tzz

train_tokenizer.py

@@ -1,19 +1,22 @@
 #!/usr/bin/env python3
 from datasets import load_dataset
+from datasets import load_from_disk
 from tokenizers import ByteLevelBPETokenizer
-
+from tqdm import tqdm
 # load dataset
-dataset = load_dataset("oscar", "unshuffled_deduplicated_hi", split="train")
+# dataset = load_dataset("oscar", "unshuffled_deduplicated_hi", split="train")
+
+dataset = load_from_disk("/home/rtx/work/dk/hf/vo")
 
 # Instantiate tokenizer
-tokenizer = ByteLevelBPETokenizer()
+tokenizer = ByteLevelBPETokenizer(add_prefix_space=True)
 
-def batch_iterator(batch_size=1000):
+def batch_iterator(batch_size=100_000):
     for i in range(0, len(dataset), batch_size):
         yield dataset[i: i + batch_size]["text"]
 
 # Customized training
-tokenizer.train_from_iterator(batch_iterator(), vocab_size=50265, min_frequency=2, special_tokens=[
+tokenizer.train_from_iterator(batch_iterator(), vocab_size=50265, min_frequency=50, special_tokens=[
     "<s>",
     "<pad>",
     "</s>",