Text Generation
Transformers
Safetensors
Arabic
English
qwen2
propaganda-detection
persuasion-techniques
span-identification
explainability
lora
conversational
text-generation-inference
Instructions to use QCRI/ProBel-MTL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QCRI/ProBel-MTL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="QCRI/ProBel-MTL") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("QCRI/ProBel-MTL") model = AutoModelForCausalLM.from_pretrained("QCRI/ProBel-MTL", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use QCRI/ProBel-MTL with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QCRI/ProBel-MTL" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QCRI/ProBel-MTL", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QCRI/ProBel-MTL
- SGLang
How to use QCRI/ProBel-MTL 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 "QCRI/ProBel-MTL" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QCRI/ProBel-MTL", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "QCRI/ProBel-MTL" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QCRI/ProBel-MTL", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use QCRI/ProBel-MTL with Docker Model Runner:
docker model run hf.co/QCRI/ProBel-MTL
| Analyze the following text and determine whether it contains propaganda techniques. | |
| Propaganda techniques include manipulative language strategies such as: | |
| - Loaded_Language: emotionally charged words to influence perception | |
| - Name_Calling-Labeling: attaching negative labels to dismiss someone | |
| - Exaggeration-Minimisation: inflating or downplaying the significance of facts | |
| - Appeal_to_Fear-Prejudice: exploiting fears or prejudices | |
| - Causal_Oversimplification: reducing complex issues to simple cause-and-effect | |
| - Flag_Waving: exploiting national or patriotic sentiments | |
| - Questioning_the_Reputation: attacking credibility without evidence | |
| - Doubt: raising questions without providing solid evidence | |
| - Appeal_to_Authority: citing authority figures to bolster claims | |
| - Slogans: using catchy, brief phrases to simplify complex issues | |
| - And other techniques that aim to manipulate the audience's opinion | |
| Respond EXACTLY in this format (in English): | |
| Label: true | |
| Explanation: <your explanation of why this text is or is not propagandistic> | |
| OR | |
| Label: false | |
| Explanation: <your explanation> | |
| Notes: | |
| - Use "true" if the text contains ANY propaganda technique, "false" otherwise. | |
| - The Label MUST be exactly "true" or "false" (lowercase). | |
| - The Explanation should identify which specific techniques are used (if any) and why. | |
| - Analyze the text objectively, considering the language, framing, and intent. | |
| Text: "{TEXT}" |