Train with yelp data perhaps

lectures/live-coding/L24/yelp_data.py

@@ -0,0 +1,57 @@
+import evaluate
+import numpy as np
+from datasets import load_dataset
+from transformers import AutoTokenizer
+from transformers import TrainingArguments
+
+def tokenize_function(examples):
+    return tokenizer(examples["text"], padding="max_length", truncation=True)
+
+
+def print_gpu_utilization():
+    nvmlInit()
+    handle = nvmlDeviceGetHandleByIndex(0)
+    info = nvmlDeviceGetMemoryInfo(handle)
+    print(f"GPU memory occupied: {info.used // 1024 ** 2} MB.")
+
+
+def compute_metrics(eval_pred):
+    logits, labels = eval_pred
+    predictions = np.argmax(logits, axis=-1)
+    return metric.compute(predictions=predictions, references=labels)
+
+
+def print_summary(res):
+    print(f"Time: {res.metrics['train_runtime']:.2f}")
+    print(f"Samples/second: {res.metrics['train_samples_per_second']:.2f}")
+    print_gpu_utilization()
+
+
+print("Starting up. Initial GPU utilization:")
+print_gpu_utilization()
+torch.ones((1, 1)).to("cuda")
+print("Initialized Torch; current GPU utilization:")
+print_gpu_utilization()
+
+dataset = load_dataset("yelp_review_full")
+tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
+small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
+
+model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=5)
+training_args = TrainingArguments(per_device_train_batch_size=8, output_dir="test_trainer")
+metric = evaluate.load("accuracy")
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=small_train_dataset,
+    eval_dataset=small_eval_dataset,
+    compute_metrics=compute_metrics,
+)
+
+result = trainer.train()
+print_summary(result)