Update README.md

README.md

@@ -24,6 +24,16 @@ pipeline =  TextClassificationPipeline(model=model, tokenizer=tokenizer)
 print(pipeline('du bist blöd.'))
 ```
 
+You can apply the pipeline on a data set.
+
+```python
+
+df['result'] = df['comment_text'].apply(lambda x: pipeline(x[:512])) #Cuts after max. legth of tokens for this model, which is 512 for this model.
+# Make two new columns out of the column "results", one with the label, one with the score.
+df['toxic_label'] = df['result'].str[0].str['label']
+df['score'] = df['result'].str[0].str['score']
+```
+
 
 ## Training