| The tokenizers obtained from the 🤗 Tokenizers library can be | |
| loaded very simply into 🤗 Transformers. | |
| Before getting in the specifics, let's first start by creating a dummy tokenizer in a few lines: | |
| thon | |
| from tokenizers import Tokenizer | |
| from tokenizers.models import BPE | |
| from tokenizers.trainers import BpeTrainer | |
| from tokenizers.pre_tokenizers import Whitespace | |
| tokenizer = Tokenizer(BPE(unk_token="[UNK]")) | |
| trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"]) | |
| tokenizer.pre_tokenizer = Whitespace() | |
| files = [] | |
| tokenizer.train(files, trainer) | |
| We now have a tokenizer trained on the files we defined. |