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anjelinejeline
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Model Details
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anjelinejeline
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Output Modalities
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unsloth/Qwen2.5-14B-InstructTRL (SFTTrainer)save_method="merged_16bit").q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_projadamw_8bit)The model was fine-tuned over 243 steps on a dataset of 1,000 samples. It demonstrated rapid convergence, hitting its optimal performance at step 207.
| Metric | Value |
|---|---|
| Initial training loss | 2.3895 |
| Best training loss | 0.0912 (Step 207) |
| Final training loss | 0.1506 |
| Total training steps | 243 |
The model uses a structured prompt to strictly enforce classification criteria, output schemas, and extraction rules.
markdown
instruction = """You are an epidemiologist classifying West Nile virus articles.Return ONE JSON object.Article:{article_text}---SCOPE RULE (VERY IMPORTANT):Only consider West Nile virus (WNV).If the article is about ANY other disease → classify as "other".---CLASSIFICATION RULES (apply in order):1) If there are NO human or animal infections mentioned(only mosquito traps/pools/surveillance, no infected humans/animals, research on climate change etc):→ label = "other"→ outbreak_detected = false→ species_affected = []→ STOP2) If the article is a surveillance report, statistics table, or seasonal summary:→ label = "epi_summary"→ outbreak_detected = false*CRITERIA*: This includes reports capturing multi-national data, seasonal comparisons, or tracking cumulative statistics across multiple countries.*EXAMPLE*: "WNV in Europe 2022: EU countries reported 292 cases across Italy (228), Greece (59), and Austria (2)." This is an epi_summary3) If there are confirmed or suspected active, localized spikes, unexpected cases, or emergency notifications:→ label = "outbreak_alert"→ outbreak_detected = true*CRITERIA*: Tone features real-time concern, unexpected increases, or immediate localized threats in a country/region.*EXAMPLE*: "In recent weeks, increasingly alarming news has spread about the increase in cases of West Nile Disease in our country. The cases reported in Italy by the National Reference Center for WND, at the Zooprophylactic Institute, have risen to 230..." This must be an outbreak_alert with outbreak_detected = true.1) If there are NO human or animal infections mentioned(only mosquito traps/pools/surveillance, no infected humans/animals):→ classify = "other"→ outbreak_detected = false→ species_affected = []→ STOP2) If the article is a surveillance report, statistics, or seasonal summary:→ classify = "epi_summary"3) If there are confirmed or suspected human/animal cases:→ classify = "outbreak_alert"---CRITICAL RULES:- outbreak_detected = true ONLY if the text explicitly mentions cases, infections or outbreak.- If only research, modelling, risk, or discussion → outbreak_detected = false- Mosquito-only positivity WITHOUT human/animal cases is ALWAYS "other" → outbreak_detected = false- ONLY extract information explicitly stated in the text.- Do NOT infer, assume, or generalize.- Do NOT guess countries or species if not explicitly mentioned.- If unsure, return empty list [] and false.- Return the ID of article and publication date.- You MUST extract and use the exact article_id provided in the input. Do not alter, shorten, or generate a new ID under any circumstances.- If no specific event date is mentioned in the text, set "event_date" to null.- Reason must be maximum 10 words, do not exceed 10 words under any circumstanceReturn ONLY valid JSON.JSON:"""
json
{"article_id": "string","label": "outbreak_alert" | "epi_summary" | "other","outbreak_detected": boolean,"countries": ["string"],"iso2_codes": ["string"],"species_affected": ["human" | "equid" | "bird" | ...],"event_date": "YYYY-MM-DD" | null,"reasoning": "string (max 10 words)"}
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "anjelinejeline/Qwen2.5-14B-Instruct-epi"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.float16,device_map="auto")prompt_template = """You are an epidemiologist classifying West Nile virus articles.Return ONE JSON object.Article:{article_text}---SCOPE RULE (VERY IMPORTANT):Only consider West Nile virus (WNV).If the article is about ANY other disease → classify as "other".---CLASSIFICATION RULES (apply in order):1) If there are NO human or animal infections mentioned(only mosquito traps/pools/surveillance, no infected humans/animals, research on climate change etc):→ label = "other"→ outbreak_detected = false→ species_affected = []→ STOP2) If the article is a surveillance report, statistics table, or seasonal summary:→ label = "epi_summary"→ outbreak_detected = false*CRITERIA*: This includes reports capturing multi-national data, seasonal comparisons, or tracking cumulative statistics across multiple countries.*EXAMPLE*: "WNV in Europe 2022: EU countries reported 292 cases across Italy (228), Greece (59), and Austria (2)." This is an epi_summary3) If there are confirmed or suspected active, localized spikes, unexpected cases, or emergency notifications:→ label = "outbreak_alert"→ outbreak_detected = true*CRITERIA*: Tone features real-time concern, unexpected increases, or immediate localized threats in a country/region.*EXAMPLE*: "In recent weeks, increasingly alarming news has spread about the increase in cases of West Nile Disease in our country. The cases reported in Italy by the National Reference Center for WND, at the Zooprophylactic Institute, have risen to 230..." This must be an outbreak_alert with outbreak_detected = true.1) If there are NO human or animal infections mentioned(only mosquito traps/pools/surveillance, no infected humans/animals):→ classify = "other"→ outbreak_detected = false→ species_affected = []→ STOP2) If the article is a surveillance report, statistics, or seasonal summary:→ classify = "epi_summary"3) If there are confirmed or suspected human/animal cases:→ classify = "outbreak_alert"---CRITICAL RULES:- outbreak_detected = true ONLY if the text explicitly mentions cases, infections or outbreak.- If only research, modelling, risk, or discussion → outbreak_detected = false- Mosquito-only positivity WITHOUT human/animal cases is ALWAYS "other" → outbreak_detected = false- ONLY extract information explicitly stated in the text.- Do NOT infer, assume, or generalize.- Do NOT guess countries or species if not explicitly mentioned.- If unsure, return empty list [] and false.- Return the ID of article and publication date.- You MUST extract and use the exact article_id provided in the input. Do not alter, shorten, or generate a new ID under any circumstances.- If no specific event date is mentioned in the text, set "event_date" to null.- Reason must be maximum 10 words, do not exceed 10 words under any circumstanceReturn ONLY valid JSON.JSON:"""article = """Seville, Spain - July 16, 2026. Health authorities in Andalusia have confirmed two new human cases of West Nile Virus in the province of Seville, resulting in one fatality. The regional health department reported that both individuals, residents of Coria del Río, were hospitalized last week after developing severe neurological symptoms. This brings the total number of localized infections in the province to three this month, sparking urgent vector control operations in the affected municipal zones."""formatted_prompt = prompt_template.format(article_text=article)inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda")outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.1)response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)print(response)
python
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This qwen2 model was trained 2x faster with Unsloth and Huggingface's TRL library.