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import os
import torch
import pandas as pd
import numpy as np
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
Trainer,
TrainingArguments,
DataCollatorWithPadding
)
from datasets import Dataset, load_from_disk
from sklearn.metrics import accuracy_score, f1_score
from sklearn.utils.class_weight import compute_class_weight
from tqdm import tqdm
# Set paths
RAW_CSV = "data.csv"
CACHE_DIR = "./cached_deberta_dataset"
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v3-small")
# Load or process dataset
if os.path.exists(CACHE_DIR):
print("📦 Loading cached dataset...")
dataset = load_from_disk(CACHE_DIR)
train_ds, val_ds = dataset["train"], dataset["test"]
else:
print("🔧 Processing and caching dataset...")
df = pd.read_csv(RAW_CSV)
df = df[["text", "organic"]]
df["organic"] = df["organic"].astype(int)
data = {
"text": df["text"].tolist(),
"label": df["organic"].tolist()
}
full_dataset = Dataset.from_dict(data)
dataset = full_dataset.train_test_split(test_size=0.1, seed=42)
def tokenize(batch):
tokenized = tokenizer(
batch["text"],
truncation=True,
padding="max_length",
max_length=512
)
tokenized["label"] = batch["label"]
return tokenized
dataset = dataset.map(tokenize, batched=True)
dataset.set_format(type="torch", columns=["input_ids", "attention_mask", "label"])
dataset.save_to_disk(CACHE_DIR)
train_ds, val_ds = dataset["train"], dataset["test"]
# Calculate class weights from training labels
train_labels = np.array(train_ds["label"])
class_weights = compute_class_weight(
class_weight="balanced",
classes=np.array([0, 1]),
y=train_labels
)
class_weights_tensor = torch.tensor(class_weights, dtype=torch.float)
# Load model
model = AutoModelForSequenceClassification.from_pretrained("microsoft/deberta-v3-small", num_labels=2)
# Custom Trainer with weighted loss
class WeightedLossTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
labels = inputs.pop("labels")
outputs = model(**inputs)
logits = outputs.logits
loss_fct = torch.nn.CrossEntropyLoss(weight=class_weights_tensor.to(logits.device))
loss = loss_fct(logits, labels)
return (loss, outputs) if return_outputs else loss
# Evaluation metrics
def compute_metrics(eval_pred):
logits, labels = eval_pred
preds = torch.tensor(logits).argmax(dim=-1)
acc = accuracy_score(labels, preds)
f1 = f1_score(labels, preds)
return {"accuracy": acc, "f1": f1}
# Training arguments
training_args = TrainingArguments(
output_dir="./ai-small-weighted",
evaluation_strategy="steps",
eval_steps=5000,
save_strategy="steps",
save_steps=5000,
save_total_limit=20,
logging_steps=10,
per_device_train_batch_size=48,
gradient_accumulation_steps=8,
num_train_epochs=3,
learning_rate=1e-6,
weight_decay=0.01,
max_grad_norm=1.0,
fp16=torch.cuda.is_available(),
load_best_model_at_end=True,
metric_for_best_model="f1",
greater_is_better=True,
logging_dir="./logs",
)
# Trainer
trainer = WeightedLossTrainer(
model=model,
args=training_args,
train_dataset=train_ds,
eval_dataset=val_ds,
tokenizer=tokenizer,
data_collator=DataCollatorWithPadding(tokenizer),
compute_metrics=compute_metrics,
)
# Train and save
trainer.train()
trainer.save_model("./ai-small-weighted/final_model")
tokenizer.save_pretrained("./ai-small-weighted/final_model") |