Update README.md

README.md

@@ -72,7 +72,7 @@ library_name: transformers
 
 ## Overview
 
-`BERT-Lite` is an **ultra-lightweight** NLP model derived from **google/bert-base-uncased**, optimized for **real-time inference** on **edge and IoT devices**. With a quantized size of **~10MB** and **~2M parameters**, it delivers efficient contextual language understanding for highly resource-constrained environments like microcontrollers, wearables, and smart home devices. Designed for **low-latency** and **offline operation**, BERT-Lite is perfect for privacy-first applications requiring intent detection, text classification, or semantic understanding with minimal connectivity.
+`BERT-Lite` is an **ultra-lightweight** NLP model derived from **google/bert_uncased_L-2_H-64_A-2**, optimized for **real-time inference** on **edge and IoT devices**. With a quantized size of **~10MB** and **~2M parameters**, it delivers efficient contextual language understanding for highly resource-constrained environments like microcontrollers, wearables, and smart home devices. Designed for **low-latency** and **offline operation**, BERT-Lite is perfect for privacy-first applications requiring intent detection, text classification, or semantic understanding with minimal connectivity.
 
 - **Model Name**: BERT-Lite
 - **Size**: ~10MB (quantized)
@@ -312,82 +312,80 @@ To adapt BERT-Lite for custom IoT tasks (e.g., specific smart home commands):
 1. **Prepare Dataset**: Collect labeled data (e.g., commands with intents or masked sentences).
 2. **Fine-Tune with Hugging Face**:
    ```python
-     # !pip install transformers datasets torch --upgrade
-
-        import torch
-        from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
-        from datasets import Dataset
-        import pandas as pd
-
-        # 1. Prepare the sample IoT dataset
-        data = {
-            "text": [
-                "Turn on the fan",
-                "Switch off the light",
-                "Invalid command",
-                "Activate the air conditioner",
-                "Turn off the heater",
-                "Gibberish input"
-            ],
-            "label": [1, 1, 0, 1, 1, 0]  # 1 = Valid command, 0 = Invalid
-        }
-        df = pd.DataFrame(data)
-        dataset = Dataset.from_pandas(df)
-
-        # 2. Load tokenizer and model
-        model_name = "boltuix/bert-lite"  # Replace with any small/quantized BERT
-        tokenizer = BertTokenizer.from_pretrained(model_name)
-        model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)
-
-        # 3. Tokenize the dataset
-        def tokenize_function(examples):
-            return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=64)
-
-        tokenized_dataset = dataset.map(tokenize_function, batched=True)
-
-        # 4. Manually convert columns to tensors (NumPy 2.0 safe)
-        tokenized_dataset = tokenized_dataset.map(lambda x: {
-            "input_ids": torch.tensor(x["input_ids"]),
-            "attention_mask": torch.tensor(x["attention_mask"]),
-            "label": torch.tensor(x["label"])
-        })
-
-        # 5. Define training arguments
-        training_args = TrainingArguments(
-            output_dir="./bert_lite_results",
-            num_train_epochs=5,
-            per_device_train_batch_size=2,
-            logging_dir="./bert_lite_logs",
-            logging_steps=10,
-            save_steps=100,
-            eval_strategy="no",
-            learning_rate=5e-5,
-        )
-
-        # 6. Initialize Trainer
-        trainer = Trainer(
-            model=model,
-            args=training_args,
-            train_dataset=tokenized_dataset,
-        )
-
-        # 7. Fine-tune the model
-        trainer.train()
-
-        # 8. Save the fine-tuned model
-        model.save_pretrained("./fine_tuned_bert_lite")
-        tokenizer.save_pretrained("./fine_tuned_bert_lite")
-
-        # 9. Inference example
-        text = "Turn on the light"
-        inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=64)
-        model.eval()
-        with torch.no_grad():
-            outputs = model(**inputs)
-            logits = outputs.logits
-            predicted_class = torch.argmax(logits, dim=1).item()
-
-        print(f"Predicted class for '{text}': {'✅ Valid IoT Command' if predicted_class == 1 else '❌ Invalid Command'}")
+       import torch
+    from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
+    from datasets import Dataset
+    import pandas as pd
+
+    # 1. Prepare the sample IoT dataset
+    data = {
+        "text": [
+            "Turn on the fan",
+            "Switch off the light",
+            "Invalid command",
+            "Activate the air conditioner",
+            "Turn off the heater",
+            "Gibberish input"
+        ],
+        "label": [1, 1, 0, 1, 1, 0]  # 1 = Valid command, 0 = Invalid
+    }
+    df = pd.DataFrame(data)
+    dataset = Dataset.from_pandas(df)
+
+    # 2. Load tokenizer and model
+    model_name = "boltuix/bert-lite"  # Replace with any small/quantized BERT
+    tokenizer = BertTokenizer.from_pretrained(model_name)
+    model = BertForSequenceClassification.from_pretrained(model_name, num_labels=2)
+
+    # 3. Tokenize the dataset
+    def tokenize_function(examples):
+        return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=64)
+
+    tokenized_dataset = dataset.map(tokenize_function, batched=True)
+
+    # 4. Manually convert columns to tensors (NumPy 2.0 safe)
+    tokenized_dataset = tokenized_dataset.map(lambda x: {
+        "input_ids": torch.tensor(x["input_ids"]),
+        "attention_mask": torch.tensor(x["attention_mask"]),
+        "label": torch.tensor(x["label"])
+    })
+
+    # 5. Define training arguments
+    training_args = TrainingArguments(
+        output_dir="./bert_lite_results",
+        num_train_epochs=5,
+        per_device_train_batch_size=2,
+        logging_dir="./bert_lite_logs",
+        logging_steps=10,
+        save_steps=100,
+        eval_strategy="no",
+        learning_rate=5e-5,
+    )
+
+    # 6. Initialize Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_dataset,
+    )
+
+    # 7. Fine-tune the model
+    trainer.train()
+
+    # 8. Save the fine-tuned model
+    model.save_pretrained("./fine_tuned_bert_lite")
+    tokenizer.save_pretrained("./fine_tuned_bert_lite")
+
+    # 9. Inference example
+    text = "Turn on the light"
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=64)
+    model.eval()
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        predicted_class = torch.argmax(logits, dim=1).item()
+
+    print(f"Predicted class for '{text}': {'✅ Valid IoT Command' if predicted_class == 1 else '❌ Invalid Command'}")
    ```
 3. **Deploy**: Export the fine-tuned model to ONNX or TensorFlow Lite for edge devices.