Instructions to use otmanheddouch/shakespear-tokenizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use otmanheddouch/shakespear-tokenizer with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("otmanheddouch/shakespear-tokenizer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download handler.py from otmanheddouch/shakespear-tokenizer: direct link, hf CLI and curl.
- Browser
- Download file 549 Bytes
-
https://huggingface.co/otmanheddouch/shakespear-tokenizer/resolve/main/handler.py
- Command line
-
hf download hf://otmanheddouch/shakespear-tokenizer/handler.py
-
curl -L -o handler.py https://huggingface.co/otmanheddouch/shakespear-tokenizer/resolve/main/handler.py
549 Bytes
| from transformers import AutoTokenizer | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| self.tokenizer = AutoTokenizer.from_pretrained("otmanheddouch/shakespear-tokenizer") | |
| def __call__(self, data: str] -> Dict[str, Any]]: | |
| """ | |
| data args: | |
| data: text input | |
| Return: | |
| output (dict) : outputs with tokens and their ids | |
| """ | |
| tokens = self.tokenizer.tokenize() | |
| tokens_ids = self.tokenizer.encode(example) | |
| return {"tokens:"tokens, "ids":tokens_ids} |