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DEVIPUTRA AI // PURE PYTORCH & TRANSFORMERS ENGINE
Fine-tunes Multilingual HingHate Transformer on Code-Mixed Hinglish Slangs & Hate Speech.
Features:
- Pure Python & PyTorch (zero external DLL blocks from pandas/sklearn)
- Supports PyTorch CUDA / Tensor Cores / CPU execution
- Stratified 85/15 Train-Val split with F1-Score & Accuracy tracking
- Saves production-ready checkpoint into models/hing_hate_bert_rtx5050/
"""
import os
import sys
import csv
import random
from typing import Dict, List, Tuple
if hasattr(sys.stdout, 'reconfigure'):
try:
sys.stdout.reconfigure(encoding='utf-8')
except Exception:
pass
import torch
LABEL_TO_ID = {"Normal": 0, "Offensive": 1, "Hate Speech": 2}
ID_TO_LABEL = {0: "Normal", 1: "Offensive", 2: "Hate Speech"}
class HinglishTextDataset(torch.utils.data.Dataset):
def __init__(self, texts: List[str], labels: List[int], tokenizer, max_length: int = 128):
self.texts = list(texts)
self.labels = list(labels)
self.tokenizer = tokenizer
self.max_length = max_length
def __len__(self):
return len(self.texts)
def __getitem__(self, idx):
text = str(self.texts[idx])
label = self.labels[idx]
encoding = self.tokenizer(
text,
truncation=True,
padding='max_length',
max_length=self.max_length,
return_tensors='pt'
)
return {
'input_ids': encoding['input_ids'].squeeze(0),
'attention_mask': encoding['attention_mask'].squeeze(0),
'label': torch.tensor(label, dtype=torch.long)
}
def load_data_clean(filepath: str) -> Tuple[List[str], List[int]]:
texts, labels = [], []
with open(filepath, 'r', encoding='utf-8', errors='ignore') as f:
reader = csv.reader(f)
header = next(reader, None)
for row in reader:
if len(row) >= 2:
txt = row[0].strip()
lbl = row[1].strip()
if txt and lbl in LABEL_TO_ID:
texts.append(txt)
labels.append(LABEL_TO_ID[lbl])
return texts, labels
def stratified_split(texts: List[str], labels: List[int], val_ratio: float = 0.15, seed: int = 42):
random.seed(seed)
class_groups = {0: [], 1: [], 2: []}
for t, l in zip(texts, labels):
class_groups[l].append(t)
train_texts, train_labels = [], []
val_texts, val_labels = [], []
for l, group in class_groups.items():
random.shuffle(group)
val_count = max(1, int(len(group) * val_ratio))
val_texts.extend(group[:val_count])
val_labels.extend([l] * val_count)
train_texts.extend(group[val_count:])
train_labels.extend([l] * (len(group) - val_count))
# Shuffle training items
combined = list(zip(train_texts, train_labels))
random.shuffle(combined)
train_texts, train_labels = zip(*combined)
return list(train_texts), list(train_labels), val_texts, val_labels
def compute_metrics(preds: List[int], targets: List[int]) -> Dict[str, float]:
total = len(targets)
if total == 0:
return {"accuracy": 0.0, "f1": 0.0}
correct = sum(1 for p, y in zip(preds, targets) if p == y)
acc = correct / total
# Weighted F1
f1_weighted = 0.0
for c in [0, 1, 2]:
tp = sum(1 for p, y in zip(preds, targets) if p == c and y == c)
fp = sum(1 for p, y in zip(preds, targets) if p == c and y != c)
fn = sum(1 for p, y in zip(preds, targets) if p != c and y == c)
support = sum(1 for y in targets if y == c)
prec = tp / (tp + fp) if (tp + fp) > 0 else 0.0
rec = tp / (tp + fn) if (tp + fn) > 0 else 0.0
f1_class = (2 * prec * rec / (prec + rec)) if (prec + rec) > 0 else 0.0
f1_weighted += f1_class * (support / total)
return {"accuracy": round(acc, 4), "f1": round(f1_weighted, 4)}
def train_rtx5050(
data_path: str = "data/master_train_dataset.csv",
base_model_name: str = "l3cube-pune/hing-bert",
output_dir: str = "models/hing_hate_bert_rtx5050",
epochs: int = 3,
batch_size: int = 16,
lr: float = 2e-5
):
print("=" * 70)
print("DEVIPUTRA AI // HINGLISH TRANSFORMER FINE-TUNING PIPELINE")
print("=" * 70)
# 1. Device and Architecture Detection
if torch.cuda.is_available():
gpu_name = torch.cuda.get_device_name(0)
vram_gb = torch.cuda.get_device_properties(0).total_memory / (1024**3)
device = torch.device("cuda:0")
print(f"[GPU ENGINE ACTIVE]: {gpu_name} ({vram_gb:.2f} GB VRAM)")
else:
device = torch.device("cpu")
print("[DEVICE]: CPU (Optimized for lightweight inference)")
# 2. Load Dataset using pure csv module
if not os.path.exists(data_path):
data_path = os.path.join("data", "sample.csv")
print(f"Loading Hinglish training corpus: {data_path}...")
texts, labels = load_data_clean(data_path)
print(f"Total samples: {len(texts)} samples loaded.")
train_texts, train_labels, val_texts, val_labels = stratified_split(texts, labels, val_ratio=0.15)
print(f"Split: {len(train_texts)} train items | {len(val_texts)} validation items.")
# 3. Load HuggingFace Tokenizer & Model
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from torch.optim import AdamW
print(f"Loading transformer backbone ({base_model_name})...")
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = AutoModelForSequenceClassification.from_pretrained(
base_model_name,
num_labels=3,
id2label=ID_TO_LABEL,
label2id=LABEL_TO_ID
)
model.to(device)
train_ds = HinglishTextDataset(train_texts, train_labels, tokenizer)
val_ds = HinglishTextDataset(val_texts, val_labels, tokenizer)
train_loader = torch.utils.data.DataLoader(
train_ds, batch_size=batch_size, shuffle=True
)
val_loader = torch.utils.data.DataLoader(
val_ds, batch_size=batch_size, shuffle=False
)
optimizer = AdamW(model.parameters(), lr=lr, weight_decay=0.01)
best_val_f1 = 0.0
os.makedirs(output_dir, exist_ok=True)
print("\nSTARTING TRAINING LOOPS...")
for epoch in range(1, epochs + 1):
model.train()
total_loss = 0.0
for step, batch in enumerate(train_loader):
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
target_labels = batch['label'].to(device)
optimizer.zero_grad()
outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=target_labels)
loss = outputs.loss
loss.backward()
optimizer.step()
total_loss += loss.item()
if (step + 1) % 10 == 0 or (step + 1) == len(train_loader):
print(f" Epoch [{epoch}/{epochs}] | Step [{step+1}/{len(train_loader)}] | Batch Loss: {loss.item():.4f}")
avg_train_loss = total_loss / len(train_loader)
# Validation Phase
model.eval()
val_preds, val_targets = [], []
with torch.no_grad():
for batch in val_loader:
input_ids = batch['input_ids'].to(device)
attention_mask = batch['attention_mask'].to(device)
target_labels = batch['label']
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
preds = torch.argmax(outputs.logits, dim=1).cpu().tolist()
val_preds.extend(preds)
val_targets.extend(target_labels.tolist())
metrics = compute_metrics(val_preds, val_targets)
val_acc = metrics["accuracy"]
val_f1 = metrics["f1"]
print(f"\nEPOCH {epoch} SUMMARY:")
print(f" Train Loss : {avg_train_loss:.4f}")
print(f" Validation Acc : {val_acc * 100:.2f}%")
print(f" Validation F1 : {val_f1:.4f}")
print("-" * 50)
# Save Best Checkpoint
if val_f1 >= best_val_f1:
best_val_f1 = val_f1
print(f"Saving checkpoint to: '{output_dir}'...")
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
print("\nTRAINING COMPLETE!")
print(f"Best Validation F1: {best_val_f1:.4f}")
print(f"Model saved to: {output_dir}")
print("=" * 70)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Hinglish Fine-Tuning Script")
parser.add_argument("--epochs", type=int, default=2)
parser.add_argument("--batch_size", type=int, default=16)
parser.add_argument("--lr", type=float, default=2e-5)
args = parser.parse_args()
train_rtx5050(epochs=args.epochs, batch_size=args.batch_size, lr=args.lr)
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