| import time | |
| from transformers import AutoTokenizer, pipeline | |
| from optimum.onnxruntime import ORTModelForTokenClassification | |
| tokenizer = AutoTokenizer.from_pretrained("./bert-italian-ner-onnx-quantized-avx512") | |
| model = ORTModelForTokenClassification.from_pretrained("./bert-italian-ner-onnx-quantized-avx512") | |
| nerpipeline = pipeline('ner', model=model, tokenizer=tokenizer) | |
| text = "La sede storica della Olivetti è ad Ivrea" | |
| start_time = time.time() | |
| output = nerpipeline(text) | |
| print(f"--- {time.time() - start_time} seconds ---") | |
| print(output) |