Instructions to use crabz/FERNET-CC_sk-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crabz/FERNET-CC_sk-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="crabz/FERNET-CC_sk-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("crabz/FERNET-CC_sk-ner") model = AutoModelForTokenClassification.from_pretrained("crabz/FERNET-CC_sk-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {"do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "do_basic_tokenize": true, "never_split": null, "special_tokens_map_file": "cache/ee3ccb4bc8ed4c3c3cb32c5f954c6703aec7926e320ececc0fc9dcec6dd538ba.dd8bd9bfd3664b530ea4e645105f557769387b3da9f79bdb55ed556bdd80611d", "name_or_path": "fav-kky/FERNET-CC_sk", "tokenizer_class": "BertTokenizer"} |