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Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io> Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from https://huggingface.co/boltuix/NeuroBERT-NER
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"""
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Example online usage of Pooling API for Named Entity Recognition (NER).
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Run `vllm serve <model> --runner pooling`
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to start up the server in vLLM. e.g.
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vllm serve boltuix/NeuroBERT-NER
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"""
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import argparse
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import requests
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import torch
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def post_http_request(prompt: dict, api_url: str) -> requests.Response:
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headers = {"User-Agent": "Test Client"}
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response = requests.post(api_url, headers=headers, json=prompt)
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return response
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--host", type=str, default="localhost")
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parser.add_argument("--port", type=int, default=8000)
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parser.add_argument("--model", type=str, default="boltuix/NeuroBERT-NER")
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return parser.parse_args()
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def main(args):
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from transformers import AutoConfig, AutoTokenizer
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api_url = f"http://{args.host}:{args.port}/pooling"
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model_name = args.model
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# Load tokenizer and config
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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config = AutoConfig.from_pretrained(model_name)
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label_map = config.id2label
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# Input text
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text = "Barack Obama visited Microsoft headquarters in Seattle on January 2025."
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prompt = {"model": model_name, "input": text}
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pooling_response = post_http_request(prompt=prompt, api_url=api_url)
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# Run inference
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output = pooling_response.json()["data"][0]
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logits = torch.tensor(output["data"])
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predictions = logits.argmax(dim=-1)
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inputs = tokenizer(text, return_tensors="pt")
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# Map predictions to labels
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tokens = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])
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labels = [label_map[p.item()] for p in predictions]
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assert len(tokens) == len(predictions)
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# Print results
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for token, label in zip(tokens, labels):
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if token not in tokenizer.all_special_tokens:
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print(f"{token:15} → {label}")
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if __name__ == "__main__":
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args = parse_args()
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main(args)
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