ruanchaves/bert-large-portuguese-cased-hatebr

from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
import numpy as np
import torch
from scipy.special import softmax

model_name = "ruanchaves/bert-large-portuguese-cased-hatebr"
s1 = "Quem não deve não teme!!"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
config = AutoConfig.from_pretrained(model_name)
model_input = tokenizer(*([s1],), padding=True, return_tensors="pt")
with torch.no_grad():
    output = model(**model_input)
    scores = output[0][0].detach().numpy()
    scores = softmax(scores)
    ranking = np.argsort(scores)
    ranking = ranking[::-1]
    for i in range(scores.shape[0]):
        l = config.id2label[ranking[i]]
        s = scores[ranking[i]]
        print(f"{i+1}) Label: {l} Score: {np.round(float(s), 4)}")