Create tokenizer_make2.py

tokenizer_make2.py

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+from transformers import BertTokenizerFast
+import os
+import tensorflow as tf
+
+class MiniSunTokenizer:
+    def __init__(self, vocab_file=None):
+        if vocab_file:
+            self.tokenizer = BertTokenizerFast(vocab_file=vocab_file, do_lower_case=False)
+        else:
+            self.tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
+
+        # Define special tokens
+        self.pad_token = '[PAD]'
+        self.unk_token = '[UNK]'
+        self.cls_token = '[CLS]'
+        self.sep_token = '[SEP]'
+        self.mask_token = '[MASK]'
+        self.eos_token = '[EOS]'
+
+    def encode(self, text, max_length=512, padding=True, truncation=True):
+        """
+        Encodes the input text (string or batch of strings).
+        It automatically detects if the input is a batch or a single sentence.
+        """
+        if isinstance(text, list):  # If batch of texts, call batch_encode_plus
+            return self._encode_batch(text, max_length, padding, truncation)
+        else:  # Single text input
+            return self._encode_single(text, max_length, padding, truncation)
+
+    def _encode_single(self, text, max_length=512, padding=True, truncation=True):
+        # Encode a single string
+        encoded = self.tokenizer.encode_plus(
+            text,
+            add_special_tokens=True,
+            max_length=max_length,
+            padding='max_length' if padding else False,
+            truncation=truncation,
+            return_attention_mask=True,
+            return_tensors='tf'
+        )
+        return {
+            'input_ids': encoded['input_ids'].numpy().tolist(),
+            'attention_mask': encoded['attention_mask'].numpy().tolist()
+        }
+
+    def _encode_batch(self, texts, max_length=512, padding=True, truncation=True):
+        # Encode a batch of strings
+        encoded_batch = self.tokenizer.batch_encode_plus(
+            texts,
+            add_special_tokens=True,
+            max_length=max_length,
+            padding='max_length' if padding else False,
+            truncation=truncation,
+            return_attention_mask=True,
+            return_tensors='tf'
+        )
+        return {
+            'input_ids': encoded_batch['input_ids'].numpy().tolist(),
+            'attention_mask': encoded_batch['attention_mask'].numpy().tolist()
+        }
+
+    def decode(self, token_ids):
+        # Decodes token IDs back into text
+        return self.tokenizer.decode(token_ids, skip_special_tokens=True)
+
+    def save_pretrained(self, save_directory):
+        # Save the tokenizer in Hugging Face format
+        os.makedirs(save_directory, exist_ok=True)
+        self.tokenizer.save_pretrained(save_directory)
+
+    def __call__(self, text, *args, **kwargs):
+        """
+        This allows the tokenizer object to be called directly like `tokenizer(text)`.
+        It will automatically detect if the input is a batch or a single sentence.
+        """
+        return self.encode(text, *args, **kwargs)
+
+
+# Example usage of the tokenizer
+tokenizer = MiniSunTokenizer()