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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen3-0.6B |
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tags: |
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- event-data |
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- political-science |
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- computational-social-science |
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--- |
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# Example usage with vLLM |
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## Load the model and tokenizer |
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``` |
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from vllm import LLM, SamplingParams |
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from transformers import AutoTokenizer |
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model = LLM(model="ahalt/event-attribute-extractor", |
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enable_prefix_caching=True, |
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max_model_len=8000, |
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gpu_memory_utilization=0.80) |
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B") |
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sampling_params = SamplingParams( |
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temperature=0.5, # Greedy decoding breaks Qwen |
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top_p=0.8, # Qwen3 non-thinking recommendation |
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top_k=20, # Qwen3 recommendation |
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presence_penalty=1.5, # Recommended for quantized models |
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min_p=0.0, |
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#guided_decoding=guided_decoding_params, # Optionally, set a JSON schema for contrained decoding |
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max_tokens=1024, |
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) |
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``` |
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## Prompt setup |
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``` |
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system_content_short = """Extract political events as JSON. |
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OUTPUT FORMAT: |
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[ |
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{ |
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"event_type": "EVENT_TYPE", |
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"anchor_quote": "quote from text", |
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"actor": "who performed action OR N/A", |
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"recipient": "who was targeted OR N/A", |
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"date": "when occurred OR N/A", |
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"location": "where occurred OR N/A" |
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} |
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] |
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Return valid JSON only. Empty array [] if no events.""" |
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def make_prompt(doc, event_type, tokenizer): |
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messages = [ |
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{"role": "system", "content": system_content_short}, |
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{"role": "user", "content": f"## Document: {doc}\n\n## Event Type: {event_type}"}, |
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] |
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prompt = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True, |
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enable_thinking=False |
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) |
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return prompt |
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``` |
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## Example usage |
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``` |
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text = """KYIV, Ukraine (AP) — Ukraine’s anti-corruption agencies said they had uncovered a major graft scheme involving inflated military procurement contracts, just two days after Ukraine’s parliament voted to restore the agencies’ independence. |
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In a joint statement published Saturday on social media, the National Anti-Corruption Bureau (NABU) and the Specialized Anti-Corruption Prosecutor’s Office (SAPO) said the suspects had taken bribes in a scheme that used state funds to buy drones and other military equipment at inflated prices. |
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“The essence of the scheme was to conclude state contracts with supplier companies at deliberately inflated prices,” the statement said, adding that offenders had received kickbacks of up to 30% of the contracts’ value. |
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event_type = "Investigate, charge, or prosecute" |
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prompt = make_prompt(text, event_type, tokenizer) |
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output = model.generate(prompt, sampling_params=sampling_params) |
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response = output[0].outputs[0].text.strip() |
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[{"event_type": "Investigate, charge, or prosecute", |
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"anchor_quote": "Ukraine\u2019s anti-corruption agencies said they had uncovered a major graft scheme involving inflated military procurement contracts", |
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"actor": "National Anti-Corruption Bureau (NABU); Specialized Anti-Corruption Prosecutor\u2019s Office (SAPO)", |
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"recipient": "suspects involved in the scheme", |
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"date": "Saturday", |
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"location": "Ukraine"} |
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``` |
