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a53dc0a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | import json
import torch
from datasets import Dataset
import evaluate
from transformers import RobertaTokenizer, RobertaForSequenceClassification, Trainer, TrainingArguments
# Load dataset from JSON
with open("ai_training_dataset.json", "r", encoding="utf-8") as f:
data = json.load(f)
# Check that all labels are integers (0, 1, or 2)
for item in data:
item["label"] = int(item["label"]) # Ensure type is correct
# Convert to HuggingFace Dataset
dataset = Dataset.from_list(data)
dataset = dataset.train_test_split(test_size=0.2)
train_dataset = dataset["train"]
eval_dataset = dataset["test"]
# Load tokenizer
model_name = "roberta-base"
tokenizer = RobertaTokenizer.from_pretrained(model_name)
# Tokenization function
def tokenize(example):
return tokenizer(example["text"], truncation=True, padding="max_length", max_length=512)
# Tokenize datasets
train_dataset = train_dataset.map(tokenize, batched=True)
eval_dataset = eval_dataset.map(tokenize, batched=True)
# Keep only model-required fields
train_dataset.set_format(type="torch", columns=["input_ids", "attention_mask", "label"])
eval_dataset.set_format(type="torch", columns=["input_ids", "attention_mask", "label"])
# Load model with 3 output labels
model = RobertaForSequenceClassification.from_pretrained(model_name, num_labels=3)
# Optional: define metrics
accuracy = evaluate.load("accuracy")
def compute_metrics(eval_pred):
logits, labels = eval_pred
preds = torch.argmax(torch.tensor(logits), dim=1)
return accuracy.compute(predictions=preds, references=labels)
# Training configuration
training_args = TrainingArguments(
output_dir="./models/roberta-detector",
evaluation_strategy="epoch", # MUST match save_strategy
save_strategy="epoch",
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
num_train_epochs=3,
logging_steps=10,
save_total_limit=1,
load_best_model_at_end=True,
metric_for_best_model="eval_loss",
report_to="none", # Prevents WandB issues
)
# Trainer setup
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
)
# Train
trainer.train()
# Save model + tokenizer
model.save_pretrained("./models/roberta-detector")
tokenizer.save_pretrained("./models/roberta-detector")
print("✅ Model trained and saved.")
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