Instructions to use mohammedaly22/t5-small-question-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mohammedaly22/t5-small-question-generation with Transformers:

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

pipe = pipeline("text-generation", model="mohammedaly22/t5-small-question-generation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("mohammedaly22/t5-small-question-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("mohammedaly22/t5-small-question-generation")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mohammedaly22/t5-small-question-generation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mohammedaly22/t5-small-question-generation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohammedaly22/t5-small-question-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mohammedaly22/t5-small-question-generation

SGLang

How to use mohammedaly22/t5-small-question-generation 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 "mohammedaly22/t5-small-question-generation" \
    --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": "mohammedaly22/t5-small-question-generation",
		"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 "mohammedaly22/t5-small-question-generation" \
        --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": "mohammedaly22/t5-small-question-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mohammedaly22/t5-small-question-generation with Docker Model Runner:
```
docker model run hf.co/mohammedaly22/t5-small-question-generation
```

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

t5-small-squad-qg-v2

This model is a fine-tuned version of t5-small on the SQuAD dataset. It achieves the following results on the evaluation set:

Loss: 1.6608
BLEU: 20.00
Rouge1: 47.69
Rouge2: 26.43
RougeL: 44.15
RougeLSum: 44.15
METEOR: 45.84
BertScore: 91.82

Model description

Intended uses & limitations

Define some useful functions for highlighting the answer in the paragraph and preparing the instruction prompt that will be fed to the model:

def highlight_answer(context, answer):
    context_splits = context.split(answer)
    
    text = ""
    for split in context_splits:
        text += split
        text += ' <h> '
        text += answer
        text += ' <h> '
        text += split
    
    return text

def prepare_instruction(answer_highlighted_context):
    instruction_prompt = f"""Generate a question whose answer is highlighted by <h> from the context delimited by the triple backticks.
    context:
    ```
    {answer_highlighted_context}
    ```
    """
    
    return instruction_prompt

Use the model as a Hugging Face Pipeline:

from transformers import pipeline

pipe = pipeline('text2text-generation', model='mohammedaly22/t5-small-squad-qg-v2')

context = """During the 2011–12 season, he set the La Liga and European records\
for most goals scored in a single season, while establishing himself as Barcelona's\
all-time top scorer. The following two seasons, Messi finished second for the Ballon\
d'Or behind Cristiano Ronaldo (his perceived career rival), before regaining his best\
form during the 2014–15 campaign, becoming the all-time top scorer in La Liga and \
leading Barcelona to a historic second treble, after which he was awarded a fifth \
Ballon d'Or in 2015. Messi assumed captaincy of Barcelona in 2018, and won a record \
sixth Ballon d'Or in 2019. Out of contract, he signed for French club Paris Saint-Germain\
in August 2021, spending two seasons at the club and winning Ligue 1 twice. Messi \
joined American club Inter Miami in July 2023, winning the Leagues Cup in August of that year.
"""

answer_highlighted_context = highlight_answer(context=context, answer='Inter Miami')
prompt = prepare_instruction(answer_highlighted_context)

This will be the final prompt:

Generate a question whose answer is highlighted by <h> from the context delimited by the triple backticks
context:
```During the 2011–12 season, he set the La Liga and European records\
for most goals scored in a single season, while establishing himself as Barcelona's\
all-time top scorer. The following two seasons, Messi finished second for the Ballon\
d'Or behind Cristiano Ronaldo (his perceived career rival), before regaining his best\
form during the 2014–15 campaign, becoming the all-time top scorer in La Liga and \
leading Barcelona to a historic second treble, after which he was awarded a fifth \
Ballon d'Or in 2015. Messi assumed captaincy of Barcelona in 2018, and won a record\
 sixth Ballon d'Or in 2019. Out of contract, he signed for French club Paris Saint-Germain\
in August 2021, spending two seasons at the club and winning Ligue 1 twice. Messi \
joined American club  <h> Inter Miami <h> in July 2023, winning the Leagues Cup in August of that year.```

Use the loaded pipeline to generate questions their answer is Inter Miami:

outputs = pipe(prompt, num_return_sequences=3, num_beams=5, num_beam_groups=5, diversity_penalty=1.0)
for output in outputs:
    print(output['generated_text'])

Result:

1. What club did Messi join in the 2023 season?
2. What was Messi's name of the club that won the Leagues Cup on July 20?
3. What club did Messi join in the Leagues Cup in July 2023?

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
2.6867	0.73	500	1.9647
2.0737	1.46	1000	1.8141
1.9364	2.19	1500	1.7515
1.8745	2.92	2000	1.7215
1.8282	3.65	2500	1.7042
1.803	4.38	3000	1.6913
1.7797	5.11	3500	1.6796
1.7592	5.84	4000	1.6749
1.7435	6.57	4500	1.6697
1.7427	7.3	5000	1.6667
1.7245	8.04	5500	1.6614
1.7211	8.77	6000	1.6621
1.7137	9.5	6500	1.6608

Framework versions

Transformers 4.38.1
Pytorch 2.1.2
Datasets 2.13.1
Tokenizers 0.15.2

Downloads last month: 13

Safetensors

Model size

60.5M params

Tensor type

F32

Model tree for mohammedaly22/t5-small-question-generation

Base model

google-t5/t5-small

Finetuned

(2284)

this model

Dataset used to train mohammedaly22/t5-small-question-generation

Space using mohammedaly22/t5-small-question-generation 1

Evaluation results

BLEU
self-reported

20.000
Rouge1
self-reported

47.690
Rouge2
self-reported

26.430
RougeL
self-reported

44.150
RougeLSum
self-reported

44.150
METEOR
self-reported

45.840
BertScore
self-reported

91.820