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from transformers import pipeline, 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(["I am very happy now.", "Not happy now."]) | |
for result in res: | |
print(result) | |
tokens = tokenizer.tokenize("I am very happy now.") | |
token_ids = tokenizer.convert_tokens_to_ids(tokens) | |
input_ids = tokenizer("I am very happy now.") | |
print(f'Tokens:{tokens}') | |
print(f'TokenIDs:{token_ids}') | |
print(f'InputIDs:{input_ids}') | |