Upload demo_03.py

demo_03.py

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+from datasets import load_dataset
+
+dataset = load_dataset("yelp_review_full")
+dataset["train"][100]
+
+#creating the dataset
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+
+
+def tokenize_function(examples):
+    return tokenizer(examples["text"], padding="max_length", truncation=True)
+
+#mapping the values:
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+#small Datasets:
+small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
+small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
+
+#Loading pretrained Model:
+from transformers import AutoModelForSequenceClassification
+
+model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
+
+###
+
+from transformers import TrainingArguments
+
+training_args = TrainingArguments(output_dir="test_trainer")
+
+#Evaluate
+
+def compute_metrics(eval_pred):
+    logits, labels = eval_pred
+    predictions = np.argmax(logits, axis=-1)
+    return metric.compute(predictions=predictions, references=labels)
+
+#Training Argumnents and importing Trainer:
+from transformers import TrainingArguments, Trainer
+
+training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")
+
+#Defining Hyperparameters for Trainer:
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=small_train_dataset,
+    eval_dataset=small_eval_dataset,
+    compute_metrics=compute_metrics,
+)
+
+#Execute the training:
+trainer.train()
+
+#Predictions:
+predictions = trainer.predict(small_eval_dataset)
+print(predictions.predictions.shape,predictions.label_ids.shape)
+print(predictions)