Instructions to use fagner/envoy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fagner/envoy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fagner/envoy")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("fagner/envoy") model = AutoModelForTokenClassification.from_pretrained("fagner/envoy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from fagner/envoy: direct link, hf CLI and curl.
- Browser
- Download file 360 Bytes
-
https://huggingface.co/fagner/envoy/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://fagner/envoy/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/fagner/envoy/resolve/main/tokenizer_config.json
360 Bytes
| {"do_lower_case": false, "do_basic_tokenize": true, "never_split": null, "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": "dmis-lab/biobert-base-cased-v1.1", "tokenizer_class": "BertTokenizer"} |