Token Classification
Transformers
ONNX
Safetensors
PEFT
English
bert
ner
legal
legal-bert
nigerian-law
lora
Instructions to use WhiteRoomProdigy/amicus-ner-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WhiteRoomProdigy/amicus-ner-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="WhiteRoomProdigy/amicus-ner-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("WhiteRoomProdigy/amicus-ner-v2") model = AutoModelForTokenClassification.from_pretrained("WhiteRoomProdigy/amicus-ner-v2") - PEFT
How to use WhiteRoomProdigy/amicus-ner-v2 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
amicus-ner-v2: LoRA fine-tuned on Nigerian legal corpus (regex silver labels + Gemini augmentation)
6eba0e5 verified | { | |
| "backend": "tokenizers", | |
| "clean_up_tokenization_spaces": true, | |
| "cls_token": "[CLS]", | |
| "do_basic_tokenize": true, | |
| "do_lower_case": true, | |
| "is_local": false, | |
| "local_files_only": false, | |
| "mask_token": "[MASK]", | |
| "max_length": 256, | |
| "model_max_length": 512, | |
| "never_split": null, | |
| "pad_token": "[PAD]", | |
| "sep_token": "[SEP]", | |
| "stride": 0, | |
| "strip_accents": null, | |
| "tokenize_chinese_chars": true, | |
| "tokenizer_class": "BertTokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": "[UNK]" | |
| } | |