Instructions to use NTA1802/NER-Completing-With-Legal-Dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NTA1802/NER-Completing-With-Legal-Dataset with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="NTA1802/NER-Completing-With-Legal-Dataset")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("NTA1802/NER-Completing-With-Legal-Dataset") model = AutoModelForTokenClassification.from_pretrained("NTA1802/NER-Completing-With-Legal-Dataset") - Notebooks
- Google Colab
- Kaggle
File size: 620 Bytes
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"backend": "tokenizers",
"cls_token": "[CLS]",
"do_basic_tokenize": true,
"do_lower_case": false,
"is_local": true,
"mask_token": "[MASK]",
"max_length": 256,
"model_max_length": 1000000000000000019884624838656,
"never_split": null,
"pad_to_multiple_of": null,
"pad_token": "[PAD]",
"pad_token_type_id": 0,
"padding_side": "right",
"sep_token": "[SEP]",
"stride": 0,
"strip_accents": null,
"tokenize_chinese_chars": true,
"tokenizer_class": "TokenizersBackend",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]"
}
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