Instructions to use Hezam/ArabicT5-49GB-small-classification-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hezam/ArabicT5-49GB-small-classification-generation with Transformers:

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

pipe = pipeline("text-generation", model="Hezam/ArabicT5-49GB-small-classification-generation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Hezam/ArabicT5-49GB-small-classification-generation")
model = AutoModelForSeq2SeqLM.from_pretrained("Hezam/ArabicT5-49GB-small-classification-generation", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hezam/ArabicT5-49GB-small-classification-generation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hezam/ArabicT5-49GB-small-classification-generation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hezam/ArabicT5-49GB-small-classification-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Hezam/ArabicT5-49GB-small-classification-generation

SGLang

How to use Hezam/ArabicT5-49GB-small-classification-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 "Hezam/ArabicT5-49GB-small-classification-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": "Hezam/ArabicT5-49GB-small-classification-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 "Hezam/ArabicT5-49GB-small-classification-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": "Hezam/ArabicT5-49GB-small-classification-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Hezam/ArabicT5-49GB-small-classification-generation with Docker Model Runner:
```
docker model run hf.co/Hezam/ArabicT5-49GB-small-classification-generation
```

ArabicT5-49GB-small-classification-generation / README.md

Hezam

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dc3180c over 2 years ago

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metadata

language:
  - ar
metrics:
  - Accuracy
library_name: transformers
pipeline_tag: text2text-generation
tags:
  - t5
  - text2text-generation
  - text2text
  - Classification and Generation
  - Classification
  - Generation
  - ArabicT5
  - Text Classification
  - Text2Text Generation
widget:
  - example_title: الرياضة
  - text: |
      أوقفوا القتل الجماعي في غزة

# ArabicT5 Model for Arabic News Classification and Generation

In this model focus on classifying and generating news Arabic.

# The number in the generated text represents the category of the news, as shown below:

category_mapping = {

  'Political':1,
  'Economy':2,
  'Health':3,
  'Sport':4,
  'Culture':5,
  'Technology':6,
  'Art':7,
  'Accidents':8

}

# Training parameters


Training batch size	`8`
Evaluation batch size	`8`
Learning rate	`1e-4`
Max length input	`64`
Max length target	`200`
Number workers	`4`
Epoch	`5`

# Results


Training Loss	`3.20`
Classification Accuracy	`95.7%`
Generation Accuracy	`88.87%`

# Example usage


from transformers import T5ForConditionalGeneration, T5Tokenizer, pipeline

model_name = "Hezam/ArabicT5-49GB-small-classification-generation"
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = T5Tokenizer.from_pretrained(model_name)
generation_pipeline = pipeline("text2text-generation",model=model,tokenizer=tokenizer)

text = "أوقفوا القتل الجماعي في غزة"

output= generation_pipeline(text,
                    num_beams=10,
                    max_length=200,
                    top_p=0.9,
                    repetition_penalty = 3.0,
                    no_repeat_ngram_size = 3)[0]["generated_text"]

output

category: 1 article: كتب عبد اللطيف صبح قال الرءيس الفلسطيني محمود عباس في تصريح ل اليوم السابع وقفوا القتل الجماعي في مدينه غزة مءكدا يجب يوقفوا قتل المدنيين العزل في قطاعي غزة والضفه وغزه واوقفوا القتل الجماع

bash
category: 1 article: كتب عبد اللطيف صبح قال الرءيس الفلسطيني محمود عباس في تصريح ل اليوم السابع وقفوا القتل الجماعي في مدينه غزة مءكدا يجب يوقفوا قتل المدنيين العزل في قطاعي غزة والضفه وغزه واوقفوا القتل الجماع