File size: 2,053 Bytes
b31728c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | import os
import sys
sys .path .append (os .path .dirname (os .path .abspath (__file__ )))
from transformers import AutoTokenizer
from dataset import UrduTextDataset
from torch .utils .data import DataLoader
def main ():
print ("Loading XLM-RoBERTa Tokenizer...")
tokenizer =AutoTokenizer .from_pretrained ("xlm-roberta-base")
data_dir =os .path .join (os .path .dirname (os .path .dirname (os .path .abspath (__file__ ))),'data')
print ("\n--- Testing Sentiment Dataset ---")
sentiment_train_files =[
os .path .join (data_dir ,'roman_urdu_sentiment_train.csv'),
os .path .join (data_dir ,'urdu_sentiment_corpus_train.csv')
]
sentiment_dataset =UrduTextDataset (
csv_paths =sentiment_train_files ,
tokenizer =tokenizer ,
max_length =128 ,
task ="sentiment"
)
print (f"Total Sentiment Training Samples: {len (sentiment_dataset )}")
sample =sentiment_dataset [0 ]
print (f"\nSample Text: {sample ['text']}")
print (f"Sample Label: {sample ['label']} (0=Neg, 1=Neu, 2=Pos)")
print (f"Input IDs shape: {sample ['input_ids'].shape }")
print (f"Attention Mask shape: {sample ['attention_mask'].shape }")
loader =DataLoader (sentiment_dataset ,batch_size =4 ,shuffle =True )
batch =next (iter (loader ))
print (f"\nBatch Input IDs shape: {batch ['input_ids'].shape }")
print (f"Batch Labels: {batch ['label']}")
print ("\n--- Testing Emotion Dataset ---")
emotion_train_files =[
os .path .join (data_dir ,'semeval_emotion_train.csv')
]
emotion_dataset =UrduTextDataset (
csv_paths =emotion_train_files ,
tokenizer =tokenizer ,
max_length =128 ,
task ="emotion"
)
print (f"Total Emotion Training Samples: {len (emotion_dataset )}")
emotion_sample =emotion_dataset [0 ]
print (f"Sample Label: {emotion_sample ['label']} (0=Joy, 1=Anger, 2=Fear, 3=Sadness)")
print ("\nPhase 3 Verification Successful! Dataset class and tokenization work perfectly.")
if __name__ =="__main__":
main ()
|