This module provides OpenNLP interface implementations for ONNX models using the onnxruntime dependency.
Important: This does not provide the ability to train models. Model training is done outside of OpenNLP. This code provides the ability to use ONNX models from OpenNLP.
Models used in the tests are available in the opennlp evaluation test data.
Export a Huggingface NER model to ONNX, e.g.:
python -m transformers.onnx --model=dslim/bert-base-NER --feature token-classification exported
Export a Huggingface classification (e.g. sentiment) model to ONNX, e.g.:
python -m transformers.onnx --model=nlptown/bert-base-multilingual-uncased-sentiment --feature sequence-classification exported
Convert a sentence vectors model to ONNX, e.g.:
Install dependencies:
python3 -m pip install optimum onnx onnxruntime
Convert the model:
from optimum.onnxruntime import ORTModelForFeatureExtraction from transformers import AutoTokenizer from pathlib import Path model_id="sentence-transformers/all-MiniLM-L6-v2" onnx_path = Path("onnx") # load vanilla transformers and convert to onnx model = ORTModelForFeatureExtraction.from_pretrained(model_id, from_transformers=True) tokenizer = AutoTokenizer.from_pretrained(model_id) # save onnx checkpoint and tokenizer model.save_pretrained(onnx_path) tokenizer.save_pretrained(onnx_path)