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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 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 | import os
import sys
import numpy as np
import torch
import torch .nn as nn
from transformers import (
AutoTokenizer ,AutoModelForSequenceClassification ,
Trainer ,TrainingArguments ,DataCollatorWithPadding ,
EarlyStoppingCallback ,
)
from sklearn .metrics import accuracy_score ,f1_score ,precision_recall_fscore_support
sys .path .append (os .path .dirname (os .path .abspath (__file__ )))
from dataset import UrduTextDataset
NUM_LABELS =4
MAX_LENGTH =128
def compute_metrics (eval_pred ):
logits ,labels =eval_pred
preds =np .argmax (logits ,axis =-1 )
precision ,recall ,f1 ,_ =precision_recall_fscore_support (
labels ,preds ,average ='weighted',zero_division =0 )
macro_f1 =f1_score (labels ,preds ,average ='macro',zero_division =0 )
return {
'accuracy':accuracy_score (labels ,preds ),
'f1':f1 ,
'macro_f1':macro_f1 ,
'precision':precision ,
'recall':recall ,
}
class WeightedTrainer (Trainer ):
"""Trainer that applies class weights (+ optional label smoothing) in the loss."""
def __init__ (self ,*args ,class_weights =None ,label_smoothing =0.0 ,**kwargs ):
super ().__init__ (*args ,**kwargs )
self .class_weights =class_weights
self .label_smoothing =label_smoothing
def compute_loss (self ,model ,inputs ,return_outputs =False ,**kwargs ):
labels =inputs .pop ("labels")
outputs =model (**inputs )
weight =(self .class_weights .to (model .device )
if self .class_weights is not None else None )
loss_fn =nn .CrossEntropyLoss (
weight =weight ,label_smoothing =self .label_smoothing )
loss =loss_fn (outputs .logits .view (-1 ,NUM_LABELS ),labels .view (-1 ))
return (loss ,outputs )if return_outputs else loss
def main ():
print ("="*60 )
print (" Phase 4: Training Emotion Model")
print ("="*60 )
torch .set_num_threads (os .cpu_count ()or 1 )
model_name ="xlm-roberta-base"
print ("\n[1/5] Loading tokenizer and XLM-RoBERTa model...")
tokenizer =AutoTokenizer .from_pretrained (model_name )
model =AutoModelForSequenceClassification .from_pretrained (
model_name ,num_labels =NUM_LABELS )
print ("\n[2/5] Loading datasets...")
base =os .path .dirname (os .path .dirname (os .path .abspath (__file__ )))
data_dir =os .path .join (base ,'data')
train_files =[os .path .join (data_dir ,'semeval_emotion_train.csv')]
val_files =[os .path .join (data_dir ,'semeval_emotion_val.csv')]
train_dataset =UrduTextDataset (train_files ,tokenizer ,max_length =MAX_LENGTH ,task ="emotion")
val_dataset =UrduTextDataset (val_files ,tokenizer ,max_length =MAX_LENGTH ,task ="emotion")
print (f"Train samples: {len (train_dataset )} | Val samples: {len (val_dataset )}")
class_weights =train_dataset .get_class_weights (NUM_LABELS )
print (f"Class weights: {class_weights .tolist ()}")
data_collator =DataCollatorWithPadding (tokenizer =tokenizer )
print ("\n[3/5] Setting up Training Arguments...")
output_dir =os .path .join (base ,'models','emotion_model')
training_args =TrainingArguments (
output_dir =output_dir ,
num_train_epochs =6 ,
per_device_train_batch_size =8 ,
per_device_eval_batch_size =16 ,
gradient_accumulation_steps =4 ,
learning_rate =2e-5 ,
warmup_ratio =0.1 ,
weight_decay =0.01 ,
lr_scheduler_type ="cosine",
max_grad_norm =1.0 ,
dataloader_pin_memory =False ,
dataloader_num_workers =0 ,
logging_dir ='./logs/emotion',
logging_steps =50 ,
eval_strategy ="epoch",
save_strategy ="epoch",
save_total_limit =2 ,
load_best_model_at_end =True ,
metric_for_best_model ="macro_f1",
greater_is_better =True ,
seed =42 ,
report_to ="none",
)
print ("\n[4/5] Initializing Trainer...")
trainer =WeightedTrainer (
model =model ,
args =training_args ,
train_dataset =train_dataset ,
eval_dataset =val_dataset ,
processing_class =tokenizer ,
data_collator =data_collator ,
compute_metrics =compute_metrics ,
class_weights =class_weights ,
label_smoothing =0.1 ,
callbacks =[EarlyStoppingCallback (early_stopping_patience =2 )],
)
print ("\n[5/5] Starting training loop...")
trainer .train ()
print (f"\nTraining complete! Saving best model to {output_dir }")
trainer .save_model (output_dir )
tokenizer .save_pretrained (output_dir )
print ("Emotion model saved successfully!")
if __name__ =="__main__":
main ()
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