Fix: update example in readme

I typed bert in case of my model,

README.md

@@ -48,7 +48,7 @@ from datasets import load_dataset
 
 # Load IMDb dataset for binary classification
 dataset = load_dataset("imdb")
-tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+tokenizer = AutoTokenizer.from_pretrained("yash3056/Llama-3.2-1B-imdb")
 
 # Tokenize the dataset
 def preprocess(example):
@@ -57,7 +57,7 @@ def preprocess(example):
 tokenized_datasets = dataset.map(preprocess, batched=True)
 
 # Load model for binary classification (num_labels=2)
-model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
+model = AutoModelForSequenceClassification.from_pretrained("yash3056/Llama-3.2-1B-imdb", num_labels=2)
 
 # Training arguments
 training_args = TrainingArguments(
@@ -90,7 +90,7 @@ from datasets import load_dataset
 
 # Load AG News dataset for multi-class classification (4 labels)
 dataset = load_dataset("ag_news")
-tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+tokenizer = AutoTokenizer.from_pretrained("yash3056/Llama-3.2-1B-imdb")
 
 # Tokenize the dataset
 def preprocess(example):
@@ -99,7 +99,7 @@ def preprocess(example):
 tokenized_datasets = dataset.map(preprocess, batched=True)
 
 # Load model for multi-class classification (num_labels=4)
-model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=4)
+model = AutoModelForSequenceClassification.from_pretrained("yash3056/Llama-3.2-1B-imdb", num_labels=4)
 
 # Training arguments
 training_args = TrainingArguments(
@@ -149,8 +149,8 @@ Use the code below to get started with the model.
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
 # Load Model and tokenizers
-tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=n) #n is the number of labels in the code
+tokenizer = AutoTokenizer.from_pretrained("yash3056/Llama-3.2-1B-imdb")
+model = AutoModelForSequenceClassification.from_pretrained("yash3056/Llama-3.2-1B-imdb", num_labels=n) #n is the number of labels in the code
 ```