Odstranit peft/full_model.py

peft/full_model.py

@@ -1,165 +0,0 @@
-import numpy as np
-import torch
-from datasets import load_dataset
-from sklearn.metrics import precision_recall_fscore_support, precision_recall_curve
-from transformers import (
-    AutoTokenizer,
-    AutoModelForSequenceClassification,
-    Trainer,
-    TrainingArguments,
-    set_seed
-)
-from peft import get_peft_model, LoraConfig, TaskType
-
-# Set seed for reproducibility
-set_seed(42)
-
-# Load dataset
-ds = load_dataset("TUKE-KEMT/hate_speech_slovak")
-train_dataset = ds['train']
-train_test_split = ds['train'].train_test_split(test_size=0.2, seed=42)
-val_dataset = train_test_split['test']
-train_dataset = train_test_split['train']
-test_dataset = ds['test']
-
-# Load tokenizer and base model
-model_name = "ApoTro/slovak-t5-small"
-tokenizer = AutoTokenizer.from_pretrained(model_name, force_download=True)
-model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
-
-# Apply LoRA tuning
-peft_config = LoraConfig(
-    task_type=TaskType.SEQ_CLS,
-    inference_mode=False,
-    r=8,
-    lora_alpha=32,
-    lora_dropout=0.1
-)
-model = get_peft_model(model, peft_config)
-
-def tokenize(batch):
-    return tokenizer(
-        batch["text"], 
-        padding="max_length", 
-        truncation=True, 
-        max_length=128
-    )
-
-def prepare_dataset(dataset):
-    dataset = dataset.map(tokenize, batched=True, remove_columns=["text"])
-    return dataset.rename_column("label", "labels")
-
-train_dataset = prepare_dataset(train_dataset)
-val_dataset = prepare_dataset(val_dataset)
-test_dataset = prepare_dataset(test_dataset)
-
-# Set device
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-# Define training arguments
-training_args = TrainingArguments(
-    output_dir="./hate_speech_model",
-    per_device_train_batch_size=8,
-    per_device_eval_batch_size=16,
-    learning_rate=3e-5,
-    num_train_epochs=7,
-    evaluation_strategy="epoch",
-    save_strategy="epoch",
-    load_best_model_at_end=True,
-    metric_for_best_model="f1",
-    greater_is_better=True,
-    warmup_steps=100,
-    weight_decay=0.01,
-    report_to="none",
-    seed=42,
-    logging_steps=10,
-    gradient_accumulation_steps=2,
-    lr_scheduler_type="cosine",
-    logging_dir='./logs',
-)
-
-def compute_metrics(pred):
-    logits = pred.predictions[0]
-    preds = logits.argmax(-1)
-    labels = pred.label_ids
-    precision, recall, f1, _ = precision_recall_fscore_support(
-        labels, preds, average='binary'
-    )
-    return {
-        'precision': precision,
-        'recall': recall,
-        'f1': f1
-    }
-
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    train_dataset=train_dataset,
-    eval_dataset=val_dataset,
-    compute_metrics=compute_metrics,
-)
-
-trainer.train()
-trainer.save_model("./hate_speech_model/best_model")
-
-# Reload fine-tuned model
-model = AutoModelForSequenceClassification.from_pretrained("./hate_speech_model/best_model")
-trainer = Trainer(
-    model=model,
-    args=training_args,
-    eval_dataset=val_dataset,
-    compute_metrics=compute_metrics,
-)
-
-def find_optimal_threshold(trainer, dataset):
-    predictions = trainer.predict(dataset)
-    logits = predictions.predictions
-    
-    if isinstance(logits, tuple):
-        logits = logits[0]
-    logits = torch.tensor(logits)
-    
-    probs = torch.nn.functional.softmax(logits, dim=-1)
-    positive_probs = probs[:, 1].numpy()
-    true_labels = predictions.label_ids
-    
-    precisions, recalls, thresholds = precision_recall_curve(true_labels, positive_probs)
-    f1_scores = 2 * (precisions * recalls) / (precisions + recalls + 1e-8)
-    
-    optimal_idx = np.argmax(f1_scores[:-1])
-    optimal_threshold = thresholds[optimal_idx]
-    
-    return optimal_threshold, precisions[optimal_idx], recalls[optimal_idx], f1_scores[optimal_idx]
-
-def evaluate_with_threshold(trainer, dataset, threshold=0.5):
-    predictions = trainer.predict(dataset)
-    logits = predictions.predictions
-    
-    if isinstance(logits, tuple):
-        logits = logits[0]
-    logits = torch.tensor(logits)
-    
-    probs = torch.nn.functional.softmax(logits, dim=-1)
-    predicted_labels = (probs[:, 1] > threshold).numpy().astype(int)
-    true_labels = predictions.label_ids
-    
-    precision, recall, f1, _ = precision_recall_fscore_support(
-        true_labels, predicted_labels, average='binary', zero_division=0
-    )
-    
-    return {
-        'precision': precision,
-        'recall': recall,
-        'f1': f1
-    }
-
-print("\n🔍 Finding optimal threshold...")
-optimal_threshold, best_precision, best_recall, best_f1 = find_optimal_threshold(trainer, val_dataset)
-print(f"✅ Optimal threshold: {optimal_threshold:.4f}")
-
-print("\n📊 Evaluating on the test set with the optimal threshold:")
-optimized_results = evaluate_with_threshold(trainer, test_dataset, threshold=optimal_threshold)
-print(f"🎯 Precision: {optimized_results['precision']:.4f}")
-print(f"🎯 Recall: {optimized_results['recall']:.4f}")
-print(f"🎯 F1-score: {optimized_results['f1']:.4f}")