INNER CODE UNIT · Python
save
barissayil/SentimentAnalysis · analyzer.py:114
def save(self):
# Save model.
self.model.save_pretrained(save_directory=f"models/{self.output_dir}/")
# Save configuration.
self.config.save_pretrained(save_directory=f"models/{self.output_dir}/")
# Save tokenizer.
self.tokenizer.save_pretrained(save_directory=f"models/{self.output_dir}/")
# Classifies sentiment as positve or negative.
def classify_sentiment(self, text):
# Don't track gradient.
with torch.no_grad():
# Tokens are made up of CLS token, text converted to tokens, and SEP token.
tokens = ["[CLS]"] + self.tokenizer.tokenize(text) + ["[SEP]"]
# Convert tokens to input IDs; convert them to tensor, unsqueeze, put it to device.
input_ids = (
torch.tensor(self.tokenizer.convert_tokens_to_ids(tokens))
.unsqueeze(0)