--- 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") ```