Datasets:

jooni22
/

dst-question

	---
	license: apache-2.0
	---
	### Label info:
	```
	0: "fragment",
	1: "statement",
	2: "question",
	3: "command",
	4: "rhetorical question",
	5: "rhetorical command",
	6: "intonation-dependent utterance"
	```

	### Training process:
	```
	{'loss': 1.8008, 'grad_norm': 7.2770233154296875, 'learning_rate': 1e-05, 'epoch': 0.03}
	{'loss': 0.894, 'grad_norm': 27.84651756286621, 'learning_rate': 2e-05, 'epoch': 0.06}
	{'loss': 0.6504, 'grad_norm': 30.617990493774414, 'learning_rate': 3e-05, 'epoch': 0.09}
	{'loss': 0.5939, 'grad_norm': 34.73934555053711, 'learning_rate': 4e-05, 'epoch': 0.12}
	{'loss': 0.5786, 'grad_norm': 6.585583209991455, 'learning_rate': 5e-05, 'epoch': 0.15}
	{'eval_loss': 0.5915874242782593, 'eval_accuracy': 0.8297766749379653, 'eval_f1': 0.8315132136625163, 'eval_precision': 0.8410462605264737, 'eval_recall': 0.8297766749379653, 'eval_runtime': 265.1144, 'eval_samples_per_second': 22.801, 'eval_steps_per_second': 1.426, 'epoch': 0.15}
	{'loss': 0.5928, 'grad_norm': 10.66515064239502, 'learning_rate': 4.8276456394346784e-05, 'epoch': 0.18}
	{'loss': 0.5611, 'grad_norm': 3.804234266281128, 'learning_rate': 4.655291278869355e-05, 'epoch': 0.21}
	{'loss': 0.5151, 'grad_norm': 8.275078773498535, 'learning_rate': 4.4829369183040333e-05, 'epoch': 0.24}
	{'loss': 0.4696, 'grad_norm': 2.44854474067688, 'learning_rate': 4.310582557738711e-05, 'epoch': 0.26}
	{'loss': 0.5183, 'grad_norm': 8.534456253051758, 'learning_rate': 4.138228197173389e-05, 'epoch': 0.29}
	{'eval_loss': 0.5429911017417908, 'eval_accuracy': 0.8415219189412738, 'eval_f1': 0.8231674368620022, 'eval_precision': 0.8383674385161947, 'eval_recall': 0.8415219189412738, 'eval_runtime': 268.1016, 'eval_samples_per_second': 22.547, 'eval_steps_per_second': 1.41, 'epoch': 0.29}
	{'loss': 0.4802, 'grad_norm': 10.636425018310547, 'learning_rate': 3.965873836608066e-05, 'epoch': 0.32}
	{'loss': 0.4877, 'grad_norm': 6.05213737487793, 'learning_rate': 3.793519476042744e-05, 'epoch': 0.35}
	{'loss': 0.5093, 'grad_norm': 5.5984015464782715, 'learning_rate': 3.621165115477422e-05, 'epoch': 0.38}
	{'loss': 0.496, 'grad_norm': 7.945780277252197, 'learning_rate': 3.4488107549120996e-05, 'epoch': 0.41}
	{'loss': 0.5005, 'grad_norm': 5.778200626373291, 'learning_rate': 3.276456394346777e-05, 'epoch': 0.44}
	{'eval_loss': 0.41184064745903015, 'eval_accuracy': 0.8684863523573201, 'eval_f1': 0.8635611747282996, 'eval_precision': 0.8629771033516368, 'eval_recall': 0.8684863523573201, 'eval_runtime': 270.0108, 'eval_samples_per_second': 22.388, 'eval_steps_per_second': 1.4, 'epoch': 0.44}
	{'loss': 0.4436, 'grad_norm': 4.413114070892334, 'learning_rate': 3.1041020337814545e-05, 'epoch': 0.47}
	{'loss': 0.4899, 'grad_norm': 18.563016891479492, 'learning_rate': 2.9317476732161327e-05, 'epoch': 0.5}
	{'loss': 0.4637, 'grad_norm': 26.92985725402832, 'learning_rate': 2.7593933126508105e-05, 'epoch': 0.53}
	{'loss': 0.4387, 'grad_norm': 7.494612693786621, 'learning_rate': 2.5870389520854876e-05, 'epoch': 0.56}
	{'loss': 0.4401, 'grad_norm': 20.5152530670166, 'learning_rate': 2.4146845915201654e-05, 'epoch': 0.59}
	{'eval_loss': 0.42229706048965454, 'eval_accuracy': 0.8663358147229115, 'eval_f1': 0.859666580414163, 'eval_precision': 0.8638930298685418, 'eval_recall': 0.8663358147229115, 'eval_runtime': 272.7465, 'eval_samples_per_second': 22.163, 'eval_steps_per_second': 1.386, 'epoch': 0.59}
	{'loss': 0.4289, 'grad_norm': 10.1361665725708, 'learning_rate': 2.2423302309548433e-05, 'epoch': 0.62}
	{'loss': 0.4193, 'grad_norm': 8.068666458129883, 'learning_rate': 2.0699758703895207e-05, 'epoch': 0.65}
	{'loss': 0.4038, 'grad_norm': 8.713869094848633, 'learning_rate': 1.8976215098241985e-05, 'epoch': 0.68}
	{'loss': 0.4073, 'grad_norm': 12.182595252990723, 'learning_rate': 1.7252671492588764e-05, 'epoch': 0.71}
	{'loss': 0.4095, 'grad_norm': 13.43953800201416, 'learning_rate': 1.5529127886935542e-05, 'epoch': 0.74}
	{'eval_loss': 0.3974127173423767, 'eval_accuracy': 0.8726220016542597, 'eval_f1': 0.8677290061110087, 'eval_precision': 0.8672987137526573, 'eval_recall': 0.8726220016542597, 'eval_runtime': 270.2975, 'eval_samples_per_second': 22.364, 'eval_steps_per_second': 1.398, 'epoch': 0.74}
	{'loss': 0.3473, 'grad_norm': 16.423139572143555, 'learning_rate': 1.3805584281282317e-05, 'epoch': 0.76}
	{'loss': 0.3982, 'grad_norm': 6.357703685760498, 'learning_rate': 1.2082040675629095e-05, 'epoch': 0.79}
	{'loss': 0.3286, 'grad_norm': 4.977189064025879, 'learning_rate': 1.0358497069975871e-05, 'epoch': 0.82}
	{'loss': 0.3712, 'grad_norm': 4.068944454193115, 'learning_rate': 8.634953464322648e-06, 'epoch': 0.85}
	{'loss': 0.345, 'grad_norm': 6.266202926635742, 'learning_rate': 6.911409858669425e-06, 'epoch': 0.88}
	{'eval_loss': 0.3740645945072174, 'eval_accuracy': 0.8822167080231597, 'eval_f1': 0.8780706451391699, 'eval_precision': 0.877925468669178, 'eval_recall': 0.8822167080231597, 'eval_runtime': 270.0795, 'eval_samples_per_second': 22.382, 'eval_steps_per_second': 1.4, 'epoch': 0.88}
	{'loss': 0.4049, 'grad_norm': 10.76927375793457, 'learning_rate': 5.187866253016201e-06, 'epoch': 0.91}
	{'loss': 0.3919, 'grad_norm': 12.331282615661621, 'learning_rate': 3.4643226473629783e-06, 'epoch': 0.94}
	{'loss': 0.3576, 'grad_norm': 8.6154203414917, 'learning_rate': 1.7407790417097554e-06, 'epoch': 0.97}
	{'loss': 0.3544, 'grad_norm': 10.01504135131836, 'learning_rate': 1.723543605653223e-08, 'epoch': 1.0}
	{'train_runtime': 7076.4012, 'train_samples_per_second': 7.688, 'train_steps_per_second': 0.481, 'train_loss': 0.5087223172678522, 'epoch': 1.0}
	100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3401/3401 [1:57:56<00:00, 2.08s/it]
	Training completed. Model saved.
	```

	### Classification Report:
	```
	precision recall f1-score support

	fragment 0.95 0.92 0.94 597
	statement 0.84 0.91 0.87 1811
	question 0.95 0.94 0.94 1786
	command 0.88 0.91 0.90 1296
	rhetorical question 0.73 0.62 0.67 174
	rhetorical command 0.86 0.56 0.68 108
	intonation-dependent utterance 0.57 0.38 0.46 273

	accuracy 0.88 6045
	macro avg 0.83 0.75 0.78 6045
	weighted avg 0.88 0.88 0.88 6045

	Predictions saved
	```
	### Train code:
	```python
	import pandas as pd
	from sklearn.model_selection import train_test_split
	from transformers import (
	RobertaTokenizerFast,
	RobertaForSequenceClassification,
	Trainer,
	TrainingArguments,
	EarlyStoppingCallback
	)
	from datasets import Dataset
	import torch
	import numpy as np
	from sklearn.metrics import accuracy_score, precision_recall_fscore_support, classification_report
	from tensorflow.python.keras.optimizer_v2.adam import Adam

	# Load and prepare data
	train_df = pd.read_csv("./train_fix_v1.csv")
	test_df = pd.read_csv("./test_fix_v1.csv")

	# Convert to Dataset objects
	train_dataset = Dataset.from_pandas(train_df)
	test_dataset = Dataset.from_pandas(test_df)

	# Initialize tokenizer and model
	model_name = "FacebookAI/roberta-base"
	tokenizer = RobertaTokenizerFast.from_pretrained(model_name)
	model = RobertaForSequenceClassification.from_pretrained(
	model_name,
	num_labels=7,
	id2label={
	0: "fragment",
	1: "statement",
	2: "question",
	3: "command",
	4: "rhetorical question",
	5: "rhetorical command",
	6: "intonation-dependent utterance"
	},
	label2id={
	"fragment": 0,
	"statement": 1,
	"question": 2,
	"command": 3,
	"rhetorical question": 4,
	"rhetorical command": 5,
	"intonation-dependent utterance": 6
	}
	)

	# Tokenize function
	def tokenize_function(examples):
	return tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)

	# Tokenize datasets
	tokenized_train = train_dataset.map(tokenize_function, batched=True)
	tokenized_test = test_dataset.map(tokenize_function, batched=True)

	# Compute metrics function for evaluation
	def compute_metrics(pred):
	labels = pred.label_ids
	preds = pred.predictions.argmax(-1)
	precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='weighted')
	acc = accuracy_score(labels, preds)
	return {
	'accuracy': acc,
	'f1': f1,
	'precision': precision,
	'recall': recall
	}

	# Training arguments
	training_args = TrainingArguments(
	output_dir="./roberta_base_stock",
	num_train_epochs=1, # Ustawione na 10, ale z early stopping
	per_device_train_batch_size=16,
	per_device_eval_batch_size=16,
	warmup_steps=500,
	weight_decay=0.01,
	logging_dir='./logs',
	logging_steps=100,
	evaluation_strategy="steps",
	eval_steps=500,
	save_strategy="steps",
	save_steps=500,
	load_best_model_at_end=True,
	metric_for_best_model="f1",
	learning_rate=5e-05,
	)

	# Initialize Trainer
	trainer = Trainer(
	model=model,
	args=training_args,
	train_dataset=tokenized_train,
	eval_dataset=tokenized_test,
	compute_metrics=compute_metrics,
	callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]
	)

	# Train the model
	trainer.train()

	# Save the fine-tuned model
	model.save_pretrained("./roberta_base_stock")
	tokenizer.save_pretrained("./roberta_base_stock")

	print("Training completed. Model saved.")

	# Evaluate the model on the test set
	print("Evaluating model on test set...")
	test_results = trainer.evaluate(tokenized_test)

	print("Test set evaluation results:")
	for key, value in test_results.items():
	print(f"{key}: {value}")

	# Perform predictions on the test set
	test_predictions = trainer.predict(tokenized_test)

	# Get predicted labels
	predicted_labels = np.argmax(test_predictions.predictions, axis=1)
	true_labels = test_predictions.label_ids

	# Print classification report
	print("\nClassification Report:")
	print(classification_report(true_labels, predicted_labels,
	target_names=model.config.id2label.values()))

	# Optional: Save predictions to CSV
	test_df['predicted_label'] = predicted_labels
	test_df.to_csv("./roberta_base_stock/test_predictions.csv", index=False)
	print("Predictions saved")
	```