Generation

Paper • 2210.03992 • Published Oct 8, 2022

Evaluation results

BLEU4 (Question & Answer Generation) on lmqg/qag_tweetqa
self-reported

14.140
ROUGE-L (Question & Answer Generation) on lmqg/qag_tweetqa
self-reported

37.450
METEOR (Question & Answer Generation) on lmqg/qag_tweetqa
self-reported

31.490
BERTScore (Question & Answer Generation) on lmqg/qag_tweetqa
self-reported

90.950
MoverScore (Question & Answer Generation) on lmqg/qag_tweetqa
self-reported

62.620
QAAlignedF1Score-BERTScore (Question & Answer Generation) on lmqg/qag_tweetqa
self-reported

92.640
QAAlignedRecall-BERTScore (Question & Answer Generation) on lmqg/qag_tweetqa
self-reported

92.270
QAAlignedPrecision-BERTScore (Question & Answer Generation) on lmqg/qag_tweetqa
self-reported

93.030
QAAlignedF1Score-MoverScore (Question & Answer Generation) on lmqg/qag_tweetqa
self-reported

65.470
QAAlignedRecall-MoverScore (Question & Answer Generation) on lmqg/qag_tweetqa
self-reported

64.680
QAAlignedPrecision-MoverScore (Question & Answer Generation) on lmqg/qag_tweetqa
self-reported

66.360

Model Card of research-backup/t5-large-tweetqa-qag-np