test_inference.py

import torch

import json
import typing as tp

import torch.nn.functional as F
from torch import Tensor

from datasets import ClassLabel
import transformers
from transformers import BertForSequenceClassification
from transformers import BertForSequenceClassification, AutoTokenizer
import numpy as np

tokenizer = AutoTokenizer.from_pretrained('adalbertojunior/distilbert-portuguese-cased', do_lower_case=False)

classes = ['pt','pt_br']
class_label = ClassLabel(names=classes)

def get_model():
    return BertForSequenceClassification.from_pretrained(
   './pt_br_model',
    num_labels = 2,
    output_attentions = False,
    output_hidden_states = False,
    )

model = get_model()
text = 'hello'
input_tensor = tokenizer(text, padding=True, truncation=True, max_length=256, add_special_tokens=True, return_tensors="pt")

logits=model(**input_tensor).logits
probabilities = F.softmax(logits, dim=1).flatten().tolist()
maxidx = np.argmax(probabilities)
print(classes[maxidx], probabilities[maxidx])