Not sure what is going on but I tried to run the model on 20 rows of my dataset (normal dataset really) it crashed once or twice but then ran , I tried running it on full dataset 200 rows but it keeps killing my kernel on Jupiter notebook. Even the most simple sentence can't be processed using this model now without crashing my kernel. Any leads?

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

Preprocess text (username and link placeholders)

def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
MODEL = f"cardiffnlp/twitter-roberta-base-sentiment-latest"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
config = AutoConfig.from_pretrained(MODEL)

PT

model = AutoModelForSequenceClassification.from_pretrained(MODEL)
#model.save_pretrained(MODEL)
text = "Covid cases are increasing fast!"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)

# TF

model = TFAutoModelForSequenceClassification.from_pretrained(MODEL)

model.save_pretrained(MODEL)

text = "Covid cases are increasing fast!"

encoded_input = tokenizer(text, return_tensors='tf')

output = model(encoded_input)

scores = output[0][0].numpy()

scores = softmax(scores)

Print labels and 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}) {l} {np.round(float(s), 4)}")