Instructions to use leonweber/foo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leonweber/foo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="leonweber/foo")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("leonweber/foo") model = AutoModelForTokenClassification.from_pretrained("leonweber/foo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from leonweber/foo: direct link, hf CLI and curl.
- Browser
- Download file 357 Bytes
-
https://huggingface.co/leonweber/foo/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://leonweber/foo/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/leonweber/foo/resolve/main/tokenizer_config.json
357 Bytes
| {"do_lower_case": true, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "special_tokens_map_file": null, "name_or_path": "michiyasunaga/BioLinkBERT-base", "do_basic_tokenize": true, "never_split": null, "tokenizer_class": "BertTokenizer"} |