git clone https://github.com/siddharth-sharma7/fast-Bart
cd fast-Bart
pip install -r requirements.txt

from fastBart import export_and_get_onnx_model
from transformers import AutoTokenizer

model_name = 'facebook/bart-base'
model = export_and_get_onnx_model(model_name)

tokenizer = AutoTokenizer.from_pretrained(model_name)
input = "This is a very long sentence and needs to be summarized."
token = tokenizer(input, return_tensors='pt')

tokens = model.generate(input_ids=token['input_ids'],
               attention_mask=token['attention_mask'],
               num_beams=3)

output = tokenizer.decode(tokens.squeeze(), skip_special_tokens=True)
print(output)

from fastBart import (OnnxBart, get_onnx_runtime_sessions,
                    generate_onnx_representation, quantize)
from transformers import AutoTokenizer

model_or_model_path = 'facebook/bart-base'

# Step 1. convert huggingfaces bart model to onnx
onnx_model_paths = generate_onnx_representation(model_or_model_path)

# Step 2. (recommended) quantize the converted model for fast inference and to reduce model size.
# The process is slow for the decoder and init-decoder onnx files (can take up to 15 mins)
quant_model_paths = quantize(onnx_model_paths)

# step 3. setup onnx runtime
model_sessions = get_onnx_runtime_sessions(quant_model_paths)

# step 4. get the onnx model
model = OnnxBart(model_or_model_path, model_sessions)

                      ...

from fastBart import export_and_get_onnx_model, get_onnx_model

model_name = "facebook/bart-base"
custom_output_path = "/path/to/custom/folder/"

# 1. stores models to custom_output_path
model = export_and_get_onnx_model(model_name, custom_output_path)

# 2. run already exported models that are stored in custom path
# model = get_onnx_model(model_name, custom_output_path)