train.py

# pip install transformers 
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForSequenceClassification

import torch
import torch.nn.functional as F

model_name = "distilbert-base-uncased-finetuned-sst-2-english"

model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

classifier = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
res = classifier(["We are very happy to show you the 🤗 Transformers Library", "We hope you don't hate it."])

#for result in res:
 #   print(res)

tokens = tokenizer.tokenize("We are very happy to show you the 🤗 Transformers Library")
token_ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = tokenizer("We are very happy to show you the 🤗 Transformers Library");

#print(f' Tokens: {tokens}')
#print(f'Token IDs: {token_ids}')
#print(f'Input IDs: {input_ids}')

x_train = ["We are very happy to show you the 🤗 Transformers Library", 
           "We hope you don't hate it."]

batch = tokenizer(x_train, padding=True, truncation=True, max_length=512, return_tensors="pt")

with torch.no_grad():
    outputs = model(**batch, labels=torch.tensor([1,0]))
    print(outputs)
    predictions = F.softmax(outputs.logits, dim=1)
    print(predictions)
    labels = torch.argmax(predictions, dim=1)
    print(labels)
    labels = [model.config.id2label[label_id] for label_id in labels.tolist()]
    print(labels)

save_directory = "saved"
tokenizer.save_pretrained(save_directory)
model.save_pretrained(save_directory)

tokenizer = AutoTokenizer.from_pretrained(save_directory)
model = AutoModelForSequenceClassification.from_pretrained(save_directory)