Instructions to use fav-kky/FERNET-CC_sk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fav-kky/FERNET-CC_sk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fav-kky/FERNET-CC_sk")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fav-kky/FERNET-CC_sk") model = AutoModelForMaskedLM.from_pretrained("fav-kky/FERNET-CC_sk", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from fav-kky/FERNET-CC_sk: direct link, hf CLI and curl.
- Browser
- Download file 239 Bytes
-
https://huggingface.co/fav-kky/FERNET-CC_sk/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://fav-kky/FERNET-CC_sk/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/fav-kky/FERNET-CC_sk/resolve/main/tokenizer_config.json
239 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} |