Text Generation
Transformers
PyTorch
Safetensors
Turkish
mt5
text2text-generation
question-generation
answer-extraction
question-answering
Instructions to use obss/mt5-base-3task-highlight-tquad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use obss/mt5-base-3task-highlight-tquad2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="obss/mt5-base-3task-highlight-tquad2")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("obss/mt5-base-3task-highlight-tquad2") model = AutoModelForSeq2SeqLM.from_pretrained("obss/mt5-base-3task-highlight-tquad2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use obss/mt5-base-3task-highlight-tquad2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "obss/mt5-base-3task-highlight-tquad2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "obss/mt5-base-3task-highlight-tquad2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/obss/mt5-base-3task-highlight-tquad2
- SGLang
How to use obss/mt5-base-3task-highlight-tquad2 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 "obss/mt5-base-3task-highlight-tquad2" \ --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": "obss/mt5-base-3task-highlight-tquad2", "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 "obss/mt5-base-3task-highlight-tquad2" \ --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": "obss/mt5-base-3task-highlight-tquad2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use obss/mt5-base-3task-highlight-tquad2 with Docker Model Runner:
docker model run hf.co/obss/mt5-base-3task-highlight-tquad2
Fatih commited on
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README.md
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example_title: "Question Generation (History 2)"
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- text: "extract answers: Cenevizlilerin önemli üslerinden Amasra’yı aldı. <hl> 1479’da bir antlaşma yaparak Venedik'le 16 yıllık savaşa sona verdi. <hl>"
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example_title: "Answer Extraction (History)"
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- text: "question:
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example_title: "Question Answering (Open Domain)"
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license: cc-by-4.0
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---
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example_title: "Question Generation (History 2)"
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- text: "extract answers: Cenevizlilerin önemli üslerinden Amasra’yı aldı. <hl> 1479’da bir antlaşma yaparak Venedik'le 16 yıllık savaşa sona verdi. <hl>"
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example_title: "Answer Extraction (History)"
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- text: "question: Sunulan yöntemle ne yapılabilir? context: Çalışmada sunulan yöntemle, Türkçe metinlerden otomatik olarak soru ve cevap üretilebilir. Bu proje ile paylaşılan kaynak kodu ile Türkçe Soru Üretme / Soru Cevaplama konularında yeni akademik çalışmalar yapılabilir. Projenin detaylarına paylaşılan Github ve Arxiv linklerinden ulaşılabilir."
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license: cc-by-4.0
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