Delete spam_detection_pipeline.md

spam_detection_pipeline.md

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-
-# Spam Detection using DistilBERT and Quantization
-
-## 🛠 Install Dependencies
-
-```bash
-!pip install transformers datasets evaluate scikit-learn torch
-!pip install evaluate
-```
-
-## 📥 Step 1: Load and Reduce Dataset
-
-```python
-from datasets import load_dataset
-dataset = load_dataset("yelp_polarity")
-dataset["train"] = dataset["train"].shuffle(seed=42).select(range(50000))
-dataset["test"] = dataset["test"].shuffle(seed=42).select(range(10000))
-```
-
-## ✂️ Step 2: Tokenization
-
-```python
-from transformers import AutoTokenizer
-model_name = "distilbert-base-uncased"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-def tokenize_function(example):
-    return tokenizer(example["text"], padding="max_length", truncation=True)
-
-tokenized_datasets = dataset.map(tokenize_function, batched=True)
-```
-
-## 🏷 Step 3: Rename 'label' to 'labels' and Set Format
-
-```python
-tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
-tokenized_datasets.set_format("torch", columns=["input_ids", "attention_mask", "labels"])
-```
-
-## 🧠 Step 4: Load Model
-
-```python
-from transformers import AutoModelForSequenceClassification
-model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
-```
-
-## 📊 Step 5: Define Metrics
-
-```python
-import numpy as np
-from sklearn.metrics import accuracy_score, precision_recall_fscore_support
-
-def compute_metrics(eval_pred):
-    logits, labels = eval_pred
-    preds = np.argmax(logits, axis=-1)
-    acc = accuracy_score(labels, preds)
-    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average="binary")
-    return {"accuracy": acc, "precision": precision, "recall": recall, "f1": f1}
-```
-
-## ⚙️ Step 6: Training Setup
-
-```python
-from transformers import TrainingArguments, Trainer
-
-training_args = TrainingArguments(
-    output_dir="./results",
-    eval_strategy="epoch",
-    learning_rate=2e-5,
-    per_device_train_batch_size=16,
-    per_device_eval_batch_size=16,
-    num_train_epochs=3,
-    weight_decay=0.01,
-    logging_dir="./logs",
-    logging_steps=10,
-)
-
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=tokenized_datasets["train"],
-    eval_dataset=tokenized_datasets["test"],
-    compute_metrics=compute_metrics,
-)
-```
-
-## 🚀 Step 7: Train
-
-```python
-trainer.train()
-trainer.save_model("./results")
-tokenizer.save_pretrained("./results")
-```
-
-## 🔍 Step 8: Inference on Sample Texts
-
-```python
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-model = AutoModelForSequenceClassification.from_pretrained("./results")
-tokenizer = AutoTokenizer.from_pretrained("./results")
-model.eval()
-
-sample_texts = [
-    "The food was absolutely wonderful!",
-    "Terrible experience. I will never come back.",
-    "Average service, but the food was decent.",
-    "I loved the ambiance and the staff was super friendly!",
-    "Worst food I've had in a long time.",
-    "Highly recommend this place for a date night.",
-    "The waiter was rude and the food was cold.",
-    "Amazing pizza, will order again!",
-    "They took too long to serve and it was overpriced.",
-    "Best customer service and delicious desserts!"
-]
-
-for text in sample_texts:
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
-    with torch.no_grad():
-        outputs = model(**inputs)
-        prediction = torch.argmax(outputs.logits, dim=-1).item()
-        sentiment = "Positive" if prediction == 1 else "Negative"
-    print(f"Text: {text}\nPredicted Sentiment: {sentiment}\n")
-```
-
-## 📦 Step 9: Quantize the Model
-
-```python
-import os
-import torch
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-model = AutoModelForSequenceClassification.from_pretrained("./results")
-
-quantized_model = torch.quantization.quantize_dynamic(
-    model,
-    {torch.nn.Linear},
-    dtype=torch.qint8
-)
-
-quantized_model_path = "./results/quantized_model"
-os.makedirs(quantized_model_path, exist_ok=True)
-
-torch.save(quantized_model.state_dict(), f"{quantized_model_path}/pytorch_model.bin")
-model.config.save_pretrained(quantized_model_path)
-tokenizer = AutoTokenizer.from_pretrained("./results")
-tokenizer.save_pretrained(quantized_model_path)
-
-print("✅ Quantized model saved at:", quantized_model_path)
-```