Instructions to use kleinay/nominalization-candidate-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kleinay/nominalization-candidate-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="kleinay/nominalization-candidate-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("kleinay/nominalization-candidate-classifier") model = AutoModelForTokenClassification.from_pretrained("kleinay/nominalization-candidate-classifier") - Notebooks
- Google Colab
- Kaggle
File size: 320 Bytes
b50e149 | 1 | {"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, "model_max_length": 512, "special_tokens_map_file": null, "name_or_path": "bert-base-cased", "tokenizer_class": "BertTokenizer"} |