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from transformers import TrainingArguments, Trainer
from datasets import load_dataset
import evaluate
import numpy as np
from modeling_octagon import OctagonForSequenceClassification, OctagonConfig
from tokenization_octagon import OctagonTokenizer
# Load dataset
dataset = load_dataset("imdb")
# Sample training (for demo purposes, use smaller subset)
train_dataset = dataset["train"].shuffle(seed=42).select(range(1000))
eval_dataset = dataset["test"].shuffle(seed=42).select(range(200))
# Initialize tokenizer
tokenizer = OctagonTokenizer.train_tokenizer(
texts=train_dataset["text"],
vocab_size=30522,
save_path="octagon-tokenizer.json"
)
# Tokenize function
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_train = train_dataset.map(tokenize_function, batched=True)
tokenized_eval = eval_dataset.map(tokenize_function, batched=True)
# Model config
config = OctagonConfig(
vocab_size=30522,
hidden_size=128, # Smaller for demo
num_hidden_layers=4,
num_attention_heads=4,
intermediate_size=512,
num_labels=2
)
model = OctagonForSequenceClassification(config)
# Metrics
metric = evaluate.load("accuracy")
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return metric.compute(predictions=predictions, references=labels)
# Training args
training_args = TrainingArguments(
output_dir="octagon_model",
evaluation_strategy="epoch",
save_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=3,
weight_decay=0.01,
load_best_model_at_end=True,
)
# Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_train,
eval_dataset=tokenized_eval,
compute_metrics=compute_metrics,
)
# Train
trainer.train()
# Save model
model.save_pretrained("octagon_model")
tokenizer.save_pretrained("octagon_model") |