removed sample as it is more confusing than helpful

README.md

@@ -48,56 +48,6 @@ The code above will output following list:
 ["negative","negative","positive","positive","neutral", "neutral"]
 ```
 
-## A minimal working Sample
-
-
-```python
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-from typing import List
-import torch
-import re
-
-class 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 text
-
-texts = ["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))
-```
 
 ## Model and Data