ADD t5_whisper_medical about ailia-models HOT 1 OPEN

# This is a very small notebook showing how to grab a pre-trained T5 model, fine-tune it, and export it to onnx.]
# A lot of this is inspired by huggingface.

from transformers import T5ForConditionalGeneration, T5Tokenizer, T5Config, AdamW
import torch
from onnxt5 import generate_onnx_representation, GenerativeT5
from onnxt5.api import get_sess
import tempfile

from transformers import AutoModelForSeq2SeqLM, T5Tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained("outputs/results/02/checkpoint")

tokenizer = T5Tokenizer.from_pretrained("outputs/results/02/checkpoint", legacy=False)

# Export to ONNX
generate_onnx_representation(output_prefix=f"./t5_whisper_medical", model=model)

# Load the model
decoder_sess, encoder_sess = get_sess(f"./t5_whisper_medical")
generative_t5 = GenerativeT5(encoder_sess, decoder_sess, tokenizer, onnx=True)
print(generative_t5("医療用語の訂正: こんにちは、先生。最近手足の経連があり、動機も感じます。", max_length=384, temperature=0.))
# Output: 1 <<< Positive