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))