I'm trying to use german bert sentiment analysis on Jupyter Notebook. I have installed pytorch correctly but the Kernel keeps dying. I'm on a MacBook Pro '21 with MacOs Monterey 12.3.1. I've installed Python 10.3.4. PyTorch does not show up in the list of installed packages on that environment even though in the terminal it tells me that the 'requirement is already satisfied'.
I tried to run the example code postet on hugging face.https://huggingface.co/oliverguhr/german-sentiment-bert
from transformers import AutoModelForSequenceClassification, AutoTokenizerfrom typing import Listimport torchimport reclass SentimentModel(): def __init__(self, model_name: str): self.model = AutoModelForSequenceClassification.from_pretrained(model_name) self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.clean_chars = re.compile(r'[^A-Za-züöäÖÜÄß ]', re.MULTILINE) self.clean_http_urls = re.compile(r'https*\\S+', re.MULTILINE) self.clean_at_mentions = re.compile(r'@\\S+', re.MULTILINE) def predict_sentiment(self, texts: List[str])-> List[str]: texts = [self.clean_text(text) for text in texts] # Add special tokens takes care of adding [CLS], [SEP], <s>... tokens in the right way for each model. encoded = self.tokenizer.batch_encode_plus(texts,padding=True, add_special_tokens=True,truncation=True, return_tensors="pt") encoded = encoded.to(self.device) with torch.no_grad(): logits = self.model(**encoded) label_ids = torch.argmax(logits[0], axis=1) return [self.model.config.id2label[label_id.item()] for label_id in label_ids] def replace_numbers(self,text: str) -> str: return text.replace("0"," null").replace("1"," eins").replace("2"," zwei").replace("3"," drei").replace("4"," vier").replace("5"," fünf").replace("6"," sechs").replace("7"," sieben").replace("8"," acht").replace("9"," neun") def clean_text(self,text: str)-> str: text = text.replace("\n", " ") text = self.clean_http_urls.sub('',text) text = self.clean_at_mentions.sub('',text) text = self.replace_numbers(text) text = self.clean_chars.sub('', text) # use only text chars text = ''.join(text.split()) # substitute multiple whitespace with single whitespace text = text.strip().lower() return texttexts = ["Mit keinem guten Ergebniss","Das war unfair", "Das ist gar nicht mal so gut","Total awesome!","nicht so schlecht wie erwartet", "Das ist gar nicht mal so schlecht","Der Test verlief positiv.","Sie fährt ein grünes Auto.", "Der Fall wurde an die Polzei übergeben."]model = SentimentModel(model_name = "oliverguhr/german-sentiment-bert")print(model.predict_sentiment(texts))