train.py

from datasets import load_dataset
from transformers import (
    DistilBertTokenizerFast,
    DistilBertForSequenceClassification,
    Trainer,
    TrainingArguments
)
import pandas as pd

# Load dataset
df = pd.read_csv("data.csv")
dataset = load_dataset("csv", data_files="data.csv")

# Label mapping
label_map = {"Low Risk": 0, "Medium Risk": 1, "High Risk": 2}
df["label"] = df["label"].map(label_map)

dataset = load_dataset("csv", data_files={"train": "data.csv"})

# Tokenizer
tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased")

def tokenize(batch):
    return tokenizer(batch["text"], padding=True, truncation=True)

dataset = dataset.map(tokenize, batched=True)

# Model
model = DistilBertForSequenceClassification.from_pretrained(
    "distilbert-base-uncased",
    num_labels=3
)

# Training args
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="no",
    per_device_train_batch_size=4,
    num_train_epochs=3,
    save_strategy="epoch",
    logging_dir="./logs"
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"]
)

trainer.train()
model.save_pretrained("./model")
tokenizer.save_pretrained("./model")