Update README.md

README.md

@@ -33,9 +33,9 @@ A multilingual [XLM-R-based (100 languages)](https://huggingface.co/FacebookAI/x
 
 **Fine-tuning hyperparameters**
 
-num_train_epochs=3,
-train_batch_size=8,
-learning_rate=6e-6
+      num_train_epochs=3,
+      train_batch_size=8,
+      learning_rate=6e-6
 
 **Evaluation Results**
 Model agreement (accuracy) vs. Inter-annotator agreement (0 - no agreement; 100 - perfect agreement):
@@ -56,15 +56,15 @@ Class-specific model F1-scores:
 
 from transformers import AutoModelForSequenceClassification, TextClassificationPipeline, AutoTokenizer, AutoConfig
 
-MODEL = "classla/xlm-r-parlasent"
-tokenizer = AutoTokenizer.from_pretrained(MODEL)
-config = AutoConfig.from_pretrained(MODEL)
-model = AutoModelForSequenceClassification.from_pretrained(MODEL)
-
-pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True,
-task='sentiment_analysis', device=0, function_to_apply="none")
-pipe([
-"Thank you for using our model",
-"Grazie per aver utilizzato il nostro modello"
-"Hvala za uporabo našega modela"
-])
+    MODEL = "classla/xlm-r-parlasent"
+    tokenizer = AutoTokenizer.from_pretrained(MODEL)
+    config = AutoConfig.from_pretrained(MODEL)
+    model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+  
+    pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True,
+    task='sentiment_analysis', device=0, function_to_apply="none")
+    pipe([
+    "Thank you for using our model",
+    "Grazie per aver utilizzato il nostro modello"
+    "Hvala za uporabo našega modela"
+    ])