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 location.

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)