Instructions to use p1atdev/multi-tokenizers-processor-sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use p1atdev/multi-tokenizers-processor-sample with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("p1atdev/multi-tokenizers-processor-sample", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| license: apache-2.0 | |
| See [transformers で複数のトークナイザーを一つのプロセッサーで扱う](https://zenn.dev/platina/articles/732feb7c3e9852). | |
| https://zenn.dev/platina/articles/732feb7c3e9852 | |
| ## Example usage | |
| ```py | |
| from transformers import AutoProcessor | |
| processor = AutoProcessor.from_pretrained( | |
| "p1atdev/multi-tokenizers-processor-sample", | |
| trust_remote_code=True, | |
| commit_hash="111e8a30609fb5bc13e16d08f7c49196b23d5056" | |
| ) | |
| print(processor( | |
| text_1="テキスト1", | |
| text_2="テキスト2", | |
| )) | |
| # {'input_ids': tensor([[ 1, 43412, 28745]]), 'attention_mask': tensor([[1, 1, 1]]), 'input_ids_2': tensor([[56833, 61803, 70534, 17]]), 'attention_mask_2': tensor([[1, 1, 1, 1]])} | |
| ``` | |