Update README.md

README.md

@@ -87,52 +87,52 @@ The following hyperparameters were used during training:
 ### Usage
 
 
-from transformers import AutoTokenizer, AutoModelForTokenClassification
-import torch
+-from transformers import AutoTokenizer, AutoModelForTokenClassification
+-import torch
 
-custom_id2label = {
+-custom_id2label = {
     0: "O", 1: "B-CARDINAL", 2: "I-CARDINAL", 3: "B-DATE", 4: "I-DATE",
     5: "B-EVENT", 6: "I-EVENT", 7: "B-GPE", 8: "I-GPE", 9: "B-LOC", 10: "I-LOC",
     11: "B-MONEY", 12: "I-MONEY", 13: "B-ORDINAL", 14: "B-ORG", 15: "I-ORG",
     16: "B-PERCENT", 17: "I-PERCENT", 18: "B-PERSON", 19: "I-PERSON",
     20: "B-TIME", 21: "I-TIME"
 }
-custom_label2id = {v: k for k, v in custom_id2label.items()}
+-custom_label2id = {v: k for k, v in custom_id2label.items()}
 
-model_name = "mustafoyev202/roberta-uz"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForTokenClassification.from_pretrained(model_name, num_labels=23)
+-model_name = "mustafoyev202/roberta-uz"
+-tokenizer = AutoTokenizer.from_pretrained(model_name)
+-model = AutoModelForTokenClassification.from_pretrained(model_name, num_labels=23)
 
-model.config.id2label = custom_id2label
-model.config.label2id = custom_label2id
+-model.config.id2label = custom_id2label
+-model.config.label2id = custom_label2id
 
-text = "Tesla kompaniyasi AQSHda joylashgan."
+-text = "Tesla kompaniyasi AQSHda joylashgan."
 
-tokens = tokenizer(text.split(), return_tensors="pt", is_split_into_words=True)
+-tokens = tokenizer(text.split(), return_tensors="pt", is_split_into_words=True)
 
-with torch.no_grad():
+-with torch.no_grad():
     logits = model(**tokens).logits
 
-predicted_token_class_ids = logits.argmax(-1).squeeze().tolist()
+-predicted_token_class_ids = logits.argmax(-1).squeeze().tolist()
 
-word_ids = tokens.word_ids()
-previous_word_id = None
-word_predictions = {}
+-word_ids = tokens.word_ids()
+-previous_word_id = None
+-word_predictions = {}
 
-for i, word_id in enumerate(word_ids):
+-for i, word_id in enumerate(word_ids):
     if word_id is not None:
         label = custom_id2label[predicted_token_class_ids[i]]
         if word_id != previous_word_id:  # New word
             word_predictions[word_id] = label
         previous_word_id = word_id
 
-words = text.split()  # Splitting for simplicity
-final_predictions = [(word, word_predictions.get(i, "O")) for i, word in enumerate(words)]
+-words = text.split()  # Splitting for simplicity
+-final_predictions = [(word, word_predictions.get(i, "O")) for i, word in enumerate(words)]
 
-print("Predictions:")
-for word, label in final_predictions:
+-print("Predictions:")
+-for word, label in final_predictions:
     print(f"{word}: {label}")
-    
-labels = torch.tensor([predicted_token_class_ids]).unsqueeze(0)  # Adjust dimensions
-loss = model(**tokens, labels=labels).loss
-print("\nLoss:", round(loss.item(), 2))
+
+-labels = torch.tensor([predicted_token_class_ids]).unsqueeze(0)  # Adjust dimensions
+-loss = model(**tokens, labels=labels).loss
+-print("\nLoss:", round(loss.item(), 2))