Deepchecks/parrot_fluency_model_onnx

from transformers import AutoTokenizer
from optimum.onnxruntime import ORTModelForSequenceClassification
from optimum.pipelines import pipeline

# Cargar el tokenizador y los pesos del modelo
tokenizer = AutoTokenizer.from_pretrained('Deepchecks/parrot_fluency_model_onnx')
model = ORTModelForSequenceClassification.from_pretrained('Deepchecks/parrot_fluency_model_onnx')

# Preparar el pipeline y generar inferencias
user_inputs = ['Natural language processing is an interdisciplinary subfield of linguistics, computer science, and artificial intelligence.',
'Pass on what you have learned. Strength, mastery, hmm... but weakness, folly, failure, also. Yes, failure, most of all. The greatest teacher, failure is.',
'Whispering dreams, forgotten desires, chaotic thoughts, dance with words, meaning elusive, swirling amidst.']
pip = pipeline(task='text-classification', model=model, tokenizer=tokenizer, device=device, accelerator='ort')
res = pip(user_inputs, batch_size=64, truncation='only_first')