Instructions to use jaeyong2/Dynamic_NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaeyong2/Dynamic_NER with Transformers:

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

pipe = pipeline("text-generation", model="jaeyong2/Dynamic_NER")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jaeyong2/Dynamic_NER")
model = AutoModelForCausalLM.from_pretrained("jaeyong2/Dynamic_NER")
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 jaeyong2/Dynamic_NER with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaeyong2/Dynamic_NER"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaeyong2/Dynamic_NER",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jaeyong2/Dynamic_NER

SGLang

How to use jaeyong2/Dynamic_NER 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 "jaeyong2/Dynamic_NER" \
    --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": "jaeyong2/Dynamic_NER",
		"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 "jaeyong2/Dynamic_NER" \
        --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": "jaeyong2/Dynamic_NER",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jaeyong2/Dynamic_NER with Docker Model Runner:
```
docker model run hf.co/jaeyong2/Dynamic_NER
```

jaeyong2 commited on Oct 25, 2025

Commit

67018b8

verified ·

1 Parent(s): bf14982

Update README.md

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README.md CHANGED Viewed

@@ -56,4 +56,60 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 <entities>
 [{'text': 'Tim', 'type': 'PERSON'}, {'text': 'mom', 'type': 'PERSON'}, {'text': 'Sue', 'type': 'PERSON'}, {'text': 'park', 'type': 'LOCATION'}, {'text': 'fountain', 'type': 'LOCATION'}, {'text': 'fish', 'type': 'ANIMAL'}]
 </entities>
 ```

 <entities>
 [{'text': 'Tim', 'type': 'PERSON'}, {'text': 'mom', 'type': 'PERSON'}, {'text': 'Sue', 'type': 'PERSON'}, {'text': 'park', 'type': 'LOCATION'}, {'text': 'fountain', 'type': 'LOCATION'}, {'text': 'fish', 'type': 'ANIMAL'}]
 </entities>
+```
+### examlpe (ko)
+```
+system = """
+You are an AI that dynamically performs Named Entity Recognition (NER).
+You receive a sentence and a list of entity types the user wants to extract, and then identify all entities of those types within the sentence.
+If you cannot find any suitable entities within the sentence, return an empty list.
+"""
+text = """
+수진이는 지난주 토요일에 스타필드 하남에 갔어요.
+그들은 애플 스토어에서 새로 나온 아이폰 16을 구경하고, 카페 노티드에서 도넛을 먹었어요.
+그날 저녁엔 방탄소년단 콘서트 실황 영화를 봤어요. 정말 신났죠!
+""".strip()
+named_entity = """
+[
+  {"type": "PERSON", "description": "사람 이름"},
+  {"type": "LOCATION", "description": "지명 또는 장소"},
+  {"type": "ORGANIZATION", "description": "조직, 회사, 단체"},
+  {"type": "PRODUCT", "description": "제품명"},
+  {"type": "WORK_OF_ART", "description": "예술 작품, 영화, 책, 노래 등"},
+  {"type": "DATE", "description": "날짜, 요일, 시점"}
+]
+""".strip()
+user = f"<sentence>\n{text}\n</sentence>\n\n<entity_list>\n{named_entity}\n</entity_list>\n\n"
+chat = [{"role":"system", "content":system}, {"role":"user", "content":user}]
+chat_text = tokenizer.apply_chat_template(
+            chat,
+            enable_thinking=False,
+            add_generation_prompt=True,
+            tokenize=False
+        )
+model_inputs = tokenizer([chat_text], return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+### result (ko)
+```
+<entities>
+[{'text': '수진이', 'type': 'PERSON'}, {'text': '스타필드 하남', 'type': 'LOCATION'}, {'text': '아이폰 16', 'type': 'PRODUCT'}, {'text': '방탄소년단', 'type': 'ORGANIZATION'}, {'text': '콘서트 실황 영화', 'type': 'WORK_OF_ART'}, {'text': '토요일', 'type': 'DATE'}, {'text': '카페 노티드', 'type': 'LOCATION'}]
+</entities>
 ```