Update README.md

README.md

@@ -1,3 +1,115 @@
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-license: unknown
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+```python
+import torch
+import torch.nn as nn
+from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
+
+class TextRefinementModel(nn.Module):
+    def __init__(self, model_name='tirthadagr8/custom-mbart-large-50', max_length=64):
+        super(TextRefinementModel, self).__init__()
+        self.tokenizer = MBart50TokenizerFast.from_pretrained(model_name)
+        self.mbart = MBartForConditionalGeneration.from_pretrained(model_name)
+        self.mbart.config.max_length=64
+        self.max_length = max_length
+        
+        # Set the language code for Japanese (ja_XX) or Chinese (zh_CN)
+#         self.tokenizer.src_lang = 'ja_XX'  # For Japanese
+        # self.tokenizer.src_lang = 'zh_CN'  # Uncomment for Chinese
+
+    def forward(self, input_texts):
+        # Tokenize the noisy text inputs
+        input_ids = self.tokenizer(input_texts, return_tensors='pt', padding=True, truncation=True, max_length=self.max_length)['input_ids']
+        
+        # mBART generates output logits
+        output_logits = self.mbart(input_ids).logits
+        
+        return output_logits
+
+    def generate_corrected_text(self, input_texts, temperature=0.7):
+        # Tokenize the input noisy text
+        input_ids = self.tokenizer(input_texts, return_tensors='pt', padding=True, truncation=True, max_length=self.max_length)['input_ids']
+        
+        # Generate corrected text using mBART's generate function
+        mbart_outputs = self.mbart.generate(input_ids, max_length=self.max_length, temperature=temperature, num_return_sequences=1)
+        
+        # Decode generated text
+        corrected_texts = [self.tokenizer.decode(g, skip_special_tokens=True) for g in mbart_outputs]
+        return corrected_texts
+
+# Example usage
+model = TextRefinementModel()
+
+noisy_text = ["これは間違ったテキストの例です。", "这是错误的文本示例。"]  # Japanese and Chinese examples
+corrected_text = model.generate_corrected_text(noisy_text)
+
+print(f"Corrected Text: {corrected_text}")
+```
+For training:
+```python
+from transformers import AdamW
+import torch.nn.functional as F
+from tqdm import tqdm
+from torch.utils.data import DataLoader
+import numpy as np
+
+# Initialize the mBART model and optimizer
+model = TextRefinementModel().cuda()
+optimizer = AdamW(model.parameters(), lr=5e-5)
+
+batch_size = 16
+
+# Create a custom dataset class
+class TextCorrectionDataset(torch.utils.data.Dataset):
+    def __init__(self, data, tokenizer, max_length=64):
+        self.data = data
+        self.tokenizer = tokenizer
+        self.max_length = max_length
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        noisy_text, correct_text = self.data[idx]
+        inputs = self.tokenizer(noisy_text, return_tensors='pt', padding='max_length', truncation=True, max_length=self.max_length)
+        labels = self.tokenizer(correct_text, return_tensors='pt', padding='max_length', truncation=True, max_length=self.max_length)
+        
+        # Adjust label tensors for correct shape
+        input_ids = inputs['input_ids'].squeeze()  # Remove extra batch dimension
+        labels = labels['input_ids'].squeeze()     # Same for labels
+        return input_ids, labels
+
+# Create DataLoader with batching
+train_dataset = TextCorrectionDataset(train_data, model.tokenizer)
+train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+
+# Define training loop with batches
+def train_epoch(model, train_loader, optimizer):
+    model.train()
+    total_loss = []
+    step_iter=0
+    for input_ids, labels in tqdm(train_loader):
+        # Move tensors to model's device
+        input_ids = input_ids.to(model.mbart.device)
+        labels = labels.to(model.mbart.device)
+        
+        # Forward pass
+        outputs = model.mbart(input_ids=input_ids, labels=labels)
+        loss = outputs.loss
+        
+        # Backpropagation
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+        total_loss.append(loss.item())
+        
+        if step_iter%100==0:
+            print('Loss:',np.mean(total_loss))
+        
+        step_iter+=1
+    return np.mean(total_loss)
+
+# Example training loop
+for epoch in range(5):  # Train for 5 epochs (or as needed)
+    loss = train_epoch(model, train_loader, optimizer)
+    print(f"Epoch {epoch+1}, Loss: {loss:.4f}")
+```