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4464723f22
Signed-off-by: wang.yuqi <noooop@126.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
55 lines
1.5 KiB
Python
55 lines
1.5 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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from argparse import Namespace
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from vllm import LLM, EngineArgs
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from vllm.utils.argparse_utils import FlexibleArgumentParser
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def parse_args():
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parser = FlexibleArgumentParser()
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parser = EngineArgs.add_cli_args(parser)
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# Set example specific arguments
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parser.set_defaults(
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model="boltuix/NeuroBERT-NER",
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runner="pooling",
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enforce_eager=True,
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trust_remote_code=True,
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)
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return parser.parse_args()
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def main(args: Namespace):
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# Sample prompts.
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prompts = [
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"Barack Obama visited Microsoft headquarters in Seattle on January 2025."
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]
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# Create an LLM.
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llm = LLM(**vars(args))
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tokenizer = llm.get_tokenizer()
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label_map = llm.llm_engine.vllm_config.model_config.hf_config.id2label
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# Run inference
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outputs = llm.encode(prompts, pooling_task="token_classify")
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for prompt, output in zip(prompts, outputs):
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logits = output.outputs.data
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predictions = logits.argmax(dim=-1)
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# Map predictions to labels
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tokens = tokenizer.convert_ids_to_tokens(output.prompt_token_ids)
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labels = [label_map[p.item()] for p in 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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