File size: 1,600 Bytes
611dbe2 | 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 | from datasets import load_dataset
from transformers import (
AutoTokenizer,
AutoModelForSequenceClassification,
Trainer,
TrainingArguments,
)
import torch
# STEP 1: Load IMDb Dataset
dataset = load_dataset("imdb")
# STEP 2: Tokenize the Data
checkpoint = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
def preprocess(example):
return tokenizer(example["text"], truncation=True, padding="max_length", max_length=256)
tokenized = dataset.map(preprocess, batched=True)
tokenized = tokenized.remove_columns(["text"])
tokenized = tokenized.rename_column("label", "labels")
tokenized.set_format("torch")
# Use a smaller subset for quick training
train_dataset = tokenized["train"].shuffle(seed=42).select(range(2000))
val_dataset = tokenized["test"].shuffle(seed=42).select(range(500))
# STEP 3: Load Model
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
# STEP 4: Define Training Arguments
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
save_strategy="epoch",
num_train_epochs=3,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
logging_dir="./logs",
logging_steps=50,
report_to="none"
)
# STEP 5: Train
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=val_dataset,
tokenizer=tokenizer,
)
trainer.train()
# STEP 6: Save Locally to Repo Folder
model.save_pretrained("./")
tokenizer.save_pretrained("./")
print("✅ Model and tokenizer saved locally!")
|